dataesr/scientific-paragraphs-categorization huggingface dataset:
1, 0. 5 and 1 s. To demonstrate the distance-dependence, we estimate the s 1 (equation 2) within each moving distance window of 0-30 km, 10-40 km, 20-50 km, and so on up to 200 km. For brevity, we present the results only for the KiK-net dataset. Figure  4  shows that the period-and distance-dependence of S-wave radiation pattern is strongly dependent on the style-of-faulting of the events. Firstly, for strike-slip events, the s 1 values at T = 0. 5 s are significant (95% confidence intervals) up to 100 km. For long period PSAs (T = 1 s), which we consider comparable to low frequency S-wave amplitudes, we observe that significant s 1 estimates persist up to 200 km. On the other hand, normal and oblique-slip events show a much earlier decay in correlation starting at R ∼ 20-40 km, depending on the frequency.
Reverse-slip events are peculiar in a few aspects. For instance, despite being the largest contributors to the KiK-net dataset in terms of number of events and records, they show a weaker correlation (smaller s 1 in Fig. 3 ) values, and lesser reduction in aleatory variability, than other style-of-faulting events. At the same time, the distance-dependent decay (for T = 1 s in Fig. 4 ) starts to be significant at around 100 km -much later than normal and oblique-slip events. Empirical  43, 44  and simulation  45  based studies showed that the hanging-wall effects of reverse-slip events may persist up to 100 km, and are sensitive to magnitude, dip, and depth to top-of-rupture plane. Observations from M7. 6 Chi-Chi (Taiwan), M6. 7, Northridge (New-Zealand), M6. 3, L' Aquila (Italy), and M7. 9, Wenchuan (China) support these simulations  45  ; wherein, equidistant ground-shaking intensities (PSAs) on the hanging-wall could be three times larger than those on the foot-wall side of the rupture.
Anisotropic Ground-shaking predictions For a new event, given its rupture focal-mechanism and regional S-wave crustal velocity structure (or take-off angle table)  38  , the theoretical S-wave radiation amplitudes (AS) in its vicinity can be estimated, which can then be translated into anisotropic ground-shaking adjustments compatible with the isotropic ground-shaking prediction models  33, 34. We use the period-, distance-and style-of-faulting dependent coefficients s 0 and s 1 derived from the Japanese KiK-net dataset  3  (shown in Fig. 4 ) to predict the radiation patterns of four well-recorded events available in the dataset (Table  1 ). Instead of comparing observed and predicted PSAs for each event at T = 0. 1, 0. 5 and 1 s, Fig. 5  compares the predicted anisotropic amplification/attenuation (equation 2 with coefficients in Fig.
Implications in seismic Ground-shaking studies S-wave radiation pattern is an extensively studied phenomenon in seismology. They have been repeatedly observed and quantified from observed S-wave amplitudes of a few well-recorded strike-slip events in the Fourier domain, or sometimes Peak Ground Acceleration and Velocity. However, within engineering seismology, they have never been quantified over large datasets of PSAs (Pseudo-Spectral Accelerations) from several normal, oblique, reverse, and strike-slip events -PSAs are the standard ground-shaking intensity measures applied in structural design codes, probabilistic seismic hazard and risk assessments, and ShakeMaps. The reasons for this include the limited spatial density of strong-motion networks to observe the patterns for any single event, and the dominance of other ground-shaking attenuation processes.
Our empirical radiation pattern models are simple and practical. The coefficients s 0 and s 1 of equation (  2 ), derived from the KiK-net and NGA-West2 datasets, can be applied to the respective isotropic ground-shaking prediction models  33, 34. These adjustment factors have an impact on ground-shaking maps 1 ; with at least ±40% change in the isodistant predictions, depending on the site azimuth and distance relative to the event epicentre. In complement, the prediction variance φ 0 is reduced by up to 8% for the KiK-net dataset based ground-shaking model. Regional dependence  39, 46, 47  of adjustment factors (s 0 and s 1 of equation  2 ) is an issue worth investigating, given that we do observe some event-to-event (or regional) variability -but that would require a more complex treatment of crustal heterogeneities.
