dataesr/scientific-paragraphs-categorization huggingface dataset:
In future, the formulation for the firebrand landing distance would be modified to include information about the wind speed, fire intensity, radius of the firebrands and fuel characteristics. Though without a validation test case, these simulations do provide an insight into the flexibility of this formulation to incorporate effects of random processes such as the turbulent heat diffusion and fire-spotting, specially while characterising the landing distance of firebrands. It can be deduced that the mathematical formulation is independent of the choice of the method used to ascertain the drifting part and can serve as a versatile addition to the existing fire spread simulators to include the effects of random spread. The results from this study provide a support to the implementation of the proposed approach for the effects of random phenomena into operational softwares such as WRF-SFIRE and ForeFire.
Acknowledgement This research is supported by  MINECO  under Grant  MTM2013-40824-P , by  Bizkaia Talent  and  European Commission  through  COFUND programme under Grant   AYD-000-226 , and also by the  Basque Government  through the  BERC 2014-2017 program  and by the  Spanish Ministry of Economy and Competitiveness MINECO: BCAM Severo Ochoa accreditation   SEV-2013-0323. The authors would also like to thank the two anonymous reviewers for their insightful comments which helped in improving the manuscript both in presentation and scientific content.
A software tool called Polarimetric Lidar Simulator (PLS) has been developed, based on the theoretical framework presented by  Freudenthaler (2016a) , which performs a complete search of the error space. The PLS is applied to several lidar systems in order to show the dependence of the systematic error on their design features. Random errors due to signal noise are neglected in this work. Their contribution to the uncertainty can be derived, in a similar way to  Pappalardo et al. (2004)  and  Guerrero-Rascado et al. (2008) , by simulating the com- plete error space of δ due to the uncertainties of the lidar parameters. In Sect. 2 we describe the functional blocks. In Sect. 3, the PLS performance is explained in detail. In Sect. 4 a synthetic lidar set-up is used to identify the most important error sources.
Acknowledgements. This work was supported by the Andalusia regional government through projects  P12-RNM-2409  and  P10-RNM-6299  and by the  Spanish Ministry of Science and Technology  through projects  CGL2010-18782  and  CGL2013-45410-R. The financial support for EARLINET in the ACTRIS Research Infrastructure Project by the  European Union 's  Horizon 2020 research and innovation programme  under grant agreement no. 654169  and previously under grant agreement no. 262254  in the  Seventh Framework Programme  ( FP7/2007-2013 ) is gratefully acknowledged. This work was also supported by the  University of Granada  through the contract " Plan Propio. Programa 9. Convocatoria 2013 " and the grant  AP2009-0559. Edited by: A. Ansmann Reviewed by:  F. Cairo  and one anonymous referee.
Proteins studied We applied the COMMA method to three archetypal proteins: (i) the B domain of staphylococcal protein A (PDB id: 1BDD, residues 1-60, NMR), a highly stable protein, (ii) the DNA-binding domain of the human tumour suppressor protein p53 (PDB id: 2XWR, chain A, residues 89-293, 1. 68Å resolution), a highly flexible protein, (iii) the cytoplasmic region of the receptor tyrosine kinase KIT (PDB id: 1T45, residues 547-935, 1. 90Å resolution), an allosterically regulated protein.
Set up of the systems The 3D coordinates for the studied proteins were retrieved from the Protein Data Bank (PDB)  [34]. All crystallographic water molecules and other non-protein molecules were removed. The structure of the DNAbinding domain of P53 contains a bound zinc ion. At physiological temperature, Zn 2+ rapidly dissociates from the protein and the resulting Zn 2+ -free P53 is folded and stable  [35, 36]. Consequently, we removed the zinc ion from the initial PDB structure and simulated P53 in the apo form. The mutated form of KIT was generated by in silico substitution of the aspartate (D) in position 816 into a valine (V) using MODELLER 9v7  [37]. All models were prepared using the LEAP module of AMBER 12  [38] , with the ff12SB forcefield parameter set: (i) hydrogen atoms were added, (ii) Na + or Cl -counter-ions were added to neutralise the systems charge, (iii) the solute was hydrated with a cuboid box of explicit TIP3P water molecules with a buffering distance up to 10Å.
