dataesr/scientific-paragraphs-categorization huggingface dataset:
Data availability. The data sets generated and analyzed during the current study (MycoDB_version4, FungalTree_version2, and PlantTree_version2) are available in the Dryad Digital Repository (https://datadryad. org//resource/doi:10. 5061/ dryad. 723m1. 4)  44. The data repository in Dryad also includes data from the published data descriptor for MycoDB  45. Code availability. The original R code written for the analyses presented here is available in the Dryad Digital Repository (https://datadryad. org//resource/ doi:10. 5061/dryad. 723m1. 4)  44.
, 1998)  at two different sites, Payerne (Switzerland) and Gerona (Spain), obtaining differences of -2. 7 ± 3. 4 and 0. 3 ± 9. 4 W m -2 , respectively. Dürr et al. (2005)  found good agreement between LDR measurements and simulations with the MODerate resolution atmospheric TRANsmission model (MODTRAN;  Berk et al. , 2000) , with values of +1. 5 and -3. 2 W m -2 for night-time (274 cases) and daytime (94 cases), respectively, at Payerne station. The main goal of this work is to compare BSRN LDR measurements with simulations made with two complex models using observed and modeled data from a relatively long period (between 2010 and 2016). The Izaña Atmospheric Observatory (IZO, http://izana. aemet. es, last access: 28 November 2017) is an optimal station to carry out this study, because all the model input parameters (PWV, precipitable water vapor; AOD, aerosol optical depth; total ozone; in situ N 2 O; in situ CO 2 ; CO 2 profiles; meteorological radiosondes) are measured at the station.
Radiative transfer models and input parameters The simulations of surface LDR were determined with two RTMs: libRadtran and MODTRAN. The libRadtran model (freely available from http://www. libradtran. org, last access 20 October 2017;  Mayer and Kylling, 2005)  used in this work is version 2. 0. 1  (Emde et al. , 2016). The simulations were performed with highly resolved absorption coefficients that were calculated using the absorption band parameterization REPTRAN. It is based on the high-resolution transmission molecular absorption (HI-TRAN) 2004 spectroscopic database, in which wavelength integrals were parameterized as weighted means over representative wavelengths  (Gasteiger et al. , 2014). The simulations performed using REPTRAN in the thermal range showed relative differences of about 1 % with respect to simulations performed with high spectral resolution models, and they are 6-7 times better than the simulations done with the LOWTRAN band parameterization  (Gasteiger et al.
, 2014). This work used is the MODTRAN version 6 (Berk and Hawes, 2017), an atmospheric transmittance and radiance model developed by the US Air Force Research Laboratory in collaboration with Spectral Sciences, Inc. We have selected a band model with a resolution of 1 cm -1 for spectral calculations. The MODTRAN band model molecular spectroscopy is based on the HITRAN database  (Rothman et al. , 2013). The main differences between the two models is in the molecular absorption band: while MODTRAN uses the HI-TRAN database  (Rothman et al. , 2013) , libRadtran uses the absorption band parameterization REPTRAN  (Gasteiger et al. , 2014). For both models, the LDR simulations were calculated by using the DISORT (DIScrete Ordinate Radiative Transfer) solvers, developed by  Chandrasekhar (1960)  and  Stamnes et al.
(1988 Stamnes et al. ( , 2000)) , as radiative transfer equation (RTE) solver, and based on the five multi-stream discrete ordinates algorithm. The number of streams used to run DISORT was 16. For each simulation, the integrated downward irradiance was calculated in the spectral range 4-100 µm. The two models were run using the same inputs, atmosphere and geometry in order to minimize. The rest of the inputs measured at IZO are -Radiosondes: temperature and relative humidity (RH) profiles: in this work, we used the AEMET's meteorological radiosondes dataset. Radiosondes are launched twice a day, at 11:00 and 23:00 UTC at the Güimar station (WMO GRUAN station no. 60018, 105 m a. s. l. ). This station is located at the coastline, approximately 15 km to the southeast of IZO. Vertical profiles of pressure, temperature and relative humidity were obtained with Vaisala RS92 radiosondes (Rodriguez-Franco and  Cuevas, 2013; Carrillo et al.
