dataesr/scientific-paragraphs-categorization huggingface dataset:
7. We quantified the degree of isolation experienced at each sampling site by averaging the residuals of the IBD data points that include that site. This approach is similar to the decomposed pairwise regression analysis to detect outlier populations described by  Koizumi, Yamamoto, and Maekawa (2006)  and essentially provides an index of genetic differentiation corrected for geographic distance. Sites with the largest average residual values were presumed to be more isolated than those with smaller average residuals. Due to strong correlation between G ST and G″ ST for both species (spotted salamander r =. 993, wood frog r =. 979), we quantified isolation using only the G ST -based IBD relationships. Genetic diversity relationships were assessed using H E and AR. We conducted three statistical analyses to test hypotheses concerning the relationship between these three factors: multiple regression between each measure of genetic diversity and all retained urbanization variables, multiple regression between urbanization variables and degree of site isolation, and simple linear regression between each measure of genetic diversity and degree of isolation.
| Landscape resistance modeling We tested support for a series of resistance surface models to determine the relative influence of ten landscape features on the genetic structuring of each species. The modeling was a two-step process. First, we optimized resistance values for each feature, and then, we conducted generalized additive modeling to determine which features were most influential for each species. Features to be analyzed were generally selected based on data availability and previous resistance modeling for these species  (Richardson, 2012). Land cover was based on the 2011 National Land Cover Database (NLCD;  Homer et al. , 2015)  and merged into three class that generally describe forests (land cover A), open areas and agriculture (land cover B), and developed areas and open waterbodies (land cover C; Table  2 ;  Richardson, 2012).
Road data were derived from the State of Maine NG911 Roads dataset and sorted into three classes describing limited-access interstate freeways, secondary roads (e. g. , state highways), and light roads. We subset river data from the National Hydrography Dataset (USGS, https ://nhd. usgs. gov/, access Feb. 18, 2016) into two classes based on the Strahler numbering system. Medium rivers included order 4 and 5 streams; large rivers included order 6 and 7 streams; and lower order waterways were not considered. Railway data were based on the Maine Department of Transportation's RailRouteSys dataset  (Johnson et al. , 2011). We calculated a terrain ruggedness index (TRI; Riley, DeGloria, & Elliot, 1999) using the "tri" function in the R package spatialEco  (Evans, 2017)  to characterize topographic heterogeneity.
Raster processing was performed using ArcGIS v10. 2. Processing included buffering all linear features to ensure their continuity following conversion to a raster and the resampling of all rasters to a 90-m resolution, which was necessary given computation constraints owing to the extent of the landscape being processed. Pairwise effective resistance between each sampling site was measured based on a circuit theory approach in GFlow  (Leonard et al. , 2017). We conducted partial Mantel tests with 10,000 permutations using the vegan R package to evaluate correlations between pairwise effective resistance values and genetic differentiation (G ST ) while controlling for effects of geographic distance between sites. The candidate resistance cost values that explained the most variation (largest R 2 value) were selected as optimal.
Wood frog OLS regression: y = 1. 028 × 10 -2 + -1. 831 × 10 -5 x + 4. 581 × 10 -7 x 2 , R 2 = 0. 445, Mantel's r = 0. 628, p <. 001. Spotted salamander: y = 9. 416 × 10 -3 + 2. 535 × 10 -5 x, R 2 = 0. 038, Mantel's r =. 4 ). The degree of isolation (mean IBD residual) experienced by a site was significantly greater for locations with more nearby light roads for both species, with this effect being stronger for wood frogs (β = 5. 99 × 10 -7 , p <. 001) than spotted salamanders (β = 5. 67 × 10 -7 , p =. 003; Table  4 ). For the wood frog dataset involving nearby light road length, a single outlier site was removed due to having over twice the distance of nearby light roads than the next closest site. The influence of light roads on site isolation was also analyzed using a simple linear regression (Figure  6 ), which further enforced the positive relationship.