Production of the trajectories For every protein, 2 replicates of 50 ns, with different initial velocities, were performed in the NPT ensemble using the PMEMD module of AMBER 12. The temperature was kept at 310 K and pressure at 1 bar using the Langevin piston coupling algorithm. The SHAKE algorithm was used to freeze bonds involving hydrogen atoms, allowing for an integration time step of 2. 0 fs. The Particle Mesh Ewald method (PME)  [41]  was employed to treat longrange electrostatics. The coordinates of the system were written every ps. Standard analyses of the MD trajectories were performed with the ptraj module of AMBER 12.
4b-d ). Notice that the length of the pathways does not depend on the length of the β-strands, i. e. longer β-strands do Table  2  between different methods to analyse the dynamical behaviour of proteins and their inter-residue communication COMMA Bio3D  [11]  GSATools  [16]  PyInteraph  [10]  PSN-ENM  [20]  Taylor et al. [17]  Software availability The technical characteristics and functionalities of COMMA and of five state-of-the-art methods are reported. The PSN-ENM method  [20]  and the method proposed by Taylor et al. [17]  are not implemented as software not exhibit longer paths. These observations on BdpA and p53 DBD support the utility of COMMA to compare proteins of very different natures in a straightforward way.
Related tools As noted in the introduction, a number of previously developed methods are dedicated to the analysis of the dynamical behaviour of proteins and their inter-residue communication  [10, 11, 16, 17, 20]. These tools however typically consider only dynamical correlations or/and non-covalent interactions, whereas COMMA combines four different dynamical properties in a unified framework (Table  2 ). Moreover COMMA describes communication at different levels, from individual residues to the whole dynamical architecture of the protein. In particular, the identification of communicating pairs of secondary structure elements is a unique feature of our method (Table  2 ). Finally, COMMA, which uses MDTraj Python package  [70] , does not depend on a particular MD package and can handle most popular formats used in the protein structural dynamics community.
Acknowledgements This work was partially undertaken in the framework of the LabEx CALSIMLAB, supported by the public grant  ANR-11-LABX-0037-01  constituting a part of the  "Investissements d'Avenir" program  (reference :  ANR-11-IDEX-0004-02 ; YK). It was also partially undertaken under the  MAPPING  project ( ANR-11-BINF-0003 ,  Excellence Programme "Investissement d'Avenir" in Bioinformatics ). We acknowledge the access to the  HPC resources of the Institute for Scientific Computing and Simulation at UPMC (Equip@ Meso  project  -ANR-10-EQPX-29-01 ,  Excellence Program  " Investissement d'Avenir "); funds from the  Institut Universitaire de France.
3 to maximize MUSE throughput and minimize redshift-dimming of the surface brightness (SB). We have removed quasars in proximity of bright stars and fields with high galactic extinction. Among the remaining quasars, we have then selected objects with available UVES spectroscopy to maximize "transverse" science projects within the MUSE GTO program. The second subsample of 5 radio-quiet and 2 radio-loud quasars is part of a MUSE GTO program to study the connection between absorption line systems in quasar spectra and emission-line galaxies (094. A-0131, 095. A-0200, 096. A-0222). The selection criteria of this program required quasars at higher redshift, i. e. z ≈ 3. 6 -4. 0 in order to maximize the available path-length of the Ly α forest within the MUSE wavelength range. Moreover, these quasars have very deep UVES spectroscopy.
Data Reduction We used the standard MUSE pipeline v1. 0  (Weilbacher et al. 2012 , 2014, Weilbacher in prep. )  for the basic steps of the data reduction with the default (recommended) parameters. For each of the individual exposures we performed bias subtraction, flat-fielding, twilight and illumination correction, and wavelength calibration. Sky subtraction was not performed with the pipeline but was done at a later stage with custom developed software as discussed below. The response curve and telluric correction were obtained from one of the spectrophotometric standards observed during the same night except for field #17 where we had to use a standard star from the previous night. Finally, flux calibrated data was drizzled onto a 3D grid using the information from geometry and astrometry tables in order to produce the final datacubes.