According to the results obtained in Sect. 4. 1, the uncertainties of PWV and AOD dominate the total uncertainty; thus, the LDR bias was analyzed. The box plot of LDR bias for different PWV is presented in Fig. 4a  and b. Both models tend to underestimate LDR (up to 5 W m -2 ) in the case of daytime measurements with Table  4. Statistics for the LDR bias between libRadtran and MODTRAN simulations and BSRN LDR at IZO (in W m -2 ) performed with daytime (1048 cases) and night-time (1014 cases) data in the period 2010-2016 (MB, mean bias; RMS, root mean square of the bias; R 2 ). The statistics for the relative bias are in brackets (in %).
Summary and conclusions Cloud-free longwave downward radiation (LDR) measured at the BSRN Izaña Atmospheric Observatory was compared with two complex RTMs, libRadtran v2. 0. 1 and MOD-TRAN v6, in the period 2010-2016, for a high number of cases (2062) grouped into daytime (11:00 UTC; 1048 cases) and night-time (23:00 UTC; 1014 cases) datasets. IZO is an optimal station to carry out this study, because all the model input parameters (precipitable water vapor, aerosol optical depth, total ozone, N 2 O in situ, CO 2 in situ, CO 2 profile and meteorological radiosondes) are measured at the station. The agreement between measurements and simulations is excellent and very similar for both models. The mean bias (simulations -BSRN measurements) is -1. 73 W m -2 (-1. 1 %) and 0. 15 W m -2 (0. 1 %) for libRadtran/BSRN at daytime and night-time UTC, respectively, and -1.
Introduction Hardware and software advances have contributed greatly to the deposition of over 100 000 crystal structures in the Protein Data Bank (PDB;  Berman et al. , 2003). Yet, despite the rapid growth in the number of macromolecular crystal structures determined, as of September 2017 over 93% of PDB depositions relate to diffraction data collected to a resolution better than 3. 0 A ˚, while over 98% of depositions relate to diffraction data collected to resolutions better than 3. 5 A ˚. Valuable information can be obtained from low-resolution structures  (Schro ¨der et al. , 2010). For example, biologically important macromolecules, including large macromolecular assemblies, membrane proteins and receptors, tend to result in crystals that diffract relatively poorly. Unfortunately, solving crystal structures from low-resolution data is difficult and timeconsuming and can fail.
The combined function, implemented in the program REFMAC5 (v. 5. 8. 0155;  Murshudov et al. , 2011) , was used by CRANK2 for all of the reciprocal-space refinement, phasing and phase combination. Furthermore, CRANK2 used Parrot (v. 1. 0. 4; Cowtan, 2010)  for crystal-space density modification and Buccaneer  (Cowtan, 2006; v. 1. 6. 3  was used for all data sets apart from the SecYEG-SecA complex, where the older v. 1. 6. 1 was used owing to a weak density-filtering regression) for model building. Density modification by Parrot included solvent flattening, histogram matching and, for data sets with multiple monomers in the asymmetric unit, automatic NCS operator determination and NCS averaging. NCS averaging was not performed for F 1 -ATPase as the subunits are in different conformations. The programs MOLREP  (Vagin & Teplyakov, 2010)  and  Phaser (McCoy et al.
SecYEG-SecA SAD data set at 4. 5 A ˚resolution Crystals of the SecYEG-SecA protein-translocation complex  (Zimmer et al. , 2008)  from Thermotoga maritima diffracted to 4. 5 A ˚resolution and the resulting data set contained anomalous signal from selenomethionine-derivatized SecYEG. The authors originally solved the structure by molecular replacement, NCS and cross-crystal averaging, experimental MAD phases from selenomethionine-derivatized SecYEG and interative manual model building and refinement. To test our new MR-SAD protocol, we started from a molecular-replacement solution obtained from the 7. 5 A resolution structure of Aquifex aeolicus SecYEG and Bacillus subtilis SecA by the same authors (PDB entry 3dl8) that resulted in an R free factor of 51. 8% after 'jelly-body' refinement in REFMAC5  (Murshudov et al.
Acknowledgements We thank  Gwyndaf Evans ,  Eugene Krissinel ,  Ruslan Sanishvili  and  Piotr Sliz  for critically reading the manuscript. For the diffraction data collection from crystals of F 1 -ATPase and AAA-ATPase we gratefully acknowledge the provision of beam time on beamlines ID29 and  ID14-4  through the  ESRF  in-house research programme. ARC and AH acknowledge  Jens Kaiser  and the scientific staff of SSRL beamline  12-2  for their support with X-ray diffraction measurements. The operations at SSRL are supported by the  Department of Energy  and the  National Institutes of Health. Author contributions are as follows. PS and  NSP  designed the research and analyzed the results. HO, GAL and SL are responsible for data set 1,  ARC  and AH for data set 2, DA and GS for the GPCR ECR-Mb complex data set, AAM for the AAA-ATPase data set, and MWB, GAL, SM and CM-D for the F 1 -ATPase data set.