Inferences from resistance surface modeling are sensitive to the spatial scale (i. e. , grain size and study extent) of analyses. Previous work has demonstrated a relationship between the grain size and estimated resistance values of environmental variables  (Zeller et al. , 2014) ; however, very high sensitivity was uncommon and even when observed, it remains difficult to determine which grain sizes are most ecologically meaningful. Our grain size was 90 m, which is reasonable given the home range of these species. Moreover, the grain size was determined by the resolution of our most coarse raster dataset and represented a trade-off with the large study extent to maintain computational tractability. The effects of study extent on resistance estimates are less well resolved  (Zeller, McGarigal, & Whiteley, 2012).
, 2010; Koen, Bowman, Garroway, & Wilson, 2013). Without concentrating sampling on the periphery of a study region, IBD data are inherently scant near study margins due to fewer possible pairwise population combinations at the largest distance classes, which causes anomalously high variance at the upper end of the distance distribution and potentially obscures ecologically relevant patterns. We identified strong relationships between the spatial extent of analyses and the strength of IBD relationships using our IBD scaling profiles. By quantifying the IBD slope across the range of distance values in our dataset, we could assess the relative importance of gene flow to genetic structuring across scales for each species. Slopes were greatest for wood frogs up to about 6 km and to about 9 km for spotted salamanders, suggesting that opportunities for instances of substantive pairwise isolation and divergence quickly increase with distance as the strongest locally constraining effects of gene flow become less universal to all population pairs.
TA B L E 2 Reclassification of National Land Cover Database fields into three categories for use in resistance surface modeling NLCD category NLCD descriptions Assigned category 41 Deciduous forest A 42 Evergreen forest A 43 Mixed forest A 90 Woody wetlands A 95 Emergent herbaceous A wetlands 21 Developed, open B space 52 Shrub/scrub B 71 Grassland/herbaceous B 81 Pasture/hay B 82 Cultivated crops B 11 Open water C 22 Developed, low C intensity 23 Developed, medium C intensity 24 Developed, high intensity.
In their original version, those principles suggested that clashes between bases are avoided by a combination of a concerted change in twist, roll, and slide, as the basepair propeller increases to improve stacking  (13). One year later, Dickerson formulated a simple numerical algorithm allowing for a quantification of Calladine's principles, coining the procedure as the 'Calladine's Rules'  (14, 15) , which explains qualitatively the local variation (at the basepair level) in twist, roll, propeller twist and the torsion angle ␦ for a few B-DNA sequences which were previously determined experimentally  (14). Unfortunately, the extension and predictive power of these rules, even in the most recent versions, is limited  (1, 16). Attempts to gain more quantitative information were based on the analysis of the variability in local helical parameters in structural databases  (17, 18) , but to date (data accessed on the 19 March 2019), isolated B-DNA structures in the Nucleic acid Data Bank (NDB) allowed us to obtain flexibility data for only 5 of the 136 distinct tetranucleotides (only AATT, CGCG, CGAA, GCGA and ATTC are represented more than 500 times from 10 134 tetranucleotide analyzed belonging to 727 PDB structures).
Even when the database is extended by including protein-DNA complexes (155 316 tetranucleotides belonging to 3434 PDB structures), the sampling is not dense enough to describe sequence-dependent DNA flexibility at the tetranucleotide level (24 out of the 136 tetranucleotides are still represented less than 500 times). In this context, atomistic molecular dynamics (MD) simulations are the only alternative to obtain robust and transferable parameters  (10, 19, 20). The first requirement for deriving physical descriptors of DNA from MD simulations is the availability of extended simulations for a library of sequence fragments containing all distinct tetranucleotides. This requires a significant computational effort which has encouraged joint projects such as the Ascona B-DNA Consortium (ABC, https://bisi.
System preparation and MD simulations All oligonucleotides were constructed with the leap program of AMBERTOOLS 15  (31)  and simulated using the pmemd. cuda code (32) from AMBER14  (31) , following the standard ABC protocol  (10). Canonical duplexes were generated using Arnott B-DNA fiber parameters  (33) , and solvated by a truncated octahedral box of SPC/E (34) water molecules with a minimum distance of 10 Å between DNA and the closest face of the box. Systems were neutralized with K + or Na + ions adding additional 150 mM of K + Cl - (or Na + Cl -). PARMBSC0  (30)  and PARMBSC1  (24)  force fields were used to describe DNA, while Dang's parameters were used for ions  (35). Systems were optimized and equilibrated as described elsewhere  (10) , and simulated for 1 s in the NPT ensemble, using Particle-Mesh Ewald corrections  (36)  and periodic boundary conditions.
