dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2009) , thus obtaining a set of 3036 ingroup sequences collected in 60 countries (Supplementary Table  S2 ). Moreover, to root the tree, we added 35 reference outgroup sequences extracted from the Los Alamos HIV database (non-C, subtype A to K). The phylogeny was constructed using PhyML with GTRþIþÀ4, SPR option and aLRT SH-like branch supports. All alignment sites were used to build this tree, including sites associated to drug resistances, as their deletion showed little impact  (Jung et al. , 2012;  see this article for details on data, alignment and phylogeny calculation). As with the previous study, we grouped countries in major areas to have enough strains for each group and to be able to extract synthetic and significant information. A total of 14 groups were used (see Supplementary Table  S2  for details and Supplementary Figs S4 and S5 for other groupings and corresponding results): Central Africa [644 strains, mostly from Zambia, plus a few strains collected in the Democratic Republic of Congo (DRC) near the Zambian border]; South Africa (including Swaziland, 731 strains); Southern Africa (other southern Africa countries, 328 strains); East Africa (188 strains, mostly from Burundi and Tanzania); Horn of Africa (118 strains, mostly from Ethiopia); West Africa (63 strains, mostly from Senegal); Asia (366, mostly from India); South America (245, mostly from Brazil); Central America (26, mostly from Cuba); North America (9, all from the USA); Northern (106), Western (67), Southern (92) and Eastern (53) Europe.
We have already indicated above certain differences between the two analyses. A general trend (with this data set and others) is that DELTRAN returns fewer ancestral ambiguities and fewer indirect origins than ACCTRAN (none versus five, Supplementary Fig. S3 ). As a consequence, DELTRAN covers more sequences than ACCTRAN (64% versus 58%) and finds one more phylotype (19 versus 18). Moreover, six small phylotypes (5150 sequences in total) are found by one method but not the other. However, both methods mostly agree. A number of phylotypes are identical (8) or nearly indentical (7 with similarity 480%) in both analyses, and the geographical history is the same for 88% of the covered sequences (see Supplementary Fig. S3  for more results and explanations on these measures). Overall, this comparison gives us high confidence in most of the results of the PhyloType analysis and puts some others into perspective (e.
CONCLUSION To summarize results regarding the HIV-1 subtype C pandemic: PhyloType recovers a number of already-identified transmission chains (e. g. East Africa to Brazil), contradicts a few others (e. g. origin of Indian epidemic) and suggests some new routes (e. g. two different geographical origins of the Ethiopian epidemic). Note that PhyloType reveals potential founder events, rather than gene flows as proposed by  Slatkin and Maddison (1989)  and used in numerous studies (e. g. Wallace et al. , 2007; Ve´ras et al. , 2011). PhyloType results are obtained rapidly, in a matter of minutes, through a user-friendly Web interface. Therefore, we believe that PhyloType software has the potential to be extremely useful for exploring and interpreting the large virus phylogenies, which are available today and should become the norm in the near future.
The application's speed should be a major asset in surveillance tasks. However, for many viruses (e. g. Hepatitis B), strain annotations (countries, dates. ) are not yet as easily obtained from the databases as they are with HIV-1, which should imply the need for some significant preliminary work before launching PhyloType analyses. Moreover, representative samples are still lacking for several viruses (e. g. Nonovirus), and sampling efforts will have to be made to use PhyloType (and any other similar tool) effectively. Several directions deserve further exploration, most notably the implementation of simple and computationally efficient probabilistic models, to be used instead of parsimony to infer ancestral annotations, and the automatic combination of annotation values, as performed by other methods (e.
ACKNOWLEDGEMENTS We thank  Heikki Lehvaslaiho  and  Anne-Muriel Arigon-Chiffoleau  for the discussions when we started working on this project,  Marco Salemi  for providing us with the Albania data set and three anonymous referees for their helpful comments. Funding:  HIVphyML  project (T. D. O. , F. C. , O. G. ;  NRF South Africa  and  CNRS France) ;  PhyloSpace  project (F. C. , M. J. , O. G. ; ANR 6e`me Extinction);  NUMEV Montpellier Labex  (M. J. , O. G. );  Wellcome Trust  (T. D. O. ; grant  082384/Z/07/Z ).
