dataesr/scientific-paragraphs-categorization huggingface dataset:
In the case of polarization data with several detectors solved simultaneously, the polarization equation is inverted on a per-pixel basis (see Sect. 4. 2). For each sky map, a hit count map is computed (number of samples per pixel; one sample has a duration of 5. 544 ms). We provide maps from which the zodiacal light component, which varies in time, has been removed, based on templates fitted on the survey-difference maps (see Sect. 5. 4). These templates are systematically subtracted from the data of each bolometer prior to the mapmaking. Unlike in the 2013 release, the far sidelobes (FSL) are not removed from the maps. At most, this leaves residuals of order 0. 5-1. 5 µK in the 100-353 GHz maps, with uncertainties on the residuals of roughly 100 % (Planck Collaboration VII 2015). At higher frequencies, Galactic pick-up from the FSL produces significant residuals in about half of the detectors of the order of 0.
Mapmaking method In the same way as in Planck Collaboration VIII (2014), we use a destriping algorithm to deal with the HFI low-frequency noise. In this approach, the noise in a ring r is represented by an offset, denoted o r , and a white noise part n, which is uncorrelated with the low-frequency noise. For a given bolometer, we can write Eq. (  6 ) as: P t = G × (A tp T p + t orb ) + Γ tr o r + n t , (12) where Γ tr is the ring-pointing matrix (which associates the data sample t with the ring number r). The unknowns are the gain G, the offsets for each ring o r , and the sky signal for each pixel T p = (I p , Q p , U p ). As there is a degeneracy between the average of the offsets and the zero level of the maps, we impose the constraint o r = 0. The absolute zero level of the maps is determined as described in Sect.
4. 4. For the production of the maps for the 2015 HFI data release, we first build the rings for all detectors. We apply the following frequency-dependent processing to these compressed data sets. For CMB frequencies (100, 143, and 217 GHz) channels: 1. we estimate a first approximation of the orbital dipole gain together with the offsets and the amplitude of the longtime-constant residuals for each bolometer independently, neglecting the polarization signal; 2. we then derive the gains and offsets for a fixed amplitude of the long-time-constant residuals using the multi-bolometer algorithm. At 353 GHz, the long-time-constant residuals are more difficult to constrain. They are driven more by Galactic emission drifts than by the dipole, which dominates at lower frequencies, so that the model described in Sect.
For polarization, we invert the 3 × 3 system derived from Eq. (  12 ) for each pixel independently with a criterion that the condition number be lower than 10 3 : T = A T N -1 A -1 A T N -1 d , (13) where d are the calibrated, cleaned ring data (offsets and orbital dipole removed) d = (P -Γ o) /Gt orb. Note that we use HEALPix (not IAU) conventions for the sign and normalization of the Q and U Stokes parameters. For destriping, we use the same tool as before, polkapix, which was thoroughly validated in  Tristram et al. (2011). Maps are built by simple co-addition in each pixel of the destriped, calibrated, and time-varying component-subtracted signal. We subtract the CMB dipole as measured by Planck (see Sect. 5. 1). We introduced the following modifications with respect to Planck Collaboration VIII (2014): we include polarization in the destriping for the channels that include PSBs; we enlarged the masked fraction of the sky, from 10 to 15 %, based on Galactic emission, to avoid signal gradients leaking into offsets; to improve the offset accuracy, we compute one set of offsets combining all detectors, using full-mission data, and use them for all derived maps involving these detectors.
Planck's scanning strategy samples almost all the sky pixels every six months, with alternating scan directions in successive 6-month periods. The full cold HFI mission encompasses five surveys, each covering a large fraction of the sky. Surveys 1-2 and 3-4 are paired to produce Year 1 and Year 2 maps (see Table  A. 2). Maps are produced for the full-mission dataset together with the survey, year, and half-mission maps. With each map is associated a hit-count map and variance maps (II; and QQ, UU, IQ, IU, and QU when polarization is reconstructed). Overall, a total of about 6500 sky maps have been produced. We have used this data set to evaluate the performance of the photometric calibration by examining difference maps (see Sect. 6. 2).