It is known that MUSE cubes reduced with the standard pipeline might have small astrometric offsets in the coordinate system because of a small "derotator wobble"  (Bacon et al. 2015). To correct for this effect, we registered the datacubes using the position of point sources in MUSE whitelight images (obtained by collapsing the datacubes along the wavelength direction) in different exposures. The final steps of the data reduction were performed with custom tools for flat-fielding correction and sky-subtraction that are part of the CubExtractor package  (Cantalupo, in prep. )  and that have been specifically developed to improve data quality for the detection of faint and diffuse emission in MUSE datacubes. In particular, the flat-fielding correction is performed as a self-calibration on each individual datacube using the sky-continuum and the sky-lines as a spatially uniform source to re-calibrate each individual slice (part of an Integral Field Unit, IFU) and IFU as a function of wavelength.
3D detection and extraction The final step in our data analysis procedure is the detection and extraction of extended line emission from the reduced and processed cubes. For this task we use newly developed threedimensional automatic extraction software called CubExtractor  (Cantalupo, in prep. )  based on a three-dimensional extension of the connected-labelling-component algorithm with union finding of classical binary image analysis (see, e. g. Shapiro & Stockman 2001). In particular, after datacubes and associated variances are being filtered (smoothed), voxels above a given user-defined signal-to-noise ratio (SNR) threshold are connected and objects are detected if they contain a certain number of connected voxels above a user-defined threshold. Three-dimensional segmentation masks produced by CubExtractor are then used for photometry and in order to obtain several lower-dimensional projections of the extracted objects, such as optimally-extracted images, two-dimensional spectra, and velocity maps, as we will show in the next sections.
1 Z sol ) clumps. Given these inferred clump sizes, current simulations of cosmological structure formation would unfortunately lack the spatial resolution (and, possibly, additional physics) to properly model this gas phase (see, e. g. Cantalupo et al. 2014  for discussion 7  ). In addition, our results imply that these gas clumps must be ubiquitous within at least 50 pkpc from every bright quasar at 3 < z < 4, independent of the quasar emission opening angle. This distance increases to at least 200 pkpc if we assume that bright quasars have anisotropic emission and that variation in emission geometry and opening angles are the origin for the different sizes of the MUSE detected nebulae. This first exploratory MUSE survey for quasar fluorescent emission demonstrates that even relatively short integration times (1h) with MUSE coupled with advanced data reduction and analysis tools are able to provide a three-dimensional view of the intergalactic gas in emission around any bright quasar at high redshift.
Discovery of novel diversity in high-throughput sequencing studies is an important aspect in environmental microbial ecology. To evaluate the effects that amplicon clustering methods have on the discovery of novel diversity, we clustered an environmental marine high-throughput sequencing dataset of protist amplicons together with reference sequences from the taxonomically curated Protist Ribosomal Reference (PR 2 ) database using three de novo approaches: sequence similarity networks, USEARCH, and Swarm. The potentially novel diversity uncovered by each clustering approach differed drastically in the number of operational taxonomic units (OTUs) and in the number of environmental amplicons in these novel diversity OTUs. Global pairwise alignment comparisons revealed that numerous amplicons classified as potentially novel by USEARCH and Swarm were more than 97% similar to references of PR 2.
INTRODUCTION High-throughput sequencing technologies have fundamentally changed our perceptions and concepts of environmental protist diversity  (Amaral-Zettler et al. , 2009; De Vargas et al. , 2015; Logares et al. , 2014; Massana et al. , 2015; Stoeck et al. , 2009). Current highthroughput sequencing surveys analyze protist communities by targeting specific molecular markers, resulting in datasets of many millions of sequencing reads that can be used to address community-comparative, ecosystem-functioning, and novel-diversity questions  (Dunthorn et al. , 2014b). The detection of novel diversity, in specific, is often based on sequence similarity. Potentially novel reads are identified by having a low similarity to previously sequenced reference taxa (e. g. ,  Berney et al. , 2013; Dunthorn et al.
, 2014b; Edgcomb et al. , 2011b; Filker et al. , 2014; Gimmler & Stoeck, 2015; Hartikainen et al. , 2014). Following this strategy, groups of sequences that contain both environmental reads and references represent environmental diversity which is covered by taxonomic reference databases, whereas groups of sequences that exclusively contain environmental reads represent novel variants of diversity. Our understanding of protist diversity is far from complete  (Pawlowski et al. , 2012). While the detection and description of novel protists is a central task, our ability to detect novel diversity in molecular environmental studies is affected by the way reads are clustered into operational taxonomic units  (OTUs). A traditional method of constructing de novo OTUs is by using the popular program USEARCH  (Edgar, 2010) , though several other similar alternatives exist (e.