Of 55 spoligotypes not matching any extant shared international type (SIT) in SITVIT database, 22 fitted into 15 newly-issued SITs. Certain autochthonous South American genotypes were found to be actively evolving. LAM3, which is wild type for RD rio , was the predominant LAM subfamily in both districts and the RD rio signature was rare among autochthonous, newly created, SITs and orphan patterns. Two genotypes that are rarely observed in neighboring countries ̶ SIT2/H2 and SIT159/T1 Tuscany ̶ were conspicuously represented in Argentina. The infrequent Beijing patterns belonged to Peruvian patients. We conclude that the genotype diversity observed reflects the influence of the Hispanic colonization and more recent immigration waves from Mediterranean and neighboring countries. Unlike in Brazil, the RD rio type does not play a major role in the tuberculosis epidemic in Buenos Aires.
, 2016). Single nucleotide polymorphism barcode approaches based on high throughput whole genome technology are probably the most accurate tools currently available to classify clinical isolates and construct high resolution and reproducible phylogenies. However, until data produced by such technologies become widely available, the analysis of information based on simpler genotyping approaches can still cast light on the genotypic structure of M. tuberculosis in as yet insufficiently explored areas  (Brudey et al. , 2006; Midori Kato-Maeda et al. , 2011; Sola et al. , 2001). Globally, seven M. tuberculosis lineages have been described, with different phylogeographic specificities. These are Lineage 1 Indo-Oceanic (which corresponds to East African Indian or EAI lineage in the SITVIT database), Lineage 2 East-Asian (including Beijing), Lineage 3 East-African-Indian (corresponding to Central Asian or CAS in SITVIT database), Lineage 4 Euro-American, Lineage 5 West Africa (M.
Genotyping DNA was obtained by boiling lysis of actively grown bacilli harvested from Löwenstein-Jensen slants. Spoligotyping was performed according to the international standard protocol  (Kamerbeek et al. , 1997). We compared the spoligotypes in SITVITEXTEND, which is a proprietary extension of the SITVITWEB database available at the website of the Institut Pasteur de la Guadeloupe (http://www. pasteurguadeloupe. fr:8081/SITVIT_ONLINE/) (Demay et al. , 2012). Isolates with autochthonous, orphan or newly created spoligotypes of LAM, T and undefined families were submitted to a multiplex PCR designed to identify the LAM-RD rio subtype through the detection of a large specific deletion  (Gibson et al. , 2008). We did not investigate the RD rio status of geographic-specific genotypes classified as Haarlem, S, and X because these families are genetically distant from LAM and therefore are not likely to harbor the LAM RD rio specific deletion.
Overall strain family distribution The overall frequencies of M. tuberculosis families were: T 35. 9% (n = 293), LAM 33. 2% (n = 271), Haarlem 19. 5% (n = 159), S 3. 2% (n = 26), X1. 5% (n = 12), Ural 0. 7% (n = 6), BOV 0. 2% (n = 2), Beijing 0. 2% (n = 2), Cameroon 0. 2% (n = 2). A total of 783 (95. 9%) isolates were represented by 145 SITs (Shared or Spoligotype International Types), including 5. 2% (n = 42) isolates classified in SITs with unknown (Unk) spoligotype signatures (Suppl. Table  2 ). Out of 55 isolates whose patterns did not match any SIT registered in the proprietary SITVITEXTEND database, 22 were classified in 15 newly created SITs after matching an orphan pattern in SITVITEXTEND or due to two or more isolates sharing a new pattern within the present study. The remaining 33 isolates displayed orphan spoligo patterns (i.
e. , did not match any other pattern in the SITVITEXTEND database). A total of 713 (83. 4%) isolates were found to cluster in 74 SITs, ranging from 2 to 78 isolates per SIT and other 70 SITs were represented by a single isolate in this study (in addition to the 33 newly described orphan patterns). Suppl. Table  3  shows clustered SITs containing ≥10 isolates in Argentina (i. e. present study plus preexisting entries from Argentina in SITVIT EXTEND database) and their worldwide distribution according to SITVITEXTEND database.