Zero levels Planck-HFI cannot measure the absolute sky background. The mapmaking procedure does not change the mean value of the input TOI. We therefore adjust the monopole on the maps a posteriori, in a similar manner to the method used in Planck Collaboration VIII (2014) that relies on external datasets. To achieve this, we need to take into account two major components of the monopole: 1. Galactic dust emission: we estimate the brightness in the HFI single-detector maps that corresponds to zero gas columndensity (i. e. , zero Galactic dust emission). To do so, we use the H i column density, which is assumed to be a reliable tracer of the Galactic gas column-density in very diffuse areas (see Planck Collaboration VIII 2014, Sect. 5. 1). The offsets derived are then subtracted from each detector's data in the processing.
Subset map cross-covariance When mapping subsets of the available data (selecting detectors and/or time spans) we have a choice between solving for independent baseline offsets for the subset in question or reusing full-mission, full-frequency baselines (as in the 2015 HFI map release). Full-mission baselines are more accurate, leaving less large-scale noise in the maps, but introduce noise correlation between detector-set maps. We can measure the resulting bias in cross-spectra through noise simulations. Comparison between noise spectra from a Monte Carlo analysis at 100 GHz using both methods of destriping is shown in Fig. 16  for the case of detector sets. Noise spectra for each detector set show more large-scale power when using independent baselines than in the full mission case. On the other hand, the cross-power spectrum is biased by up to a few times 10 -3 µK 2 below = 10; the same is true for the half-mission subset.
Intra-frequency accuracy For polarization reconstruction with Planck-HFI data, we have to combine data from several detectors. Any relative calibration error will induce an intensity-to-polarization leakage (see Sect. 7. 3). For the CMB channels, we have assessed the relative    16. Average EE spectra obtained from 100 simulations of detector-set noise maps. The latter have been produced using either the full-mission baseline destriping (MBD) or the destriping run on each subset independently (run baseline destriping or RBD). Both the increased low-noise in the RBD case and the increased cross-spectrum noise bias in the MBD case are apparent. calibration accuracy for each detector at a given frequency using two complementary methods. As in section 6. 2 (see figure  14 ) of Planck Collaboration VIII (2014), we derive relative inter-calibration factors for each detector (for 100 to 353 GHz), rescaling their cross-pseudo-power spectra, estimated over 40 % of the sky (30 % for 353 GHz) in the range 25-300, which encompasses the first acoustic peak.
Bandpass mismatch (BPM) Each emission component k (where k = CMB, dust, synchrotron, etc. ) is integrated over the bandpass of the detector according to a given spectrum f k (ν). Since the polarized HFI channels are calibrated using the CMB orbital dipole, we define the transmission coefficients C b k = f k (ν)H b ν dν f CMB (ν)H b ν dν (17) ≡ C k (1 + b BP,k ) , where   16 ). Then, ordering all the data samples, m b t , for a bolometer observing a position p on the sky into a single vector D b , defining A to be the pointing matrix in temperature and polarization, and n the noise vector, Eq. (  16 ) reads D b = k C k A           I k p Q k p U k p           + k C k b BP,k A           I k p Q k p U k p           + n. ( 18 ) Using all bolometers b within a channel, the mapmaking procedure solves for the total signal Stokes parameters (I tot p , Q tot p , U tot p ) in pixel p, formally computing,          I tot p Q tot p U tot p          = A T N -1 A -1 A T N -1 D , (19) which becomes          I tot p Q tot p U tot p          = k C k           I k p Q k p U k p           + k C k N b -1 b=0 b BP,k Γ b p           I k p Q k p U k p           , (20) where Γ b p ≡ A T N -1 A -1 A T N -1 ∆ b A.
Acknowledgements. The Planck Collaboration acknowledges the support of:  ESA ;  CNES  and  CNRS/INSU-IN2P3-INP (France) ;  ASI ,  CNR , and  INAF (Italy) ;  NASA and DoE (USA) ;  STFC  and  UKSA (UK) ;  CSIC ,  MINECO , JA, and  RES (Spain) ;  Tekes, AoF , and  CSC (Finland) ;  DLR  and  MPG.
Data description Data were available for 751 pedigree-recorded Scottish Blackface lambs born between 2001 and 2003 from a flock that originated from previously selected fat and lean lines, established in 1988  [20]. In vivo carcass composition measurements (on 600 lambs) were obtained once a year, over 3 days at an average age of 24 weeks using CT. Cross-sectional scans were taken at the ischium (ISC), the 5th lumbar vertebra (LV5) and the 8th thoracic vertebra (TV8), and from each scan image, the areas and image densities were obtained for fat, muscle and bone components of the carcass. Live weight (LW) was also recorded at the time of the CT measurements. Prediction equations for total weight of each tissue were obtained from a calibration study [Scotland's Rural College (SRUC) formerly known as Scottish Agricultural College (SAC), unpublished] on unrelated Scottish Blackface lambs of the same age, and are as follows: Fat These prediction equations were used to calculate the following traits: predicted fat weight, predicted muscle weight, predicted bone weight, predicted total carcass weight, fat proportion, muscle proportion, bone proportion, fat, muscle and bone areas at the cross sectional scan of the ischium (mm 2 ) and their densities (see Karamichou et al.
