dataesr/scientific-paragraphs-categorization huggingface dataset:
Tensions between large and low recoil in angular observables We discuss for the first time here a rather different type of tension, concerning the B → K * µ + µ -angular observables at large and low recoil. On the one hand, we observe that branching ratios exhibit the same discrepancy pattern between theory and experiment at low and large recoil 2. On the other hand, the current deviations at LHCb in P 5 require NP contributions with opposite sign in the two kinematic regions. Indeed, the pull between the SM value and the LHCb experimental measurement in P 5  [15, 19]  has the opposite sign (albeit the significance is only 1. 2σ) w. r. t. its large-recoil bins, in particular P 5  [4, 6]  and P 5  [6, 8]. This very slight tension is not there in the case of the Belle data where same-sign deviations are observed, even though the error bars are rather large in this case.
[1]. Indeed, according to the latter reference, this would amount to the inclusion of the data sets of 2017 and 2018 which are said to have the same statistical power as the combined data set of Run 1, 2015 and 2016. For each fit (corresponding to a given hypothesis and set of data), both the pull of the hypothesis w. r. t. the SM (Pull SM ) and the best-fit-point (b. f. p) are computed, which we plot as functions of either R K [1. 1, 6]  or Q 5  [1. 1,6]. Before discussing the results of our analysis, we first state our assumptions: We follow the same approach as in Refs. [3] [4] [5] [6] 14]. We consider a set of b → s observables measured by different experiments, we determine the experimental and theoretical correlation matrices between these observables assuming Gaussian distributions.
Acknowledgments This work received financial support from the grants  CICYT-FEDER-FPA 2014-55613-P  and FPA 2017-86989-P [JM, SDG, BC, MA], and from  Centro de Excelencia Severo Ochoa   SEV-2012-0234  [BC]; from the  EU   Horizon 2020 program  from the grants No. 690575 , No. 674896  and No. 692194  [SDG]. J. M. acknowledges financial support from the  Catalan ICREA Academia Program. The work of P. M. is supported by the  Beatriu de Pinós postdoctoral program of the Government of Catalonia's Secretariat for Universities and Research of the Ministry of Economy and Knowledge of Spain.
001) with modest effect sizes ranging from 0. 33 to 0. 52 (Supplementary Table  3 ). A notable pattern of connectivity was evident in these retinotopic regions of IPS: structural connectivity of VWFA was greatest in ventral and posterior subregion (i. e. , IPS1), with diminishing connectivity in more dorsal and anterior retinotopic subregions (i. e. , from IPS2-4). An additional aim of this analysis was to examine differences in the strength of structural connectivity between VWFA and language network nodes (i. e. , from Vogel et al. 27  and the "reading map" of NeuroSynth) relative to connectivity strength between VWFA and fronto-parietal attention network nodes (i. e. , from Vogel et al. 27  and the retinotopic map ROIs). Results from the paired-sample t-test showed that structural connectivity between VWFA and the fronto-parietal attention network overall was significantly greater than connectivity with the language network (t(312) = -30.
Discussion VWFA is a critical brain region for reading and is widely considered a key node of the brain's language network, however detailed whole-brain analysis of VWFA structural and functional circuitry using a large and high-quality dataset has been absent from the literature, precluding a more comprehensive understanding of its large-scale architecture. Converging results from probabilistic tractography and seed-based functional connectivity revealed that VWFA has moderate-to-strong connectivity with nodes of the brain's language network, particularly STS regions associated with phonological processing, as well as fronto-parietal attentional systems, particularly the IPS. The strength of structural and functional connectivity between VWFA and nodes of the attentional system was greater than those measured between the VWFA and the language network.
Methods Data. The Washington University Institutional Review Board (IRB # 201204036) approved all procedures for the Human Connectome Project (HCP). Informed consent was obtained from all participants and all open access data were deidentified. Participants. Participants consisted of 313 adults from the Human Connectome Project (HCP) obtained under the HCP 1200 data release (188 females; age range: 26-35 years old; details of demographic information and behavioral data can be found in Supplementary Tables  1  and 2 ). The participants were selected from 1206 subjects based on the following criteria: (1) age between 26 and 35 years old; (2) right-handed, defined as a handedness score >0; (3) completed all four runs of 3T resting-state fMRI (rs-fMRI) protocol; (4) averaged scan-to-scan head motion for rs-fMRI data is less than 0.