Our method, producing average adjustment factors, aims to balance model complexity and ease of applicability, so that anisotropic adjustment factors can be estimated for any empirical ground-shaking prediction model. Our study intends a new approach to the inclusion of style-of-faulting effects in empirical ground-shaking prediction models. For instance, several ground-shaking models  4, 5  derived from the NGA-West2 2 and RESORCE 31 datasets (used in USGS  48  and SHARE  49  hazard maps) propose average isotropic, style-of-faulting and frequency-dependent adjustment factors. These factors were shown to vary considerably from one model to another and were not representative of any clear physical phenomenon  50. While some studies could not constrain these adjustment factors  29, 51  , others could only derive them for large earthquakes  4.
Methods The NGA-West2 2 , KiK-net datasets  3  , and take-off angle tables  38  used in this study are publicly available. As an electronic supplement, we provide an extract of the KiK-net dataset with all relevant data and model coefficients of equation (  2 ). The peer-reviewed and published isotropic ground-shaking models  33, 34  were developed using either the R-software  60  or a multi-step mixed-effects regression procedure described in the relevant publications  34. The equations used to calculate the far-field S-wave radiation coefficients were developed by Aki and Richards 6.
Acknowledgements We thank the two anonymous reviewers whose constructive criticism helped us improve the manuscript considerably. We would like to thank  Eleonora Rivalta  for her valuable insights. We extend our thanks to our colleagues at section 2. 6: Seismic Hazard and Risk  Dynamics of GeoForschungsZentrum Potsdam , for the regular discussions and feedbacks. This research is funded by the  SIGMA2 consortium (EDF ,  CEA ,  PG&E ,  SwissNuclear ,  Areva ,  CEZ ,  CRIEPI)  under grant  -2017-2021  (http://www. sigma-2. net/).
Population and sample The study population (n=904) consisted of 224 residents and 680 patients from the community pharmacy and the LTCF, respectively. In the LTCF, residents were eligible if they were aged 65 or older and living in the facility until 2017. In the community pharmacy, the study population was calculated based on the minimum legal number of inhabitants per pharmacy (3,500 inhabitants) and on the percentage of elderly living in the district of Cascais in 2016 (19. 6%). Moreover, individuals were included if they were aged 65 or older and had their medication history available in the pharmacy database in 2017. We excluded patients if their records were out of date, ie, if there were no sales in 2017.
Data extraction Data were extracted for sociodemographics (age and sex), clinical features (number of comorbidities, previous history of CVD, and the presence of dementia), and drugrelated characteristics (number of medications and presence of polypharmacy). The previous history of CVD and the presence of dementia were defined according to medication used to treat CVD and dementia, respectively, as a proxy. Polypharmacy was defined as taking five or more medications. 15 Information on comorbidities was validated by one member of the research team (JPA) and then confirmed by a physician (LHC). For the records with medical diagnosis, the validation process was performed by comparing the available medical diagnoses with the medication used. When information was insufficient to reach a consensus, data were considered missing.
Data analysis The total number of PIMs, total number of patients using PIMs, and total number of patients using PIMs with previous history of CVD were assessed. The most commonly described PIMs were analyzed and coded by pharmacotherapeutic groups, using the WHO ATC classification system. 15 tatistical analysis was performed using IBM SPSS v. 24. 0. Descriptive statistics were used, where numerical variables were expressed using central tendency and dispersion measures (either as mean and SDs, whichever was applicable) and categorical variables as absolute and relative frequencies. Bivariate statistics were used to compare both settings regarding differences in sociodemographic, clinical, and pharmacotherapeutic features. Chi-squared test and Student's t-test for independent samples were used, whichever was applicable considering a 95% CI.
Limitations This study has some limitations. First, to assess drug use in the ambulatory setting, we reported community pharmacy data which limited our ability to extract accurate information on patients' comorbidities, and consequently the capacity to judge drug-disease interactions. Second, the number of comorbidities, the previous history of CVD, and the presence of dementia were based on the analysis of the pharmacotherapeutic regimen of each patient available in the community pharmacy and LTCF. We therefore believe that comorbidities are more likely to be underreported and some misclassification bias could also be present. However, to minimize this bias, we have assessed comorbidities as a group (history of CVD or dementia), instead of using individual diagnoses from both settings. Third, we had incomplete information on all drug-related variables in both settings, which did not allow for the extraction of the frequency of exposure.