Another study on signatures of selections using the Sheep HapMap dataset (including 71 breeds) and statistical methods that took the hierarchical structure of sheep populations into account, exploited LD information and focused on the age of the selection signatures to identify a region on OAR6 that contains two genes, ABCG2 and NCAPG which are within our region of interest  [34]. The selection signature in this region of OAR6 was detected for Central European, Italian and South West European sheep breeds, but they did not include Blackface sheep. Using an F ST approach, Gutierrez-Gil et al. [35]  compared five sheep dairy breeds with five non-dairy breeds from the Sheep HapMap data and identified a selection signature on OAR6 that encompasses the ABCG2/SPP1 genes in the region of interest.
[70]  suggested that its partial dysfunction led to congenital vertebral malformations in both humans and rats; furthermore, Sparrow et al. [71]  reported that a mutation in this gene was the likely cause of spondylocostal dysostosis in some human families  [71] ; (4) KIF22 for which mutations have been linked to bone diseases in humans  [72, 73] ; (5) IL4R is a candidate gene because of its known role in cartilage homeostasis and osteoarthritis in humans  [74] [75] [76] [77] ; and (6) IL21R, which may have biological functions that are relevant to bone metabolism in humans  [78] [79] [80]. On OAR1, the only candidate gene that we could identify was KAT2B which is involved in cell growth and differentiation. We analysed our data with two different detection approaches (i. e. single SNP association versus RHM) that both identified many relevant chromosomal regions.
Screening Beef Samples for stx1, stx2, eae, and the Top Seven EHEC Serogroups As recommended in the ISO/TS 13136 (EU) and MLG5B. 05 (US) reference methods, the 180 stx and eae positive samples were tested for the top seven EHEC serogroups. Among these, 115 samples were positive for at least one of the top seven US regulated EHEC serogroups and 99 were positive for at least one of the top five EHEC serogroups screened by the European ISO/TS 13136 method (data not shown). The most frequently found serogroup was O103 (n = 71), followed by O26 (n = 45), O121 (n = 38), O157 (n = 18), O45 (n = 14), O145 (n = 6), and O111 (n = 1). Interestingly, 30 samples were positive for 2 serogroups, 11 were positive for 3 serogroups, and 7 for more than 4 serogroups. In final, 41. 74% (48/115) of the beef samples tested positive for more than one O-group marker.
Screening Beef Samples for stx1, stx2, eae, and the eae Subtypes The 180 stx and eae positive beef samples were further tested for the eae subtypes gamma, beta, epsilon and theta, which are associated with one or more of the top seven EHEC serogroups. Among these 180 stx and eae positive samples, 135 tested positive for at least one of the four eae-subtypes: gamma, beta, epsilon and theta (data not shown). The most frequently detected eaesubtypes were eae-beta (n =94) and eae-theta (n = 65), followed by eae-epsilon (n = 15), and eae-gamma (n = 5). Trying to correlate the eae subtype with the serogroup, we identified 51 beef samples for which at least one serogroup was associated with the corresponding related eae-subtype. Among these 51 samples, 6 were positive with multiple serogroups.
Image sequences were pre-filtered with a LoG filter (MexicanHat) as described  52  to improve signal-to-noise ratio before tracking trajectories. A step-fitting algorithm  52  implemented in MatLab (The MathWorks) was used for step-finding and step-size measurements. The mean run length was determined by averaging pooled data. The run length distribution was also analysed by nonlinear least-squares fitting of cumulative distribution from X 0 to infinity to 1 À exp[(X 0 À X)/l] as described by Thorn et al. 53. The data obtained was fitted by applying the normalMixEM procedure in the mixtools package in R  54. The mixtools package is one of several available in R to fit mixture distributions or to solve model-based clustering. The procedure was applied with its default values to the step-finding data obtained by the step-finding algorithms  51.
