dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical Assessment Data collected by the cancer registries and used in this study were age, comorbidities (the number of chronic diseases and their severity), and the BC characteristics coded according to the International Classification of Diseases for Oncology, 3 rd Edition. In addition, the BC pathological-tumour-node-metastasis (PTNM) was assessed using the recommendations of the 5th edition of Union for International Cancer Control. The stages have been grouped into three groups: the first group combined stage 0 and I tumours less than 2 cm without lymph node involvement, the second group combined stage II and III tumours comprising between 2?1 cm and 5 cm, with or without lymphadenopathy, and the third group corresponded to stage IV metastatic tumours. Furthermore, the Scarff Bloom Richardson (SBR) grade, modified by Elston and Ellis, was also measured because this classification of BC evaluates the ''aggressiveness'' of the tumour by taking into account the speed of tumour development and its management  [12].
Patients Between April 1, 2011 and March 31, 2012, we performed a monocentric retrospective study that included all patients who had undergone a CT scan for a non-traumatic severe hemorrhage. Exclusion criteria were represented by all suspected traumatic contexts, hemorrhages caused by an underlying tumor, a vascular malformation, or a postoperative bleeding. During this period, 93 patients were included (50 males, 43 females), mean age 76 ± 11 years, who had been referred by various departments: emergency (n = 75), cardiology (n = 7), recovery unit (n = 5), rheumatology (n = 2), infectious-disease department (n = 2), and internal medicine (n = 2). Seventy-three patients underwent a cerebral, 16 an abdominal, and 4 a lower-limb CT scan. Data were collected using Xplore Exploitation software, version 6.
Statistical analyses The statistics were computed using STATA V10 software (Stata Corp, College Station, Texas, USA). Data are expressed as frequencies and associated percentages for categorical data, and as means ± standard deviations for continuous data. The categorical data from the two treatment groups were compared using the chi-squared test (or Fisher's exact test, if necessary). Continuous data were compared using Student's t-test (or the Kruskal-Wallis test, if necessary), normality was verified by the Shapiro-Wilk test and homoscedasticity by the Fisher-Snedecor test. The risk ratio, for patients who received AC or APA, for a cerebral hemorrhage and subsequent cerebral herniation, a rectus sheath, iliopsoas bleeding, or a quadriceps hematoma were shown with their corresponding 95% confidence intervals.
, 2011). Antifreeze proteins (AFP) allow certain organisms to survive in subzero environments by binding, through an 'icebinding surface' (IBS), to ice-crystal nuclei as they form, inhibiting their further growth. The neutron structure was determined to 1. 85 A ˚resolution (PDB entry 3qf6;  Howard et al. , 2011)  using a perdeuterated crystal of only 0. 13 mm 3. The neutron diffraction data revealed a tetrahedral 'ice-like' cluster of four water molecules at the IBS that was not observed in previous X-ray studies; using these waters, it was possible to model the AFP-ice interaction and to illustrate that the hydrophobic residues of the IBS bind to, and recognize ice, via the holes in the six-membered rings of the ice crystal structure. In the course of parallel work for methodological purposes  (Petit-Haertlein et al.
, 1999). The data were then scaled and merged using the CCP4 program SCALA  (Winn et al. , 2011). Datacollection and processing statistics for the neutron data can be found in Table  2. Advances in sample-environment options now allow the routine collection of neutron data at cryotemperatures  (Coates et al. , 2014) , which not only can improve the nuclear scattering density definition for mobile side chains and water molecules  (Blakeley et al. , 2004)  but also allows the study of cryo-trapped intermediates  (Casadei et al. , 2014).
Structure refinement The previously deposited perdeuterated neutron structure of type III AFP (PDB entry 3qf6) was used as the starting model and the corresponding X-ray diffraction data (at 1. 05 A resolution, measured at ID29, ESRF; de  Sanctis et al. , 2012)  were used for the joint X-ray/neutron refinement of the selectively protonated deuterated type III AFP. The assumption here is that the X-ray data from the selectively protonated deuterated crystals should be the same as those from the perdeuterated crystal, as H and D are equivalent using X-rays. To obtain an initial refinement model, the original model was randomly perturbed and multiple conformations and all water molecules were removed. Restrained joint X-ray and neutron  (Afonine et al. , 2010)  refinement was then conducted using  ).