Global analysis Trajectories were processed with the cpptraj (39) module of the AMBERTOOLS 15 package  (31) , and the NAFlex server  (40)  for standard analysis. DNA helical parameters and backbone torsion angles were measured and analysed with the CURVES+ and CANAL programs  (41) , following the standard ABC conventions  (10). Duplexes were named following the Watson strand. The letters R, Y and X stand for a purine, a pyrimidine, or any base respectively; base pairs flanking a dinucleotide step were denoted using two dots to represent the central step (e. g. R. Y), and one dot when trinucleotides are considered (e. g. R. Y), while X:X and XX represent a basepair and a basepair step, respectively. Bayesian Information Criterion (or BIC)  (42, 43)  was used to quantify the normal or binormal (i.
Statistics, graphics and molecular plots The statistical analysis, including the Bayesian Information Criterion (BIC), Bayes Factor analysis, Helguerro's theorem, Kullback-Lieber divergence, PL and correlations measurements, as well as associated graphics, were obtained with R 3. 0. 1 statistical package  (51) , the MatLab R2016b package, numpy  (52)  and matplotlib  (53). The molecular plots were generated using VMD 1. 9  (54).
CONCLUSIONS The analysis of numerous molecular dynamics trajectories obtained with an accurate, last generation, force field has allowed us to derive some general rules concerning the equilibrium conformation distribution of B-DNA, which represent a significant step beyond Calladine-Dickerson earlier propositions. Specifically, we are now able to predict when significantly anharmonic distributions will arise as a function of the tetranucleotide sequence context:   9 ), and conformations predicted by using the MD datasets produced herein. The six intra-basepair parameters were predicted (red lines) and compared with all the experimental structures (gray lines) and Calladine-Dickerson prediction for Roll and Twist (cyan line). Vertical dashed lines represent predicted binormal/bimodal steps, while vertical dotted lines represent steps with clear multi-peaked distributions although not bimodal according to Helguerro (see Materials and Methods).
In the last row, predicted BI% (yellow and green) were compared with 31 P-NMR gold-standard measurements and multi-microsecond long MD simulation of the same sequence using PARMBSC1 force field  (9). NMR1 stands for the work by Schwieters et al. (74) , and NMR2 from Tian et al. (68). 3D superposition showing the capability of the miniABC library and our set of rules to predict two conformational substates observed experimentally for DDD at the highly polymorphic GCGA tetranucleotide (NMR structures with PDB id: 171D, and 1GIP). The RMSD was measured after aligning the backbones between the experimental structures and the two substates captured by the miniABC library. • The first and second moments (average and covariance) of the equilibrium distributions of helical coordinates for DNA can only be understood in terms of nonlocal sequence-dependence contexts, to at least the trinucleotide level for intra-basepair coordinates, and the tetranucleotide level for inter-basepair coordinates.
(74) , and NMR2 from Tian et al. (68). 3D superposition showing the capability of the miniABC library and our set of rules to predict two conformational substates observed experimentally for DDD at the highly polymorphic GCGA tetranucleotide (NMR structures with PDB id: 171D, and 1GIP). The RMSD was measured after aligning the backbones between the experimental structures and the two substates captured by the miniABC library.
ACKNOWLEDGEMENTS P. D. D. is a  PEDECIBA  ( Programa de Desarrollo de las Ciencias Básicas)  and  SNI  ( Sistema Nacional de Investigadores, Agencia Nacional de Investigaci ón e Innovaci ón, Uruguay ) researcher. A. B. and J. W. are La Caixa PhD fellows (UB and  IRB Barcelona, Spain). M. O. is an  ICREA (Instituci ó Catalana de Recerca i Estudis Avanc  ¸ats) researcher. The authors thank the  Ascona B-DNA Consortium  for the trajectories in the standard set (ABC). Calculations were performed in the  Laboratory for Computation and Visualization of Mathematics and Mechanics at the EPFL, Lausanne , and at  IRB Barcelona.