Conclusion More than half of the elderly included in our sample were using PIMs with risk of CCVAEs, and approximately 40% of those were associated with risk of MACCE. About half of patients with a previous history of CVD took PIMs. The most commonly used PIMs with risk of MACCE were NSAIDs and antipsychotics, which accounted for almost half of the total drugs assessed in this sample. Future interventions and more tailored tools for the management of drug therapy in elderly patients with CVD are of major importance. Clinical Interventions in Aging is an international, peer-reviewed journal focusing on evidence-based reports on the value or lack thereof of treatments intended to prevent or delay the onset of maladaptive correlates of aging in human beings. This journal is indexed on PubMed Central, MedLine, CAS, Scopus and the Elsevier Bibliographic databases.
Acknowledgments The authors would like to acknowledge the LTCFs and the community pharmacy for all the work in data extraction and for their participation in this study. The authors would also like to thank  Fundação para a Ciência e a Tecnologia, I. P. (FCT), Ministério da Ciência e da Tecnologia, Portugal  for the PhD Grant to  João Pedro Aguiar  ( SFRH/ BD/132785/2017 ).
In section 3, we analyze the origin of the anticyclonic vorticity and potential vorticity that feed the Puddies. A case study of Puddy formation is presented in section 4. In this section, we describe the main processes that are associated with Puddy formation. Furthermore, we analyze the eddy-mean interactions (section 4. 2). The analysis of the effect of varying the model resolution and topography on the Puddy formation is analyzed in section 5. Section 6 includes a summary and perspectives for future work. Satellite-derived sea surface temperature (SST) from the Multi-scale Ultra-high Resolution (MUR) data set were compared with SST from the model. MUR is a daily global data set that is available from 1 June 2002 to the present. This product is a combination of data from the Advanced Very High Resolution Radiometer, Moderate Imaging Spectroradiometer Terra and Aqua, and Advanced Microwave Spectroradiometer-EOS instruments.
3-km resolution (L3). The spin-up times of L1, L2, and L3 were assumed to be less than (or equal to) 2 years, 1 year, and 2 months, respectively. The initial and boundary conditions were processed through the ROMS-to-ROMS package by using the methodology in  Mason et al. (2010). The Puddy formation was analyzed from the L3 simulation for the period between 14 January and 14 May 2006, which corresponds to a period in the calendar year when the PCUC is more intense (e. g. ,  Combes et al. , 2015). The domains of these three simulations are shown in Figure  1. The topography that was used in L1 and L2 was obtained from SRTM15-plus (Shuttle Radar Topography Mission), which is a global bathymetry data set with 15 arc-sec nominal resolution (~0. 5 km; http://topex. ucsd. edu/WWW_html/srtm30_plus.
Simulations L1 and L2 had 50 vertical levels, while L3 had 80, which enabled us to resolve details in the topography and have enough vertical levels in the intermediate ocean layers. The atmospheric forcing for all the simulations was the same as that for the parent domain. The momentum flux was taken from a downscaled product based on NCEP/NCAR data by using QuikSCAT data for the training period of the statistical model. Over the period that was considered here for the simulation (i. e. , June 2005 and May 2006), the momentum flux was almost equivalent to using wind fields from QuikSCAT data (the reader is invited to refer to  Goubanova et al. , 2011,  for details in the statistical model). Heat and freshwater fluxes were derived from the bulk formula  (Fairall et al. , 2003)  by using the air temperature from COADS monthly climatology (da  Silva et al.
Acknowledgments Marcela Contreras  was supported by a master scholarship in Chile from  National Chilean Research and Technology Council (CONICYT). We are thankful for the supported by  FONDECYT  Grant  1181872  and the  Millennium Science Initiative  ( IC 120019 ). We thank  Jonathan Gula  for help with section and the analysis of bottom pressure anomalies (Supporting Information). Cruise data used in Figures 3 and4 are freely available under request to the  Chilean National Center for Hydrographic and Oceanographic Data (Centro Nacional de Datos Hidrográficos y Oceanográficos de Chile ; http://www. shoa. cl/n_cendhoc/). CROCO model is available at http://www. croco-ocean. org. All input data set and configuration of our CROCO simulations are described in section. Powered@NLHPC: This research was partially supported by the supercomputing infrastructure of the NLHPC ( ECM-02 ).