close vicinity of hydrophobic residues, as even a small number of H atoms are capable of obscuring the signal from adjacent water molecules. SJF was supported by  FWF  grant  P22862  from the  Austrian Science Fund. All figures were prepared with PyMOL (DeLano, 2002). This work was supported by the  Human Frontiers Science Program  grant  RGP0021/2006-C  and benefited from the activities of the DLAB consortium funded by the  European Union  under contract  HPRI-2001-50065  and from  United Kingdom Engineering and Physical Sciences Research Council (EPSRC) -funded activity within the  ILL Deuteration Laboratory  under grant  GR/R99393/01. We thank the  ILL  for provision of neutron beamtime on the LADI-III instrument. EIH is a member of the ' Carrera del Investigador', CONICET, Argentina. This work was supported by the  Centre National de la Recherche Scientifique (CNRS) , by the  Institut National de la Santeet de la Recherche Medicale  and the  Hopital Universitaire de Strasbourg (HUS).
SDSS spectroscopy In order to minimize the need for new spectroscopy, we downloaded and analyzed the existing SDSS spectroscopy of LHS MIPS 24µm targets directly from the SDSS DR7 SkyServer 4. The SDSS spectra have a resolving power of R ∼1800-2200, with a wavelength coverage of 3800-9200 Å. In this study, we use the '1d' calibrated spectra from the DR7 Data Archive Server 5  , stored in logarithmic pixel scale of 10 -4. The redshifts given in SDSS DR7 SpecObj catalog were determined by the spectro1d package  (Stoughton et al. 2002). We made a SQL search (with a 5 degree radius, r < 22. 5) in the SDSS DR7 SpecObj catalog 6 and found 2,978 objects. Spectra for all SDSS objects with redshifts in the LHS field were downloaded, irrespective of their SDSS classification. We matched these sources with the SWIRE MIPS 24 µm catalog, and excluded 2,019 SDSS targets not detected by SWIRE, and 38 SDSS targets with S 24 <400 µJy.
The spectral range covered by Hectospec allows detecting one or more typical emission lines present in the spectra of quasars and galaxies (CIV, MgII, Hβ , [O III ], Hα) for galaxies to z ∼ 1, and quasars to z ∼4. 5. The redshifts measured by HSRED also use code adapted from SDSS and use the same templates as SDSS. All spectra were visually inspected for validation as described below. A redshift quality flag is assigned to each spectrum, following the same procedure used for the DEEP2 survey  (Willmer et al. 2006; Newman et al. 2013) , where redshift qualities range from Q = 4 (probability P > 95% of being correct), 3 (90% < P < 95%), 2 (P < 90%) and 1 (no features recognized). Q = 2 spectra are assigned to objects for which only a single feature is detected, but cannot be identified without ambiguity.
Broad Line Object Identification The 3612 reduced 1d SDSS and Hectospec spectra were first fitted using our IDL program adopted from the S11 procedure. This program fits a polynomial continuum (S continuum = A λ × ( λ λ 0 ) α λ ) and a Gaussian around the redshifted CIV, MgII, and Hβ regions based on the HSRED or SpecObj redshifts (See also §3). Objects are kept as quasar candidates if they have at least one BEL (FWHM > 1000 km s -1 ,  Schneider et al. (2007) ) in the secure spectral ranges with limited atmospheric extinction and instrument errors: 3850-8400 Å  (Fabricant et al. 2008)  for MMT targets, and 3850-9000 Å  (Stoughton et al. 2002)  for SDSS targets. Outside of these ranges the spectra start to be bounded by sky-subtraction errors and therefore not reliable. The MMT range is from  Fabricant et al.
(2008) , chosen to be most consistent (< 5%) with SDSS, after comparing the optical spectra taken from SDSS and MMT of the same targets. The IDL program identifies 236 MMT09, 28 MMT05b, and 132 SDSS BEL objects, all of which have an emission line equivalent width (EW) greater than 6. Given our flux limit (r < 22. 5), the majority of the BEL quasars (83% with Mi < -23) also satisfy the M B < -23, the quasar definition in  Schmidt & Green (1983)  (Fig. 5 ). Since the SDSS quasar definition is also based on the BEL features  (Schneider et al. (2007) ) in the following text we will simply refer to these BEL objects as quasars. As a check, we visually examined all 3,612 spectra from both the MMT and SDSS surveys. This process removes 22 MMT09, 1 MMT05b, and 5 SDSS objects that were erroneously identified as quasar due to bad fits.