Floating-point formats In FloPoCo, a floating-point number x is composed of a sign bit S, an exponent field E on r bits, and a normalized significand fraction F on f bits. In addition, a two-bit exn code is used for exceptional cases such as zeroes, infinities and NaN (Not a Number ). Figure  2  depicts such a number, whose value is x = (-1) S × 1. F × 2 E-E0 with E 0 = 2 r-1 -1. 2 1 r f E F exn S Fig. 2. Format of a floating-point number x. This format is inspired by the IEEE 754 standard, but is not strictly compliant with it. This standard was designed for processor implementations and makes perfect sense there, but for FPGAs, many things can be reconsidered. Firstly, a designer should not restrict himself to the 32-bit and 64-bit formats of IEEE 754: he should aim at optimizing both exponent and significand size for the application at hand.
Comparison with previous work Table VII compares the FloPoCo results for single precision for a target frequency of 200 MHz with those from  [Echeverría and López-Vallejo 2008]  on a  Virtex-4 (4vfx100ff1152-12). The results from both units are very similar, only clearly differing in the use of BRAMs (due to the different algorithms used for the logarithm and the exponential functions). We point out that  [Echeverría and López-Vallejo 2008]  is less accurate, using (r log , f log ) = (8, 23) instead of the needed (8, 33), losing up to 10 bits of accuracy for some inputs. We are also comparing VHDL code hand-tuned for a specific FPGA and a specific precision  [Echeverría and López-Vallejo 2008]  to automatically generated code.
Observations We re-observed in H band several times six of the nine stars with nominal detections and one of the three stars with tentative detections from our original PIONIER survey  (Ertel et al. 2014). We focus here on this clean sample of stars with excesses detected using the same instrument and technique and in the same band (H band) as we employ for our follow-up observations. Only for these targets can we confidently expect a redetection and directly compare our detection statistics from our original PIONIER survey with our follow-up observations. The new data presented in this work were obtained in August 2013 and October 2014. In addition, we consider data of HD 172555 obtained in April 2014 in the context of a dedicated study. We compare our detection rate and excess levels from the new observations with our original survey  (Ertel et al.
2014 ) and previous observations of β Pic (HD 39060,  Defrère et al. 2012). The targets and the observing dates are listed in Table  1. For our observations, we followed closely the strategy motivated and outlined in  Ertel et al. (2014) , which we only briefly summarize here. All observations were carried out in H band using the PIONIER beam combiner on the VLTI in combination with the 1. 8 m Auxiliary Telescopes in the compact Notes. (1) Survey detection  (Ertel et al. 2014 ). (2) Data obtained on 2010-12-04 and on 2010-12-20 are combined to one measurement in order to improve the accuracy since no variability is seen between the two observations. f CSE is the flux ratio between the circumstellar emission and the star. ∆ CSE is the significance of the deviation of this value from the error weighted mean of all considered measurements of this target (Eq.
(2005). Observations were carried out in SMALL spectral resolution (three channels across the H band). The FOWLER read-out mode and the fast AC mode were used and the number of steps read in one scan (NREAD) was set to 1024 with a scan length of 60 µm. The observing conditions were in general well suited for our observations (seeing and coherence time <1. 5 and >2 ms, respectively, thin clouds at most). Only on one night, 9-Aug. -2013, were the conditions highly variable with occasionally very large seeing values (>2 ) and short coherence time (∼1 ms). A detailed discussion of the systematic effects produced by such observing conditions is presented in Appendix A. We discard all data taken during this night from our statistical analysis, because they have to be considered unreliable as discussed in the appendix.
Data reduction, calibration, and excess measurement We reduce our new data using the standard PIONIER pipeline version 3. 30  (Le Bouquin et al. 2011). As for our observing strategy, for the calibration and excess measurements we closely followed the procedure motivated and outlined by  Ertel et al. (2014). First, a global calibration of each night was performed to correct for the effect of field rotation on the instrumental visibilities (due to polarization effects in the VLTI optical train; Le  Bouquin et al. 2012; Ertel et al. 2014). Then, we selected pairs of a science observation and the preceding or following calibrator observation without using the same calibrator observation to calibrate two science observations. We also avoided using different observations of the same calibrator for the calibration of different observations of one science target.