Total acquisition time for the 6 runs was approximately 1 hour. Minimally preprocessed Diffusion MRI data were obtained from the HCP  65. For each individual, 1200 frames were acquired using multiband, gradient-echo planar imaging with the following parameters: RT, 720 ms; echo time, 33. 1 ms; flip angle, 52°; field of view, 280 × 180 mm; matrix, 140 × 90; and voxel dimensions, 2 mm isotropic. During scanning, each individual was eye-fixated on a projected crosshair on the screen. A total of 4 15-minute runs of rs-fMRI were acquired in each participant. Minimally preprocessed resting-state fMRI data were obtained from the HCP  65. Regions of interest (ROIs). Consistent with previous studies  27  , the VWFA was defined as a 6 mm radius geodesic disc centered on the MNI coordinate (-45, -57, -12).
This seed location was used to facilitate comparisons to previous studies of VWFA connectivity. For all the coordinate-based ROIs, a disk with a diameter of 6 mm on the cortical surface map was transformed from volumetric space to surface space using the connectome workbench (https://www. humanconnectome. org/ software/get-connectome-workbench). Language network ROIs were constructed using coordinates previously published in a whole-brain study of VWFA connectivity  27. That study identified ROIs for left-hemisphere pars triangularis (BA 45) of inferior frontal gyrus (IFG), angular gyrus (AG), supramarginal gyrus (SMG), and inferior temporal gyrus (ITG). ROIs for these regions were constructed as 6 mm radius geodesic discs centered on the following MNI coordinates: IFG, (-53, 27, 16); AG, (-49, -57, 28), SMG, (-56, -43, 31), and ITG, (-61,-33,-15).
00402). We then used this population mean as the empirical null value for our statistical analysis, and tested the hypothesis whether vertex-based connectivity is significantly greater than this threshold. The rationale for controlling for age and gender in both structural and functional analyses is that both of these factors have been shown to influence connectivity patterns in large-scale brain network analyses  74, 75  and the resulting connectivity map was then thresholded at p < 10 -15 (FDR corrected) to illustrate patterns of structural connectivity. Surface-based resting-state functional connectivity (rsFC) for each grayordinate vertex was computed from the FIX-denoised grayordinate-space rs-fMRI data in each subject. For each cortical vertex, the correlation between its time course and the average VWFA time course was computed to produce an r-map, where the r value of each vertex represents the extent of its time-series correlation with VWFA.
We compared the model fit of the parametric linear model with the GAM on the same measures, and used Spearman correlations to examine the relationship between the predicted values and the actual values on each behavioral measure. Task fMRI data for word reading and visual attention. To investigate engagement and functional connectivity of the VWFA during reading and attention tasks, and address the final goal of this study, we analyzed two task fMRI datasets including word reading/rythming (https://openneuro. org/datasets/ds000003/ versions/00001) and visuospatial attention/Flanker tasks (https://openneuro. org/ datasets/ds000102/versions/00001) from OpenNeuro. We first examined task activation in both tasks within the VWFA, and then we used the generalized psychophysiological interaction (gPPI) model  80  to examine task-based functional connectivity, and employed the same VWFA seed and language and attention network targets described in the structural and intrinsic functional connectivity analyses from the HCP dataset for this analysis (details in the Supplementary Methods).
Acknowledgements This work was supported by the  National Institutes of Health  ( K01MH102428  to  D. A. A , and  HD094623 ,  HD059205 ,  MH084164  to V. M. ), the  Brain & Behavior Research Foundation  ( NARSAD Young Investigator Grant  to D. A. A) the  European Research Council  ( ERC Starting Grant  Agreement No. 757672 :  NeuroLang  to D. W. ), the  Institut National de Recherche en Informatique et en Automatique  ( INRIA associated team LargeBrainNets ), and the  France-Stanford Center Visiting Junior Scholar Fellowship  to L. C. Data were provided by the  Human Connectome Project ,  WU-Minn Consortium  (Principal Investigators:  David Van Essen  and  Kamil Ugurbil ;  1U54MH091657 ) funded by the 16  NIH Institutes  and Centers that support the  NIH Blueprint for Neuroscience Research ; and by the  McDonnell Center for Systems Neuroscience at Washington University.
primer design for CytB, ATP6, ND4 and CO1 mitochondrial genes. We performed BLAST alignments between each of the CytB, ND4, CO1 and ATP6 sequences of X. americanum obtained by He et al. 34  (accession number NC_005928) and the EST database from X. index on NemaBLAST (http://nematode. net/NN3_frontpage. cgi). We found putative partial sequences of the four homologous mitochondrial genes in X. index (contigs XI01293 for CytB, XI01255 for ATP6, XI01185 for ND4 and XI01295 for CO1). For each gene, an alignment was performed between the X. americanum sequence and each of these partial sequences using Clustal W in Mega software version 6. 0  35. Then, specific primers were designed from these alignments for each gene and used to amplify specific fragments from X. index (CytB-852 bp, ND4-644 bp, CO1-998 bp and ATP6-550 bp) (Table  S2 ).