We wrote an IDL procedure that first measures and subtracts the continuum, and then fits one or more Gaussian profiless to the emission line. The procedure is based on the code used for the SDSS quasar catalog (S11), but includes more generality. In the cases where a single Gaussian is not a good fit to the line profile, up to 3 Gaussian components are allowed. An F-test is used to evaluate the need for each additional component. The F-test is widely used to compare the best fits of different models based on least squares comparison and the F distribution. The F value is computed as: F = χ 2 -χ 2 new DOF -DOF new / χ 2 new DOF new , ( 1 ) where DOF is the number of degrees of freedom for the variance ("Numerical Recipes", Second Edition, 1992). We compute the F-test values using the IDL mpftest program 8.
Uncertainties of Spectral measurements and Error Estimates The uncertainties in the spectral measurement arise from three main sources: (1) the quality of the spectra and instrument errors; (2) the adopted fitting process -e. g. ambiguity introduced from using certain line profiles, and from using one or multiple components; and (3) special features that could affect the algorithm -in particular, a narrow line component, especially for MgII and CIV (cf. S11); instrumental broadening with BEL; or strong NALs or BALs. The fitting errors based on S/N are automatically accounted for through our IDL program using the IDL program mp f it f un. pro 9. This program returns the 1 σ errors of each parameter from the covariance matrix. The quality of the spectra directly affects the fitting results. We observed similar S/N dependences as in S11.
[5] ; Emberlin et al. [6] ; Rodrı ´guez-Rajo et al. [7] ; Sofiev et al. [8] ). In addition, in some urban areas, particularly in Europe, the aerobiological datasets now span several decades and are beginning to provide insights into biogeographic variation in landscape phenological patterns, and how these patterns respond to current climate change  [9] [10] [11] [12]. In contrast to the well-studied Northern Hemisphere, aerobiological studies of the Southern Hemisphere have been conducted in isolation and limited to monitoring specific cities for narrow time periods with few attempts made to discern generalised patterns across a country or region (see  [13]  for an exception). Indeed, many of the existing aerobiological studies located in Australia and New Zealand have been motivated by an interest in the public health burden of pollen sensitization and allergic asthma at major population centres.
Aerobiology in the Southern Hemisphere is as complex as that in the Northern Hemisphere, given the variability of abiotic factors and environmental gradients that span the tropics to the temperate zones. The coexistence of very distinct indigenous vegetation, introduced Northern Hemisphere ornamentals, as well as exotic invasive species are additional variables that contribute to the aerobiology of each Southern Hemisphere regional center. Here we compile and analyse an historic atmospheric pollen dataset from 11 cities across Australia and New Zealand and examine the regional variations in pollen content and relative abundance. The geographical distribution of the 11 Australasian sites was broad enough to enable further examination of the role that biogeography plays in regional and urban variations in pollen composition.
/min. The samplers were set-up to operate for 6 min every hour. Sampling rods were replaced every 24 hours, stained with Calberla's solution, and examined under a transmitted light microscope. Raw pollen counts for each of the pollen/spore types identified were converted to the volumetric index (grains/m 3 of air) using a standard formula taking into account the sampling period and volume of air sampled  [22]. The daily mean concentration of fungal spores, particularly Alternaria, was also recorded at some stations (Darwin, Brisbane, Sydney, Canberra, Melbourne and Hobart), though detailed analysis of these data is not presented here. The raw pollen counts for each site used in this paper have been archived in excel files at the Australian Centre for Ecological Analysis and Synthesis data portal (http://aceas-data.
Pollen Identification Pollen identification was aided by the existing reference collections held by individual analysts or collated into digital or online reference collections (e. g. , the Department of Archaeology and Natural History at the Australian National University, http:// apsa. anu. edu. au, the CD published by Hjelmroos et al. [28] ). In a number of these aerobiological surveys, it was not possible to place identifications at a species or genus taxonomic level. In these cases the taxon name reverted to the higher level (i. e. , all genus-level identifications were placed into the relevant family) such as the Poaceae and Myrtaceae. The introduced tree taxa are referred to by their generic name (Alnus, Betula, Pinus, Quercus, Salix and Ulmus) as all are represented in Australia and New Zealand by more than one species in the genus and these are not distinguished in the available datasets.