To minimize under or over-fitting the mixture distribution, the number of components to fit each data set was selected by cross-validation (Supplementary Fig. 5 ) of corresponding data set. Crystallization and X-ray data collection. Crystals of Myo10 MD, Myo10 MD-2IQ, Myo10 IQ3-SAH-CC and myosin Vc MD in the pre-powerstroke state were obtained using the hanging-drop vapour-diffusion method. Spontaneous nucleation of myosin X motors occurred at 277 K and at 290 K for Myo10 IQ3-SAH-CC with equal amounts of reservoir solution and stock solution of the protein. The best crystals were obtained using seeding approaches. Before freezing, crystals were transferred stepwise into a final stabilization solution containing ethylene glycol or glycerol as cryo-protectant. X-ray data sets were collected at 100 K at the SOLEIL Proxima-1 beamline.
5, 80 mM NaCl, 1 mM DTT, 1 mM NaN 3 , 2 mM Mg 2 þ ADP and 2 mM vanadate. Crystals of Myo10 IQ3-SAH-CC at 12 mg ml À 1 were obtained with reservoir solution containing 14% PEG 3350, 100 mM ammonium citrate pH 7. 0, 2% dioxane. All the images of the data set were processed and integrated with the program XDS  55. A single-wavelength anomalous diffraction data set of a seleno-methionine substituted IQ3-SAH-CC was collected and phased using the SHELX program suite. The seleno-methionine IQ3-SAH-CC was used to solve the native data set diffracting up to 3. 5 Å in P4 3 2 1 2 1 space group by molecular replacement. The crystals of the motor domains were solved by molecular replacement using the program Phaser  56. Refinement was performed using Coot  57  and BUSTER or PHENIX  58. The final statistics are presented in Supplementary Table  2  and the electron density map in Supplementary Fig.
13. Molecular dynamics simulations of the SAH dimer. We modelled the IQ3-SAH-CC/CaM complex by adding two IQ3 regions with CaM bound  59  to the crystal structure of the SAH-CC dimer (residues 812-926). This model was used to perform molecular dynamics simulations with an implicit-solvent model. The simulations use the CHARMM22 all-atoms force field  60  along with the fast analytical continuous treatment of solvation (FACTS) implicit solvent model  61  , as implemented in the CHARMM program (version c38b1). FACTS parameters were Tfps ¼ 3, dielectric constant ¼ 1. 0, k ¼ 4. 0 and g ¼ 0. 015. An implicit solvent was preferred over explicit solvation because both the size and the extended shape of the SAH dimer would have made the MD simulations unreasonably expensive. Moreover, the use of the FACTS implicit solvent model in combination with the CHARMM force-field had been previously validated for the study of SAH sequences by Wolny et al.
Refinement and reconstruction of cryo-EM data. Movie frames were aligned and summed using the dosef_driftcorr motion correction software package  63  , with minor modifications to permit CPU (central processing unit)-only computations in the absence of a suitable graphics card. Defocuses and astigmatism parameters for the micrographs were estimated using the CTFFIND3 program  64  , and were subsequently used to phase-flip the micrographs before initial extraction of filament images. Image processing and three-dimensional reconstruction methods closely follow the protocol described in (Kang et al. )  65. Briefly, initial refinements were performed using the SPARX implementation of the iterative helical real space reconstruction (IHRSR) method  66  , using a low-pass filtered map of the actomyosin complex as an initial reference (40 Å resolution).
Molecular docking and model generation with cryo-EM data. A homology model of myosin X MD in the rigor state was generated by SwissPDBViewer (http://spdbv. vital-it. ch/) using the myosin Va rigor structure (1OE9; ref. 40) for a template, but replacing the N-terminal SH3 domain by that of myosin X (from the X-ray 1. 8 Å resolution pre-powerstroke MD structure described here). Indeed this myosin X pre-powerstroke structure has revealed that this domain is quite different from that of myosin V (Fig. 3c ). Atomic coordinates of actin (3j8a; ref. 68)  and Myo10 MD were fit into density maps using local refinements based on cross-correlation with synthetic models X-ray crystal structures rendered at 9 Å resolution (backbone atoms only), via the 'Fit in Map' function of the Chimera package  69  from the Computer Graphics Laboratory, University of California, San Francisco.