Phylogenetic inferences A minimum spanning tree illustrates phylogenetic relationships among the 816 M. tuberculosis spoligotypes included in the study (Fig. 1 ). Two main branches, Haarlem and LAM families, emerge from SIT53/T1, the core of the Euro-American lineage, in addition to two minor branches for S and X families. Two conspicuous clades, SIT2541/ T1 and SIT159/T1 Tuscany, sprout one after the other in a single branch directly from SIT42/LAM9 (the core spoligotype for the LAM family), followed by related autochthonous orphan patterns. The MLVA MtbC15-9 type for our isolates with SIT159 is identified as 121-51 (132244332224126153322622) and the MIRU-VNTR type for our isolates with SIT2541 (134284332224116143332632) is absent from the MIRU-VNTRplus database. According to the MIRU-VNTRplus tool, both MIRU-VNTR types are close to each other (distance 0.
LAM391/LAM4 strains in this study are wild type for RD rio , as are other LAM391/LAM4 strains from Argentina and Paraguay present in our database but not included in this study (data not shown). Owing to its heterogeneity and ambiguity, the T superfamily is thought to provide scarce phylogenetic information. However, certain genotypes within this family do bear true identity. SIT2541/T1 is closely related to, and plausibly progenitor of, the newly created SIT4125 and two new orphan types (Or22, Or29) described herein (Suppl. Table  5 ). Thus, to the best of our knowledge, SIT2541/T1 could be considered another autochthonous South American genotype which is emerging and actively evolving in Argentina. Our results suggest that SIT2541 and SIT159 are closely related to each other and might derive from the LAM family.
Acknowledgements This work was supported by  Fondo para la Investigación Científica y Tecnológica, Argentina  ( PAE-PICT2323 ),  Argentina. The work done at  Institut Pasteur de la Guadeloupe  was supported by a  FEDER grant , financed by the  European Union and Guadeloupe Region  ( Programme Opérationnel FEDER-Guadeloupe-Conseil Régional 2014-2020 , Grant number  2015-FED-192 ). VR conceived and designed the experiments. JM, RP, and EG performed the experiments. DC and NR performed the analysis on the proprietary SITVITEXTEND database. JM and VR wrote the manuscript. JM, RP, EG, AR, TH, EM, AT, NS, BL, DC, NR and VR analyzed the data, read and approved the final version.
This study aims at assessing the burden of rare (minor allele frequency < 1%) predicted damaging variants in the whole exome of 92 bipolar I disorder (BD) patients and 1051 controls of French ancestry. Patients exhibiting an extreme phenotype (earlier onset and family history of mood disorder) were preferentially included to increase the power to detect an association. A collapsing strategy was used to test the overall burden of rare variants in cases versus controls at the gene level. Only protein-truncating and predicted damaging missense variants were included in the analysis. Thirteen genes exhibited p values exceeding 10 -3 and could be considered as potential risk factors for BD. Furthermore, the validity of the association was supported when the Exome Aggregation Consortium database non-Finnish European population was used as controls for eight of them.
Bioinformatics pipeline Exome samples were all processed through the same bioinformatics pipeline following GATK 3. 3-0 Best Practices recommendations and as previously described  8. Reads were mapped to the GRCh37 1000Genomes build using BWA 0. 7. 5a  17. Picard Tools 1. 101 (http://picard. sourceforge. net) was used to flag duplicate reads. GATK  18  was applied for short insertion and deletion (InDels) realignment, base quality score recalibration (BQSR) and finally single-nucleotide variants and InDel discovery using Haplotype Caller across all samples simultaneously. Variants were annotated with SnpEff 4. 2  19  and SnpSift 4. 2 20 software using dbNSFP 2. 9. 1 and Ensembl GRCh37. 75. Exonic and splice site variants (located ± 2 bp around each coding exon) with a minor allele frequency (MAF) of <1% in our whole data set were then extracted within each gene region.
Quality check The sample QC was performed on the bi-allelic sites of the data set fulfilling the VQSLOD sensitivity threshold of 99. 5% for single-nucleotide variants and 99% for InDels, as recommended in GATK best practices. To include multi-allelic sites in the analysis, they were converted into multiple bi-allelic variants that were then left-aligned using bcftools 1. 3. Most checks were carried out with PLINK 1. 9 (https://www. cog-genomics. org/plink2). All individuals in the sample were processed through the following steps: (i) verifying concordant sex information using Plink sex check, (ii) discarding contaminated samples identified as such by significantly high heterozygosity rates and freemix contamination scores provided by the VerifyBamID software, and (iii) discarding the sample of worst overall quality among each pair of samples with Plink pi_hat relatedness estimation exceeding 0.