Pollen Count Analysis In order to reduce variation in pollen abundance records due to variations in equipment or handling protocols, the datasets were transformed into percentage values based on a total pollen sum (total pollen abundance of a single taxon recorded over the time of study divided by the total pollen abundance of all taxa recorded over the time of study) (Table  2 ). This data transformation mitigated other sources of variation in abundance between monitoring sites such as location, as coastal cities are exposed to a smaller proportion of land area and hence vegetation sources than inland cities (Figure  1 ). The pollen season has been defined in many ways by different authors  [29]. To date, no standardized approach to identifying the pollen season has been defined in Australia or New Zealand  [13, [15] [16] [17].
The length of the vectors correspond to the square-root of the r 2 values, so weak predictors have a shorter length than strong predictors. High r 2 values indicate a vector that is strongly associated with site/species variation in the ordination space. Low r 2 values would indicate a vector that doesn't really explain much in the ordination space. Environmental vectors representing three meteorological variables (Mean annual maximum and minimum temperature and mean annual precipitation) and the proportion of four broad land cover types in a 100 km radius around each urban area were fitted to the species ordination using the envfit function of the vegan package in R  [33] , with random permutations of environmental variables performed in order to assess their significance. We make the assumption that the proportion of land cover types found in a 100 km radius around pollen sampling  Pollen taxa values are expressed as a percentage of the total pollen count for the record period available in each urban area (*excluded from ranking as only occurs in one urban area).
Pollen Ranking Among Urban Areas The airborne pollen data were ranked by percentage representation, with the top 20 pollen taxa making up. 80% of the total pollen collected across 11 cities in Australia and New Zealand (Table  2  and Figure  3 ). The dataset incorporates taxa of high relative percentage representation that commonly occur in two or more urban areas (Arecaceae is an exception because of the high values recorded in Darwin). The most significant taxa across all sites are Poaceae and Cupressaceae, making up over 50% of the total airborne pollen in urban environments throughout the year. These are followed by Betula, the trees and shrubs in Myrtaceae, Pinus, Oleaceae, Casuarina, and the important herbaceous taxa such as Plantago and Rumex. Differences between the pollen taxa rankings (highest to lowest percentage representation) across the broad biogeographic regions of Australia (tropics to temperate: Australian Tropical/Subtropical = Darwin, Brisbane; Australian Temperate = Perth, Sydney, Canberra, Melbourne, Hobart) and New Zealand (North and South Island: NZ North = Kaikohe, Auckland; NZ South = Christchurch, Dunedin), reflect the strong climate controls on plant distributions, particularly those associ-.
3DCLOUD solves drastically simplified basic atmospheric equations, in order to simulate 3-D cloud structures of liquid or ice water content. Secondly, the Fourier filtering method is used to constrain the intensity of ρ, β, L out and the mean of τ or IWC of these 3-D cloud structures. The 3DCLOUD model was developed to run on a personal computer under Matlab environment with the Matlab statistics toolbox. It is used to study 3-D interactions between cloudy atmosphere and radiation.
9d. Table 1. Time step, process time for one time step and process time for 2 h-simulation with 3DCLOUD model, as a function of the numerical resolution. DYCOMS2-RF01 and BOMEX cases are presented. A comparison between 3DCLOUD and BRAMS LES computation time for a specific DYCOMS2-RF01 case is added. 3DCLOUD (Matlab code) runs on a personal computer with Intel Xeon E5520 (2. 26 GHz) and BRAMS (Fortran code) runs on a PowerEdge R720 with Intel Xeon E5-2670 (2. 60 GHz), both of them having a single-processor configuration.
Conclusions 3DCLOUD is a flexible three-dimensional cloud generator developed to simulate with a personal computer and under Matlab environment, synthetic but realistic stratocumulus, cumulus and cirrus cloud fields. Simplified dynamic and thermodynamic laws allow the generation of realistic liquid or ice water content from meteorological profiles. The stochastic process with the Fourier framework allow us to provide ice water content or optical depth sharing similar statistical properties to those observed in real clouds such as the inhomogeneity parameter (set by the user) and the invariant scale properties characterised by a spectral slope close to -5/3 from the smaller scale (set by spatial resolution of grid computation) to the outer scale (set by the user). In order to simulate cloud structures, 3DCLOUD solves simplified basic atmospheric equations and assimilates the cloud coverage set by the user (only for the stratocumulus and cumulus regimes) and meteorological profiles (pressure, humidity, wind velocity) defined by the user.