For fitting of the converter domain into our cryo-EM maps, we switched to a more comprehensive rigid-body search method to assess the robustness of the resulting solutions. From each independently-refined half-data set reconstruction as well as the one derived from all the data, we excised a small volume (32 Â 32 Â 32) containing the converter domain, and then used the Situs package  70  to perform an exhaustive six-dimensional search using our coordinates of the myosin X pre-powerstroke converter (residues 674-738) as a search model. For search parameters, we employed 5°angular increments and activated the laplacian filter option; 9 and 10 Å resolution cutoffs were applied, respectively, for the pair of half-data refinements as well as the full data set one. The resulting estimates of the converter position/orientation differ from one other; the extent of the differences reflects residual noise present in the reconstructions (noting that the residual noise in each half-data set refinement is essentially independent of the other half-data set refinement).
The third IQ was modelled using the coordinates of the 2IQ 1-2 /CaM myosin V complex (2IX7 (ref. 60)) on both the pre-powerstroke and rigor heads. The structure for the missing residues between these myosin X MD3IQ rigor and pre-powerstroke constructs was obtained using the coordinates of our IQ3-SAH-CC structure solved in this study after relaxation dynamics by molecular dynamics (see Fig. 5a right ). All molecular graphics images were rendered by UCSF Chimera or were generated with Pymol (http://www. pymol. org/). Data availability. The atomic coordinates and structure factors have been deposited in the Protein Data Bank, www. pdb. org, with accession numbers 5I0H (Myo10-MD-PPS), 5I0I (Myo10-MD2IQ-PPS), 5HMO (Myo10-SAH-CC), 5HMP (Myo5c-MD-PPS). Model and maps for the cryo-EM reconstruction of rigor Myo10 have been deposited in the Protein Data Bank and the EMDB with accession codes 5KG8 and EMD-8244, respectively.
Acknowledgements We thank beamline scientists of PX1 (SOLEIL synchrotron) for excellent support during data collection. We gratefully acknowledge the  Yale CCMI Cryo-EM  facility for support and maintenance of cryo-EM equipment. We thank staff for maintenance of the  Yale Cryo-EM facility and High-Performance Computing facility. A. H. was supported by grants from the  CNRS ,  ANR BLAN10  and  ANR-13-BSV8-0019-01 , Ligue contre le cancer and  ARC. H. L. S. was supported by  NIH  grants  DC009100  and  HL110869. C. V. S was supported by  NIH  grant  RO1-GM11053001. The AH team is part of  Labex CelTisPhyBio   11-LBX-0038  and  IDEX PSL  ( ANR-10-IDEX-0001-02-PSL ).
Data samples The results are based on proton-proton collision data collected with the ATLAS experiment at the LHC at a centre-of-mass energy of √ s = 8 TeV in 2012. Only events collected under stable beam conditions with all relevant detector subsystems operational are used. Events are selected using singlelepton triggers with p T thresholds of 24 or 60 GeV for electrons and 24 or 36 GeV for muons. The triggers with the lower p T threshold include isolation requirements on the candidate lepton in order to reduce the trigger rate to an acceptable level. The total integrated luminosity available for the analyses is 20. 3 fb -1.
0  [32] [33] [34] [35] [36] [37]. The quoted uncertainty includes the scale uncertainty and the uncertainties from PDF and α S choices. t tV: The samples of t tV with up to one additional parton were generated with the MadGraph v5 generator (v1. 3. 33) [38]  with the CTEQ6L1 PDF set. Pythia 6. 426 with the AUET2B tune  [39]  was used for showering. The top quark production and decay was performed in MadGraph and t t + Z/γ * interference was included. The t tV samples are normalised to the NLO cross-section predictions  [40, 41]. t t H: The t tH process was simulated using NLO matrix elements for pp → t tH provided by the HELAC-Oneloop package  [42] , interfaced to Pythia 8. 175  [43]  through PowhegBox  [44] , also known as the Powhel approach  [45]. The matrix-element calculation was performed using the CT10 PDF set and the parton shower used the AU2CT10 tune  [46].
The t tH sample was interfaced to Photos++. All samples were simulated taking into account the effects of multiple pp interactions based on the pile-up conditions in the 2012 data. The pile-up interactions are modelled by overlaying simulated hits from events with exactly one inelastic (signal) collision per bunch crossing with hits from minimum-bias events that are produced with Pythia 8. 160 using the A2M tune  [46]  and the MSTW2008 LO PDF  [67]. Finally the samples were processed through a simulation  [68]  of the detector geometry and response using Geant4  [69]. All simulated samples were processed through the same reconstruction software as the data. Simulated events are corrected so that the object identification efficiencies, energy scales and energy resolutions match those determined in data control samples.