Acknowledgements. This work has significantly benefited from the discussion at the hot dust workshop (JPL/CalTech, May 2015) organized by  B. Mennesson  and  R. Millan-Gabet. S. Ertel ,  J. -C. Augereau , and  A. Bonsor  thank the  French National Research Agency  ( ANR , contract  ANR-2010 BLAN-0505-01 ,  EXO-ZODI ) and  PNP-CNES  for financial support. J. Olofsson  acknowledges support from the  ALMA/Conicyt  Project  31130027. PIONIER is funded by the  Université Joseph Fourier (UJF) , the  Institut de Planétologie et d'Astrophysique de Grenoble (IPAG) , the  Agence Nationale pour la Recherche  ( ANR-06-BLAN-0421  and  ANR-10-BLAN-0505 ), and the  Institut National des Science de   A44 , page 5 of 6 A&A 595,  A44  ( 2016 ) l'Univers (INSU PNP and PNPS). The integrated optics beam combiner is the result of a collaboration between IPAG and CEA-LETI based on CNES R&T funding.
MODexplorer then Creates the multiple sequence alignment (MSA) of the query protein family using HHblits  (Remmert et al. , 2012). Generates alignments to proteins of known structure using HHSearch  (So¨ding, 2005)  and the PDB database filtered at 70% sequence identity. For every HHSearch hit, retrieves and aligns related PDB chains, i. e. chains with a sequence similarity 470%. Such 'redundant' chains can be useful. For example, they might have been solved in complex with different ligands, represent alternative conformational states or have a better structural quality. Displays alignments to the HHSearch hits and their related PDB chains both schematically with a BLAST-like bar diagram and as multiple sequence alignments including query and hit sequences, and their homologs. Graphically shows annotations on the alignments.
Multivariate Boltzmann sampling of protein-coding sequences under DF constraint Our approach builds on a multivariate Boltzmann sampling scheme, initially introduced in the context of enumerative combinatorics  (13)  and previously applied to control the GC-content of sampled RNA sequences within the RNAmutants software  (14). This approach initially relaxes the goal of preserving the DF and draws sequences that strictly preserve the amino acid sequence while only achieving, on the average, the prescribed DF. A further rejection of unsuitable sequences, whose DFs differ too much from the targeted DF, filters the generated sequences, reestablishing the uniformity within the selected subset. The produced sequences therefore feature both correct DF and coding capacity while being generated with uniform probability.
Secondary structure prediction The secondary structures of both the input mRNA sequence and random sequences are predicted using the RNAplfold software, distributed within the Vienna RNA package  (18). RNAplfold considers all possible locally stable secondary structures for an input RNA sequence and calculates base pairing probabilities, assuming a Boltzmann equilibrium. As recommended by Lange et al. (19) , we use a window size of W+50 nt (W ¼ 150, the span, is considered to be an optimal choice in the paper), and retain only those base pairs separated by at most W positions, and set a base pairing probability cut off threshold to 0. 1.
polytechnique. fr to initiate discussions, exchange programs or data between users. In addition, twoday training courses are organised twice a year. Each training course is free of charge for participants and offers a complete training to be able to install the code, launch a simulation and change surface emissions or other parameters in the code. The code is completely written in Fortran90, and running scripts are written in shell (using gnu-awk for input datafiles processing). The required software is a Fortran 95 compiler (g95 and gfortran are both free and efficient, but Makefiles with Intel's ifort compiler options are also provided). The required libraries are NetCDF (either 3. 6. x or 4. x. x), MPI (see below), and GRIB API (associated to the use of the ECMWF meteorological datasets).