Data analysis. For the mitochondrial genes, we computed the mean distances between sequences with Mega software version 6. 0 35 using the Kimura-2-parameters model and the maximum likelihood (ML) method to build phylogenetic trees  36, 37. We also completed the bootstrap values of the ML method by the Bayesian posterior probabilities (BayesPhylogenies software version 1. 1) based on the MCMC method  38. The sequence of X. americanum was included as an outgroup in our phylogenetic analysis  34. For the microsatellite data, we first analyzed the results with the GeneMarker program version 1. 75 (Applied Biosystems). Then, we used the POPULATION software version 1. 2. 31 (available at http://bioinformatics. org/populations/) to compute the distances between individuals by Cavalli-Sforza and Edwards distance (CSE)  39  and draw neighbor-joining (NJ) trees.
W1 comprised 14 samples with the same haplotype ranging from the island of Crete and Pecs in Hungary in its eastern geographic expansion to Northern California in its western expansion. By contrast, W2 comprised 12 samples located in three distant regions: the southern Iberian Peninsula (Spain and Portugal), Bordeaux (France) and Provence (France). The nine samples from NME showed five total haplotypes. NME revealed two specific haplotypes in the sample from Tabriz (Iran) and a single specific haplotype in each of the samples from Sharekord (Iran) and Alasehir (Turkey) (Figs  1  and 2A ). Subclades NME, IP and SSI, from the Central and Eastern Mediterranean, totaled eight haplotypes, while the widely spread subclade W totaled only two haplotypes (Figs  1  and 2A ). These data strongly suggest that the samples from the Central and Eastern Mediterranean possess a higher diversity than those from western countries.
the global picture of X. index diversity is an ongoing study. Although our study has benefited from a worldwide range of samples covering 16 countries over four continents, many other relevant areas need to be explored to confirm and refine the portrait of dissemination that we have drawn. The eastern Mediterranean area shows the highest diversity, but our few geographical sampling points provide only preliminary insight into this region. For instance, other countries such as Syria, Iraq, Armenia and Georgia will have to be considered. Future efforts will also aim to decipher the Phoenician heritage and its putative routes from Lebanon. We lack data from Northern Africa that could show whether a specific dissemination route by the Phoenicians and/or later by the Arabs exists in southern Mediterranean countries.
Acknowledgements We are particularly thankful to the numerous contributors that rendered this study possible by their nematode sampling and delivery. We acknowledge  John Philis  (Cyprus),  Mauro Di Vito ,  Nicola Greco and Alberto Troccoli (Italy) ,  Andy Walker (USA) ,  David Nevo  and  Mishael Mor  ( Israel and Cis-Jordan ),  Maria Arias ,  Antonio Bello ,  Lee Robertson and Pablo Castillo (Spain) ,  Carlos Gutierrez-Gutierrez  (Portugal),  Emmanuel Tzortzakakis  (Greece),  Sokhandan Bashir and Majid Olia (Iran) ,  Peter Nagy (Hungary) ,  Maria Moreno ,  Enrique Tapia-Vera  and  Pablo Meza (Chile), Marcelo Doucet (Argentine), Caroline Ojeil (Lebanon) ,  Galip Kaskavalci and Ibrahim Mistanoglu (Turkey) ,  Mohamed Youssef-Banora (Egypt) ,  Marion Claverie  and  Virginie Vigues  ( IFV France ),  Géraldine Uriel  ( CIVC France ),  Marc Chovelon  ( ITAB France ),  Sophie Vigneron  ( INRA Colmar ), for the samples of X.
With the simulation, we show how different galactic components contribute differently to the gravitational wave signal, due to their typical age and distance distributions. We find that the dominant LISA sources will be He-He systems and He-CO systems with important contributions from the thick disk and bulge but also a few systems in the stellar halo. The resulting sky map of the sources is different from previous models, with important consequences for the searches for electromagnetic counterparts and data analysis. We also emphasize that much of the science-enabling information regarding white dwarf binaries, such as the chirp mass and the sky localisation, becomes increasingly rich with long observations, including an extended mission up to 8 years.