Consistency with ExAC All variants detected in non-Finnish European (NFE) individuals from the Exome Aggregation Consortium database (ExAC) were downloaded from the open access web resource http://exac. broad institute. org/. Variants with MAF below 1% and missingness below 20% within this population were retained for analysis. They were annotated for functional consequences with SnpEff 4. 2 and SnpSift 4. 2 and classified into PTV, Mis3, Mis2, Mis1, and benign variants exactly like all variants from our own data set. For each variant class of interest (PTV, PTV +Mis3, PTV+Mis3+Mis2, PTV+Mis3+Mis2+Mis1), allele counts were aggregated by gene (total allele count, TAC) and divided by the maximum number of individuals with allele information (ANmax) on this gene to obtain an approximation of the proportion of variant carriers in the NFE population.
NDUFAF2 gene showed a large depletion of PTV, Mis3, Mis2, and Mis1 missense variant carriers among our controls compared with what was observed in the ExAC NFE population, which suggests that the strength of the association might result from chance alone for this gene. To a lesser extent, two genes showed a relatively slight depletion in the same categories of variants in our controls compared with ExAC NFE data: CCDC171 and FAM19A3. Of note, LGR5 gene did not exhibit a clear depletion in PTV, Mis3, and Mis2 variants but the inclusion of Mis1 variants disclosed a significant depletion of variants in our controls. Hence, comparison with ExAC data also casted doubts about the reality or the strength of the association of CCDC171, FAM19A3, and LGR5 variants with BD. On the contrary, MYOE1 gene suffered from a lack of variants within the ExAC NFE population, not allowing us to compare cases to the ExAC NFE population.
In the light of ExAC, the remaining association results seem more robust. The absence of PTV, Mis3, Mis2, or Mis1 variants in our controls overestimated the strength of the association of ACPP, ERI3, and PLCXD3 variants with BD in our data but appears to be consistent with the extremely low TAC/ANmax ratio in ExAC NFE data (Table  3 ). Regarding the other genes TCF7L1, BOC, VPS52, ABCC10, and ARHGAP9, comparison ExAC NFE controls displayed odd ratios of similar range, thus supporting our results.
Hence, the modeller following a hybrid approach has many issues to consider: how many parallel threads per node (between zero and the number of hyper-threads supportable on the node) and what parts of the code can take advantage of this technique? There is a balance to be struck which depends on knowledge of the code, the hardware, and the quality of the vendor-supplied compilers and libraries, and this balance needs to be re-evaluated for each architecture and model version. Other programming models, such as the partitioned global address space (PGAS), try to address the problem of the locality of memory with a single abstraction to address both shared and distributed memory. Implementations include the C extension, Unified Parallel C, Coarray Fortran, Chapel, and the (Open)SHMEM library.
component complexity (climate models typically in- clude more components associated with longertimescale processes), and 3. how they are initialised (weather models by complex data assimilation systems and climate models by very long duration "spin-up" runs for components with poor observational coverage). These differences have become blurred as weather prediction has begun to exploit ocean, snow, and ice models and These common attributes can be linked together in very complex ways. At the high level,  Alexander and Easterbrook (2015)  showed some permutations of how large components can be coupled together in eight Earth system models and at the same time indicated the relative complexity of the key components. In general, the most complexity was found in the atmospheres of these models, although some Earth system models of intermediate complexity (EMICs) were dominated by oceans.
ecmwf. int/en/forecasts/ documentation-and-support, last access: 29 April 2018), the GFDL HiRAM (2012 public release; https://www. gfdl. noaa. gov/hiram/, last access: 29 April 2018;  Zhao et al. , 2009) , and the Met Office UM (v6. 6; Davies et al. , 2005). This figure describes the flow of data associated with model prognostics within a model time step. IFS is a spectral transform model. The key property of such a model is that due to the properties of the Laplacian operator, equations for vorticity, divergence and diffusion can be easily calculated in spectral space, with advection and physics calculated on a grid distributed in physical space. The model exploits two time levels and a semiimplicit, semi-Lagrangian time-stepping routine. The time step begins with variables held in spectral space, which are then converted back to grid point space for the physics to be executed sequentially, after which they are converted back to spectral space via a fast Fourier transform (FFT) in longitude and a Legendre transform (LT) in latitude to spectral space, for which the Helmholtz equation resulting from the semiimplicit time stepping is solved and horizontal diffusion is computed.