The CHIMERE software In order to facilitate software distribution, CHIMERE is protected under the General Public License. This paper presents the latest model version release called CHIMERE 2013. The source code and the associated documentation is available on the website www. lmd. polytechnique. fr/chimere. The documentation is both technical and scientific. It includes a chapter dedicated to the set-up of a test case simulation that allows new users to easily carry out a CHIMERE simulation: model configuration and data (meteorology and emissions files) are provided to simulate the 2003 heatwave in western Europe. CHIMERE is a National Tool of the French Institut National des Sciences de l'Univers, meaning that support has to be provided to the users of model. Two mailing lists exist for this support: chimere@lmd.
polytechnique. fr to send questions to the model developers and chimereusers@lmd. polytechnique. fr to initiate discussions, exchange programs or data between users. In addition, twoday training courses are organised twice a year. Each training course is free of charge for participants and offers complete training to be able to install the code, launch a simulation and change surface emissions or other parameters in the code. The code is completely written in Fortran90, and running scripts are written in shell (using GNU awk for input datafiles processing). The required software is a Fortran 95 compiler (g95 and gfortran are both free and efficient, but Makefiles with Intel's ifort compiler options are also provided). The required libraries are NetCDF (either 3. 6. x or 4. x. x), MPI (see below), and GRIB API (associated to the use of the ECMWF meteorological datasets).
Land use types Land use types, or categories, are needed by CHIMERE to calculate a number of processes, such as deposition, biogenic emissions, or surface layer momentum and heat transfer. Land use files need to be constructed only once per model domain. There are currently 9 land use categories in CHIMERE. Those categories are calculated from available global land use databases, which can contain different numbers of classes. The source code version comes with land use data and interfaces for two databases: the Global Land Cover Facility (GLCF) and the GlobCover Land Cover (LC). GLCF is a 1 km × 1 km resolution database from the University of Maryland, following the methodology of  Hansen and Reed (2000). This global land cover classification is based on the imagery from the AVHRR satellites analysed to distinguish 14 land cover classes.
The GlobCover LC is a global land cover map at 10 arc second (300 m) resolution  (Bicheron et al. , 2011)  GLCF is a 1 km × 1 km resolution database from the University of Maryland, following the methodology of  Hansen and Reed (2000). This global land cover classification is based on the imagery from the AVHRR satellites analysed to distinguish 14 land cover classes. The GlobCover LC is a global land cover map at 10 arc second (300 meter) resolution  (Bicheron et al. (2011)  The nine CHIMERE land use types are described in Table  2. The correspondance table between the database landuse types and the CHIMERE landuse types is provided with the model. The user can choose either GLCF or Glob-Cover by simply selecting a flag; a dedicated sequence of scripts and programs prepares the landuse file in the CHIMERE format.
An additional class "inland water" has been added to the classifications in both land cover databases to distinguish between the sea water and fresh water. This feature was required to avoid model emissions of the sea salt over the fresh water surfaces. The distinction was performed using a land-sea mask. So instead of the original 14 GLCF and 22 GlobCover classes, the CHIMERE landuse pre-processor relies on 15 and 23 classes for GLCF and GlobCover, respectively. An example of CHIMERE regridded landuse is displayed in Figure  5.
430 Only the largest domain makes use of external boundary conditions ( Figure  6  ). Szopa et al. (2009)  also investigated the sensitivity of the model to the temporal increment of the boundary conditions. classes defined within the UN Land Cover Classification System (LCCS). GlobCover database is based on the ENVISAT satellite mission's MERIS sensor (Medium Resolution Image Spectrometer) level 1B data acquired in full resolution (FR) mode with a spatial resolution of 300 m. GlobCover LC was derived from an automatic and regionally-tuned classification of a time series of MERIS FR composites covering the period December 2004-June 2006. The global land cover NetCDF files are provided along with the CHIMERE distribution. The nine CHIMERE land use types are described in Table 2. The correspondence table between the database land use types and the CHIMERE land use types is provided with the model.
The user can choose either GLCF or GlobCover by simply selecting a flag; a dedicated sequence of scripts and programs prepares the land use file in the CHIMERE format. An additional class "inland water" has been added to the classifications in both land cover databases to distinguish between sea water and fresh water. This feature was required to avoid model emissions of sea salt over fresh water surfaces. The distinction was performed using a land-sea mask. So instead of the original 14 GLCF and 22 GlobCover classes, the CHIMERE land use preprocessor relies on 15 and 23 classes for GLCF and GlobCover, respectively. An example of CHIMERE regridded land use is displayed in Fig. 5.