FIRE Galaxy Model We use a subset of MW-like galaxies from the Feedback in Realistic Environment (FIRE;  Hopkins et al. 2014 ) project 1  , including m12i (a. k. a. the "Latte" simulation;  Wetzel et al. 2016, m12m and m12f (Garrison-Kimmel et al. 2017 ) simulations. These simulations are based on the improved "FIRE-2" version of the code from  Hopkins et al. (2018,  for details, see Section 2 therein) and ran with the code GIZMO (Hopkins 2015) 2. GIZMO solves the equations of hydrodynamics using the mesh-free Lagrangian Godunov "MFM" method. The analysis of the simulations is done with the publicly available Python package gizmo_analysis. More specifically, these simulations have an initial gas particle mass of about 7070 M and for the gas, both the hydrodynamic and gravitational (force softening) resolutions are fully adaptive down to 1 pc.
Modeling the gravitational wave emission To estimate the capability of LISA to detect and characterize white dwarf binaries in the galaxy models, we simulate the LISA data by co-adding the gravitational waveforms from all binaries with signals in the measurement band using the fast waveform generator in  Cornish & Littenberg (2007). Without replicating the derivation, it is valuable to point out here that the dimensionless GW strain from a compact binary at a distance r is given by h = 2(4π) 2 f 2/3 GW G 5/3 c 4 M 5/3 c r. ( 4 ) The measurement of the GW strain and frequency alone are insufficient to determine the chirp mass of the binary, which is degenerate with the distance. The chirp mass (and therefore distance) can be determined for galactic binaries in the LISA band, having wide orbital separations and orbital velocities c, to leading order in the frequency evolution M c = c 3 G 5 96 π -8/3 f -11/3 GW f GW 3/5 (5) assuming that other contributions to the orbital period evolution (e.
g. mass transfer, tides, etc. ) are sub-dominant effects. Note that f GW is a difficult parameter for LISA to constrain, so chirp mass measurements are only possible for "outliers" of the total population, requiring high signal-tonoise (S/N), comparatively large f , and/or long integration times for the LISA observations. Fortunately, due to the large number of detectable binaries, even the tails of the source distribution are well populated. Our simulated LISA response to the galaxy models use spacecraft noise levels and orbits consistent with those in the LISA mission proposal (Amaro-Seoane et al. 2017). De-termining which of the simulated binaries are individually resolvable is challenging in the regime where the confusion noise dominates the data stream. From the full input binary population, the detectable sources are identified by using an iterative process that utilizes a median smoothing of the power spectrum to estimate the noise level (dominated by source confusion around 1 mHz), regresses binaries from the data with signal-to-noise ratios S/N> 7 as detected sources, and cycles until the detection "catalog" converges  (Timpano et al.
g. , sky localisation) and will benefit from an extended mission (e. g. , measurement of the chirp mass). Within the first 2 years, less than 3 per cent of the systems will have a measured chirp mass, which increases up to 8 per cent after 4 years and 15 per cent after 8 years. Towards the end, LISA will measure the lowest chirp masses, which are likely to have the brightest EM counterparts. These results highlight the importance of refined modeling of complete galactic populations. It is a stepping stone for more thorough analysis of different binary population models, especially in combination with constrains from current and future EM surveys. Our new model also has implications for the preparation of LISA data analysis, including the unresolved gravitational wave background.
ACKNOWLEDGEMENTS The authors would like to thank  Katie Breivik ,  Silvia Toonen  and  Valeriya Korol  for many exchanges on formation channels of white dwarf binaries and  Nelson Christensen  and  Neil Cornish  for many insights on GW detections. Support for AL was provided by the  Observatoire de la Côte d'Azur  and  NASA  ATP Grant # NNX14AH35G , and  NSF   Collaborative Research Grant  # 1715847  and  CAREER  grant # 1455342. S. B. is supported by the  NSF   Graduate Research Fellowship , grant # DGE1745303. TL acknowledges the support of  NASA  grant # NNH15ZDA001N  and the  NASA LISA Study Office. Support for SGK was provided by  NSF   Collaborative Research Grant  # 1715847  and  CAREER  grant # 1455342 , and  NASA  grants  NNX15AT06G ,  JPL 1589742 , and  17-ATP17-0214. This work was supported by the  National Science Foundation  through grant  PHY 17-148958.