FUNDING Spanish Ministry of Science  [ BFU2014-61670-EXP ,  BFU2014-52864-R] ;  Catalan SGR ;  Instituto Nacional de Bioinformática ;  European Research Council  ( ERC SimDNA ); European Union's  Horizon 2020 research and innovation program  [676556]; European Union's  Horizon 2020 research; BioExcel and MuG EU-projects ;  Biomolecular & Bioinformatics Resources Platform  [ ISCIII PT 13/0001/0030 ] co-funded by the  Fondo Europeo de Desarrollo Regional  (to M. O. );  MINECO Severo Ochoa Award of Excellence, Government of Spain  (awarded to IRB Barcelona);  CNRS  and the  ANR  project  CHROME  [ ANR-12-BSV5-0017-01  to R. L. ];  Swiss National Science Foundation  [ 200020 163324  to J. H. M. ]. Funding for open access charge: European Union's  Horizon 2020 research and innovation program  [676556].
NMR1 stands for the work by Schwieters et al. (74) , and NMR2 from Tian et al. (68). 3D superposition showing the capability of the miniABC library and our set of rules to predict two conformational substates observed experimentally for DDD at the highly polymorphic GCGA tetranucleotide (NMR structures with PDB id: 171D, and 1GIP). The RMSD was measured after aligning the backbones between the experimental structures and the two substates captured by the miniABC library.
In particular, inverse modeling based on source-receptor relationships from FLEXPART has become widely used. In this paper, we present FLEXPART version 10. 4, which works with meteorological input data from the European Centre for Medium-Range Weather Forecasts (ECMWF) Integrated Forecast System (IFS) and data from the United States National Cen-ters of Environmental Prediction (NCEP) Global Forecast System (GFS). Since the last publication of a detailed FLEX-PART description (version 6. 2), the model has been improved in different aspects such as performance, physicochemical parameterizations, input/output formats, and available preprocessing and post-processing software. The model code has also been parallelized using the Message Passing Interface (MPI). We demonstrate that the model scales well up to using 256 processors, with a parallel efficiency greater than 75 % for up to 64 processes on multiple nodes in runs with very large numbers of particles.
, 2005). Since then, while updates of FLEXPART's source code and a manual were made available from the web page at https://flexpart. eu/ (last access: 30 October 2019), no citable reference was provided. In this paper, we describe FLEX-PART developments since  Stohl et al. (2005) , which led to the current version 10. 4 (subsequently abbreviated as v10. 4). FLEXPART can be run either forward or backward in time. For forward simulations, particles are released from one or more sources and concentrations (or mixing ratios) are determined on a regular latitude-longitude-altitude grid. In backward mode, the location where particles are released represents a receptor (e. g. , a measurement site). Like in the forward mode, particles are sampled on a latitude-longitudealtitude grid, which in this case corresponds to potential sources.
FLEXPART and its history FLEXPART's first version (v1) was a further development of the trajectory model FLEXTRA  (Stohl et al. , 1995)  and was coded in Fortran 77. It provided gridded output of concentrations of chemical species and radionuclides. Its meteorological input data were based on the ECMWF-specific GRIB-1 (gridded binary) format. The model was first applied in an extensive validation study using measurements from three large-scale tracer experiments  (Stohl et al. , 1998). A deposition module was added in version 2. Version 3 saw improvements in performance and the addition of a subgridscale terrain effects parameterization. In v3. 1 the output format was optimized (sparse matrix) and mixing ratio output could optionally be produced. It also allowed for the output of particle positions.
2. 7). Fifth, large parts of the code were parallelized using the Message Passing Interface (MPI), thus facilitating a substantial speedup for certain applications (see Sect. 3), and the code was unified so that a single executable can now use both ECMWF and GFS input data. Sixth, a dust mobilization scheme that can be run as a FLEXPART preprocessor was developed (Sect. 2. 8). Seventh, the software used to retrieve data from the ECMWF has been modernized and can now also be used by scientists from non-ECMWF member states (Sect. 5. 2. 1). Finally, a testing environment was created that allows users to verify their FLEXPART installation and compare results (Sect. 7). Despite the many changes and additions, in large part the operation of FLEXPART v10. 4 still resembles the original version 1 design.