(2015). To target novel diversity, we used shortest path analyses as a straightforward way to measure the distance (expressed as the number of edges that have to be crossed) between two nodes in a network  (Alvarez-Ponce et al. , 2013; Forster et al. , 2015) , specifically between environmental amplicons and reference sequences. A third method to define de novo OTUs is by using the program Swarm  (Mahé et al. , 2015; Mahé et al. , 2014). Unlike USEARCH, Swarm relies on an iterative, single-linkage algorithm that uses a small local clustering threshold d. This value d is user-defined (1 by default), and corresponds to the maximum number of differences due to substitutions or insertions/deletions between two globally aligned amplicons. Swarm selects the most abundant amplicon available in the amplicon pool to serve as a centroid for a new OTU.
Also like sequence similarity networks, the internal connections between the amplicons in Swarm's OTUs create a network of edges and nodes, which can be evaluated using methods based on graph theory. To compare how USEARCH, sequence similarity networks, and Swarm affect our ability to uncover novel diversity in protists, we used amplicon data derived from samples taken in European coastal marine environments. To place the environmental amplicons into a taxonomic context of already known diversity, we relied on the curated Protist Ribosomal Reference database (PR 2 )  (Guillou et al. , 2012). With this combination of environmental and taxonomically-identified amplicons, we asked: (i) Do all three clustering approaches predict the same amount of novel diversity? (ii) Do network analyses uncover additional novel diversity within OTUs that have underlying network topologies?.
Datasets We used already published environmental high-throughput sequencing data from the BioMarKs Consortium (www. biomarks. eu) that sampled microbial eukaryote communities at six near-shore marine sites in Norway, France, Spain, Italy and Bulgaria (e. g. ,  Bittner et al. , 2013; Dunthorn et al. , 2014a; Logares et al. , 2014; Massana et al. , 2015). The sample design and sample processing, as well as Roche/454 GS FLX Titanium sequencing of the V4 region of 18S rDNA, is detailed in  Massana et al. (2015). Quality filtering and chimera check of the raw reads with both UCHIME  (Edgar, 2010)  and ChimeraSlayer  (Haas et al. , 2011)  is also outlined in  Massana et al. (2015). The 1,476,249 cleaned V4 DNA and RNA reads were dereplicated into 312,503 strictly identical amplicons (dataset provided as Supplemental Information 1).
The scripts used to perform the analyses presented in this study can be found online in HTML format (File S1). For reference amplicons, we used the PR 2 v203 taxonomic reference database  (Guillou et al. , 2012). From this database we extracted 115,043 taxonomically identified V4 amplicons. We then combined these reference amplicons with the environmental amplicons for all downstream analyses. The clustering approaches would thus produce OTUs containing a blend of environmental and reference amplicons, or OTUs containing only one type of amplicons. Our goal was to identify differences between the three approaches, focusing on OTUs containing exclusively environmental amplicons, more likely to represent novel diversity.
Clustering Three de novo clustering approaches were used to cluster the combined amplicons. First, USEARCH v8. 0. 1623  (Edgar, 2010) , with a 97% global similarity value using options -cluster smallmem and -sortedby size. This analysis took 57 s on a Linux 2. 6 operated machine with dual Intel Xeon E5-2670 processors (2. 6 GHz) using 16 physical cores and 64 GB RAM. Second, basic network topology information was gathered by running a global pairwise alignment analysis in VSEARCH v1. 1. 3 (https://github. com/torognes/vsearch) using options -allpairs_global and -iddef 1. This analysis took about 5 days on the same computer. The resulting matrix contained 682,621,198 edges with a weight of at least 97% global sequence similarity. Based on this matrix we created sequence similarity networks in R version 3.