For example, 3DCLOUD was used to quantify the impact of stratocumulus heterogeneities on polarized radiation measurements performed by POLDER/PARASOL  (Cornet et al. , 2013)  as well as the influence of cirrus heterogeneities on brightness temperature measured by IIR/CALIPSO  (Fauchez et al. , 2013 (Fauchez et al. , , 2014)). We still have to develop a stochastic process to generate 3-D field of cloud effective radius. In a longer term, investigations will focus on the generation of 3-D mixed phase cloud and eventually on the simulation of 3-D rain rate. Another task will be to provide a FORTRAN code of 3DCLOUD, assumed to be faster than the current Matlab code.
Acknowledgements. This work has been supported by the  Programme National de Télédétection Spatiale  (PNTS, http://www. insu. cnrs. fr/actions-sur-projets/pnts-programme-national-de-teledetection-spatiale), grant no  PNTS-2012-08  and by the  Office National d'Etudes et de Recherches Aérospatiales  (the  French aerospace laboratory, ONERA ). The authors thank  Wolfram Wobrock  and  Valery Shcherbakov  for the numerous fruitful discussions on physics and atmospheric dynamics. We also thank the two reviewers whose remarks largely improved the quality of this article. Edited by:  R. Neale  The publication of this article is financed by  CNRS-INSU.
All assigned chemical shifts have been deposited to the BMRB (accession code: 19782). The redox state of the eight Cys residues of recombinant RodA was analysed using the Cys Cβ and Cα chemical shifts as described by Sharma and co-workers  (Sharma and Rajarathnam 2000). The data is consistent with all the Cys residues being involved in disulphide bonds. Importantly, preliminary analysis of the pattern of 1 H-1 H nuclear Overhauser effects (nOes) observed in 13 C and 15 N edited NOESY spectra (not shown), unambiguously indicated that the disulphide bonds are formed by cysteines C1-C6 (39-116), C2-C5 (47-110), C3-C4 (48-88) and C7-C8 (117-135). The latter pattern is in accordance with the one established by chemical methods for other hydrophobins isolated from fungal sources  (Kwan et al. 2006)  or by X-ray crystallography from recombinant proteins  (Hakanpaa et al.
2006a; Hakanpaa et al. 2006b ), supporting that this pairing is a characteristic feature of hydrophobins. Furthermore, the superposition of 15 N-1 H HSQC spectra of the recombinant protein and the protein extracted from A. fumigatus spores (not shown) indicated that the recombinant and A. fumigatus proteins show the same structure and disulphide bond pattern. Therefore, the recombinant protein is natively folded and contains the native S-S bond topology. The secondary structure of RodA was determined from the backbone and Cβ chemical shifts using the PECAN software  (Eghbalnia et al. 2005). It consists of four β-strands (S1: Q44-T52, S2: L82-C88, S3: I114-C117, S4: L133-C135) and two α-helices (H1: V32-K38, H2: G63-L72). Except for helix H1, the secondary structure elements are located in the vicinity of cysteine residues (Figure  2A ).
Acknowledgments This work was funded by the  ANR-10-Blanc SVSE 3-009   HYDROPHOBIN  project, the  European Community 's  Seventh Framework Programme  [FP7/2007-2013] under Grant Agreement No:  260338  ALLFUN and the  French-Australian Science & Technology (FAST)   Program 2011. The 600 MHz spectrometer was funded by the  Région Ile de France  and the  Institut Pasteur. We thank  Jacques D'Alayer  ( Institut Pasteur ) for initial SELDI-TOF and for Nterminal sequencing experiments.
1a ) and for the three parameterizations B, C and D (Fig. 1b-d ). The most striking feature is that the patterns of azonal sea-level pressure simulated by CLIMBER are smoother than the NCEP ones due to the spatial resolution. The thermal effect (Fig. 1b ) allows to account for the main structures linked to the landsea temperature difference. Low pressure over North Pacific and North Atlantic and high pressure over the continental regions are clearly represented. However, their meridional extent is too large. This leads to a negative SLP anomaly over Greenland and a positive one over the Scandinavian and the Barents-Kara regions, in contradiction with NCEP reanalysis. Moreover, the amplitudes of the anomalies over the Northern Hemisphere are weaker than those of the NCEP database. This implies that the anti-cyclonic structure over the North American continent is almost absent.