, 2012; Tack and Laine, 2014). Hence, remarkably little is understood of this critical life-history stage. To understand why outcrossing is broadly maintained in pathogens despite the costs, here we investigate the ecological and genetic consequences of putative outcrossing in a large natural pathogen metapopulation. Our analysis is based on data collected from Podosphaera plantaginis, a specialist powdery mildew fungus naturally infecting Plantago lanceolata. The visually conspicuous symptoms caused by P. plantaginis enable accurate tracking of infection in the wild. Long-term epidemiological data across approximately 4000 local plant populations in the A ˚land Islands, southwest of Finland, have demonstrated this pathogen to persist as a highly dynamic metapopulation with frequent extinctions and (re)colonizations of local populations  (Jousimo et al.
Host population size The total coverage (in m 2 ) of the host in the local population has been visually assessed each year simultaneously when the pathogen infection data were collected. The criteria for describing host populations is provided in detail in  Ojanen et al. (2013). In brief, suitable host populations have been identified by the presence of dense clusters of P. lanceolata, which in A ˚land occur on dry meadows and pastures. These occur as well-defined, discrete patches across the landscape.
Spatial bayesian modelling of the effect of coinfection on pathogen overwintering and strain diversity We modeled the following events of interest: the effect of the number of coinfections on persistence of infection from one year to the next, and the number of new, previously unidentified, MLGs in a pathogen population that survived from one year to the next. In addition, we assessed the drivers of the presence/absence of resting spores and coinfections among the infected populations. Our models were fitted to data from years 2012-2015. To control for the possible effect that population size, connectivity and diversity could have on the results, our models included the following predictors (described above in detail): Pathogen population size, host population size, number of distinct pathogen strains, and pathogen connectivity S p i.
Sampling model The data considered here are the genotypes of sampled individuals. For a single patch i, if the mildew is present in the patch, up to 10 infected plants are sampled and the MLGs infecting them determined. The exact number of sampled plants depends on the number available in the patch; as many infected plants are sampled as possible until 10 is reached. The observed genotype for the j th sampled plant (note that here j goes from 1 to 10, not from 1 to N i ) in patch i is G i;j ¼ k if Z i;j;k >0 and Z i;j;l <0 for all l 6 ¼ k 0 Z i;j;l >0 for multiple l.
& In words, genotype G i;j is the MLG index, if only one MLG is infecting the plant, and 0 if there is co-infection by multiple MLGs. Note that the genotypes of the MLGs in a co-infection are not observed. We also use data on pathogen population size for patch i, AA i , measured as a categorical variable (1 = less than 10 plants infected, 2 = 10-99 plants infected, 3 = 100-999 or 4 = 1000 or more) and infection prevalence for patch i, RA i , (categories of the proportion of plants infected: 1 = 0-0. 25, 2 = 0. 25-0. 5, 3 = 0. 5-0. 75 and 4 = 0. 75-1).
Acknowledgements We would like to acknowledge the numerous students who have been involved in the survey and sampling of powdery mildew populations in the A ˚land Islands in 2012-15. K Raveala  supervised the field surveys. B McDonald  and  T Giraud  provided comments on an earlier version of the manuscript. Comments from  K King ,  D Weigel ,  E Decaestecker  and an anonymous reviewer during the review process helped significantly improve the manuscript. The DNA extractions and SNP genotyping were carried out at  Institute of Biotechnology and Finnish Institute of Molecular Medicine at University of Helsinki , respectively. This work was funded by grants from the  Academy of Finland  ( 296686 ),.
150 days). In this study, eye lens weights of 22 captive-born specimens with known age will provide the first data linking age and eye lens growth in a small arboreal Neotropical rodent. Here we are interested in the reproduction and in ontogenetic allometry, ie the description of differential growth in individual ontogeny  (Gould, 1966; D'Andrea et al. , 1996)  of a neotropical Cricetidae. By rearing small arboreal mice in captivity, we aim at addressing changes of one part to another during growth, through measurements taken in six metric features at different ages after birth. In early life, growth in most kinds of mammals is continuous, but some structures cease growth sooner than others, hence not all morphological features are useful for aging individuals in a population. Growth rates of mammals are most commonly described by the sigmoidal function of the Gompertz model, although other models sometimes perform equally well  (Zullinger et al.