2. 1 (http://r-project. org) using 'igraph' scripts  (Csardi & Nepusz, 2006). To allow these network analyses in 'igraph,' we had to switch to a Linux 2. 6 operated machine with dual Intel Xeon E5-4650 processors (2. 7 GHz) using 32 physical cores and 256 GB RAM. Usage of less memory or more input data forced an untimely abort of analyses. The sequence similarity networks analyses took 3 h with this setting. Third, SWARM v2. 1. 1  (Mahé et al. , 2015; Mahé et al. , 2014) , with -d = 1 and -f. This analysis took 33 s on the first computer. Singleton and doubleton OTUs (OTUs consisting of one or two amplicons, respectively) were removed from the results of all three clustering approaches for downstream analyses.
e. , connections) that have to be crossed within an OTU to move from each environmental node (i. e. , amplicon) to its closest reference node. If an environmental node and a reference node were directly linked (i. e. , direct neighbors separated by exactly one edge), they exhibited a distance of '1' to each other. As the edges reflected global sequence similarity values of at least 97% (in sequences similarity networks), or a local basepair difference of '1' (in Swarm), we defined these environmental nodes as the part of diversity that is well represented by the PR 2 reference database. Environmental nodes that were not directly linked to reference nodes exhibited a distance of two edges or more, and were thus indirectly linked. Environmental nodes in OTUs, which exclusively consisted of environmental amplicons, exhibited 'infinite' distances to all reference nodes since no shortest path existed.
, 2011). This higher reliability and stability of all-vs. -all pairwise comparisons comes at the cost of extensive computational time  (Flynn et al. , 2015; Sun et al. , 2011) , which increases with the square to the number of input sequences  (Bik et al. , 2012). By calculating a pairwise comparison matrix of the currently largest dataset of near-shore marine protists in Europe, we operated close to the limit of dataset size that can be handled in all-vs. -all current approaches. Compared to both approaches relying on global clustering values, Swarm, with its local clustering value, produced the most OTUs in total (n = 13,240). The Swarm approach also produced the most OTUs (n = 6,228) that contained exclusively environmental amplicons (n = 81,073). These higher numbers of OTUs in total and OTUs containing exclusively environmental amplicons may be due to Swarm's high clustering stringency that iteratively links amplicons with a small number of differences to each other.
On the other hand, these high numbers may be due to missing intraspecific sequence variation in the PR 2 reference database, which usually contains only one reference per species. In natural communities, intraspecific genetic variation of microbial organisms may be much more diverse than just a few base pair differences, especially in hypervariable gene regions  (Brown et al. , 2015; Decelle et al. , 2014; Dunthorn et al. , 2012; Pernice et al. , 2013). But in Swarm, an environmental amplicon that differs by more than one base pair to a reference sequence will be placed into a novel OTU, if there are no intermediate amplicons linking them. As long as reference databases are not covering intraspecific sequence variation, it is a more effective strategy to compute Swarm OTUs from datasets consisting entirely of environmental amplicons, and perform a later taxonomical assignment; e.
Most environmental amplicons which shared less than 97% sequence similarity with references in PR 2 were congruently classified as novel by all three approaches. But both USEARCH and Swarm classified as novel numerous amplicons that were more than 97% similar to PR 2 references (Fig. 2 , Fig. S1 ). Even though clustering in USEARCH was performed at 97% similarity to delineate novel environmental amplicons from amplicons representing previously described diversity, we found 15,438 amplicons in exclusively environmental OTUs with more than 97% similarity to PR 2 references; for Swarm this fraction amounted to 47,007 amplicons. The even larger estimation of novel diversity by Swarm is caused by a combination of the approach's high clustering stringency and missing intraspecific variation in the PR 2 database.
The novel diversity uncovered by sequence similarity networks therefore comes closest to a subset of amplicons detected by all three clustering approaches that is truly less than 97% similar to references in PR 2. Furthermore, we strongly advise to perform an additionally taxonomic assignment step in Swarm and USEARCH to validate if potential novel diversity is indeed highly diverse from deposited references. At the same time, though, we are aware that even amplicons which are highly similar to entries in reference databases may represent novel genetic variants. Such hidden diversity is unlikely to be unveiled by approaches solely relying on global similarity values. Instead, more stringent approaches that trace local substitutions or methods which explore internal OTU structure stand a higher chance of revealing novel genetic variants, since they provide a higher resolution of genetic diversity.