, 2008) , MACC (Monitoring Atmospheric Composition and Climate) II, and AQMEII (Air Quality Model Evaluation International Initiative)  (Rao et al. , 2011)  projects, and an interface with OsloCTM2  (Sovde et al. , 2008)  was also developed for the CityZen project. For the specific case of mineral dust, the GOCART (Goddard Menut L. et al. : CHIMERE: a model for regional atmospheric composition modelling Fig. 7. Treatment of meteorological fields: two options are available (i) using a meteorological dataset restricted to the mean parameters (u,v,T,q,P, precipitation) and (ii) using a complete meteorological dataset that includes turbulent parameters. hand, if the meteorological model provides all the necessary meteorological variables, the meteorological interface only interpolates data on the CHIMERE grid, with an hourly time step.
Therefore an update of u * is proposed following the  Louis et al. (1982)  formulation which is particularly robust. We recommend to use this alternative formulation, no matter whether u * is available in the input fields, in order to have a deposition that is consistent with the high-resolution land 550 use. u * = C 2 DN F m |U | 2 (4) with |U | the mean wind speed at 10 m above ground level, F m is the  Louis et al. (1982)  stability function and C DN the momentum transfer coefficient as: 555 C DN = k ln z z 0m (5) with k=0. 41 the Karman constant, z the altitude where the wind speed |U | is known and z 0m , the momentum roughness length. The momentum stability function F m is estimated Fig. 7. Treatment of meteorological fields; two options are available: (i) using a meteorological dataset restricted to the mean parameters (u, v, T , q, P , precipitation) and (ii) using a complete meteorological dataset that includes turbulent parameters.
, 2012) , but the TNO (Dutch Organisation for Applied Scientific Research) inventory has also been used  (Kuenen et al. , 2011). In studies over North America, the model is also used with the US EPA (Environmental Protection Agency) inventory  (Solazzo et al. , 2012b). Finally, the EDGAR (Emissions Database for Global Atmospheric Research) global emissions inventory was recently added to CHIMERE and comparisons with the EMEP inventory over Europe are ongoing. -The biogenic emissions represent activities related to the vegetation. These emissions are computed using the global MEGAN (Model of Emissions of Gases and Aerosols from Nature) model  (Guenther et al. , 2006). -The mineral dust emissions represent the other part of "natural" emissions, but for non-reactive particle mainly generated by the surface layer dynamics  (Menut et al.
From the raw data to the data required for a specific simulation, a sequence of preprocessing actions is necessary, including a temporal disaggregation, the application of VOC, NO x and PM shares and the final species lumping into model species. A standard procedure is proposed but preprocessing actions can be bypassed by directly providing hourly anthropogenic emissions. For the standard procedure, it is proposed to prepare these data following two distinct stages, as displayed in Figure  9 :  -Step 1: Create an annual total gridded database per activity sector adapted to the horizontal simulation grid. This enables then to create monthly masses of emitted model species, already projected on the horizontal simulation grid, using seasonal factors (provided only for Europe). This step is performed only once each time a new domain is used.
975 Depending on the spatial domain, CHIMERE has been used with several anthropogenic emissions datasets. The largest number of studies was over western Europe and two datasets were used: (i) the EMEP database  (Vestreng (2003) ) and, more recently, the TNO database during the GEMS and 980 MACC projects  (Kuenen et al. (2011) ). These data are spatially interpolated to the model grid. This is performed using an intermediate fine grid with a 1km resolution (GLCF dataset,  Hansen and Reed (2000) ). Soil types described on the fine grid allow for a better apportionment of the emis-985 sions according to urban, rural, forest, crops and maritime -The fire emissions are more sporadic and require numerous and very different data: satellite to estimate the burned area each day, and a vegetation model to estimate the emitted amount for each chemical species (gas and particles).
From the raw data to the data required for a specific simulation, a sequence of preprocessing actions is necessary, including a temporal disaggregation, the application of VOC, NO x and PM shares and the final species lumping into model species. A standard procedure is proposed but preprocessing actions can be bypassed by directly providing hourly anthropogenic emissions. For the standard procedure, it is proposed to prepare these data following two distinct stages, as displayed in Fig. 9:  -Step 1: Create an annual total gridded database per activity sector adapted to the horizontal simulation grid. This then enables creation of monthly masses of emitted model species, already projected onto the horizontal simulation grid, using seasonal factors (provided only for Europe). This step is performed only once each time a new domain is used.