Throughout this paper, we avoid repeating information on aspects of the model that have not changed since earlier model descriptions. The paper should therefore always be considered together with the publications of  Stohl et al. (1998 Stohl et al. ( , 2005)). To provide the necessary context for the rest of this paper, we provide a brief overview of the FLEXPART v10. 4 directory structure in Table  1. The source code is contained in directory src. The pathnames of the input and output directories are stated in the file pathnames read by the FLEXPART executable. The directory options contains the parameters that define a run in files such as COMMAND (e. g. , start and end times of the simulation, output frequency, etc. ), RELEASES (definition of the particle releases), OUTGRID (output grid specifications) and others.
Backwards compatibility with the former temperatureindependent specification of the OH reaction (version 9 and before) can be achieved by setting the constant N in the SPECIES_nnn file to zero. The constants C and D can be derived from the former parameters as follows: C = κ r e D/T r ( 15 ) and D = A/R, ( 16 ) where A is the activation energy, R is the gas constant and κ r is the former OH reaction rate (referring to T r = 298 K), which were specified in the SPECIES_nnn file for earlier versions. OH fields other than those provided with the model code have been tested in FLEXPART. These fields may have higher spatial and temporal resolution (e. g. ,  Fang et al. , 2016) , which is important for chemical species with short lifetimes. Users are required to modify readOHfield. f90 and gethourlyOH.
Dust mobilization scheme Desert dust is a key natural aerosol with relevance for both climate and air quality. FLEXPART has been used earlier with preprocessors to initialize dust amounts from wind speed and surface properties following  Tegen and Fung (1994)   (Sodemann et al. , 2015). Now a dust mobilization routine has been included as a preprocessing tool in FLEX-PART v10. 4. The scheme, called FLEXDUST, was devel-  oped to simulate mineral dust transport with FLEXPART in forward or backward simulations  (Groot Zwaaftink et al. , 2016). This module runs independently from FLEXPART and produces gridded output of mineral dust emissions as well as input files (RELEASES) that can be used for FLEX-PART simulations of atmospheric transport. It can thus be considered a preprocessing (for forward simulations) or postprocessing tool (for backward simulations) for FLEXPART v10.
Examples of other Lagrangian particle models that have been parallelized are NAME  (Jones et al. , 2007)  and FLEXPART-WRF  (Brioude et al. , 2013) , which use a hybrid approach (OpenMP+MPI). For FLEXPART v10. 3 we decided to use a pure MPI approach for the following reasons. -It is simpler to program than a hybrid model and more flexible than a pure OpenMP model. -While OpenMP in principle may be more effective in a shared memory environment, MPI can often perform equally well or better provided there is not excessive communication between the processes. -MPI offers good scalability and potentially low overhead when running with many processes.
Implementation The FLEXPART code contains several computational loops over all the particles in the simulation, which is where most of the computational time is spent for simulations with many particles. The basic concept behind our parallel code closely resembles the "trivial parallelization" concept described above. When launched with a number of processes, N p , each process will separately calculate how many particles to release per location, attempting to achieve an approximately even distribution of particles among the processes while keeping the total number of particles the same as for a simulation with the serial version. Each running process will generate an independent series of random numbers and separately calculate trajectories and output data for its set of particles. Explicit communications between processes are only used when the output fields are combined at the master process (MPI rank 0) using MPI_Reduce operations, before writing the output.
Also, in the case in which the output of all individual particle properties is desired (option IPOUT1 = 1 or 2 in file COMMAND), we let each process append its data to the same file. We thus avoid the costly operation of transferring particle properties between processes. The performance of the implementation is discussed in Sect. 3. 2 (see Fig. 7 ). Some parts of the code are not simply loops over particles, most notably the routines for reading and transforming the input meteorological data. It follows that the performance gain of using parallel FLEXPART in general is better for simulations with a larger number of particles. We have, however, implemented a feature whereby instead of having each MPI process read and process the same input data, one dedicated MPI process is set aside for this purpose.