The distribution of the atomic and molecular gas can be derived using Hi and CO observations, while that of the star formation activity using direct tracers such as the Balmer Hα emission line or the UV emission, properly corrected for dust attenuation using for instance the far-infrared emission, or using SED fitting codes when multifrequency data are available. The Herschel Reference Survey (HRS,  Boselli et al. 2010 ) is a complete sample of 323 nearby galaxies defined with the purpose of studying the relation between the star formation process and the different components of the interstellar medium (ISM). This sample has been observed at all frequencies to provide the community with the largest possible set of homogeneous data. This unique dataset includes photometric data in the UV and in the optical bands  (Cortese et al.
Then a sky cube is built by fitting the sky dominated regions (polynomial fitting of 2 nd order in our case) and then making an interpolation of the sky spectrum on the galaxy-dominated regions. Such a sky cube is finally subtracted. vii) Eventually, suppressing ghosts due to reflection at the interfaces air/glass of the interferometer when necessary. viii) Computing the astrometry to find the correct World Coordinate System for each galaxy dataset, making use of XDSS R-band images. This is done using KOORDS routine of KARMA 4   (Gooch 1996)  by a systematic comparison between field stars that are present in both the broad-band XDSS images and our continuum images. ix) Data processing of adaptive spatial binning based on Voronoi tesselation on the data. The latter preserves the spatial resolution of bright regions while the weak emission of diffuse gas areas is recovered.
Fabry-Perot observations with the 1. 93m telescope at OHP. comparison between the Fabry-Perot fluxes and those from  Boselli et al. (2015). The response of the GHASP camera being linear and since we are not comparing quantities having the same units, we use an Ordinary Least Squares (OLS) bisector linear regression, represented on the figure with a solid red line, to calibrate the Fabry-Perot Hα fluxes. The fit is forced to pass through the origin for obvious physical reasons; the calibration coefficient is 1 ph s -1 = 0. 50 ± 0. 05 × 10 -13 erg cm -2 s -1. The integrated fluxes estimated for each galaxy using our calibration are given in Table  E. 3. We measure the sensitivity of our Fabry-Perot dataset using two methods. The first one consists in computing the detection limits from the isophotes of VESTIGE Hα narrow band images  (Boselli et al.
Quality Checks of the Residual Velocity Fields For each residual velocity field, the average value (Res) and the standard deviation of all the bins (σ res ) are computed (see Table  E. 3). Figure  3  displays, for the whole dataset, the correlation between σ res (with a mean value ∼12. 6 ± 2. 7 km s -1 , which is similar to the velocity of 13 km s -1 , computed in  Epinat et al. 2008a ) and the mean amplitude of the velocity field (which is the maximum rotation velocity V max 5 multiplied by the sin i kin ). The bisector linear regression traces a trend which indicates that, in general, high velocity amplitude values are correlated with high σ res values. Nevertheless, many points for which V max × sin i kin < 150 km s -1 are situated above the regression line, corresponding to galaxies with quality Flags "3-B" and "4-B", so that the 5 Defined in paragraph 4.
In the maps, the green line is the morphological major axis, while the black/white line is the estimated kinematical major axis, its length represents the 2 × r opt. On the rotation curve, blue dots indicate the approaching side while red crosses the receding side, the black solid vertical line represents the r opt , and the black dashed line represents the r 25 ; the blue solid line the r e f f and the green horizontal line the Hi V max ; the solid black curve is the Courteau function best fit to the rotation curve. The maps and plots for the all the galaxies of the HRS are online material, available at the CDS, on the HRS dedicated database HeDAM (https://hedam. lam. fr/), and on the Fabry-Perot database (https://cesam. lam. fr/fabryperot).
Kinematical Projection Parameters As we saw in subsection 4. 1. 1, we need a set of initial guess parameters in order to compute our kinematical models. Such a set is composed either by parameters taken from high quality data available in the literature for the HRS (morphological measurements extracted from surface brightness photometry PA morph , i morph ), systemic velocity from long-slit spectroscopy V sys , or carefully calculated by us using morphological and kinematic parameters. The galaxy center α and δ is computed either as the nucleus in the continuum image or as the kinematical center of the velocity field. Table  E. 3 shows the morphological parameters PA morph and ellipticity used to compute i morph for each galaxy, taken from  Cortese et al. (2012). We computed i morph from the value following the procedure as in  Masters et al.
Discussion: Dynamical Masses Several models, simulations and multifrequency observations consistently indicate the mass as the principal driver of galaxy evolution (i. e. Cowie et al. 1996 , Gavazzi et al. 1996 , Boselli et al. 2001 for observations; Navarro et al. 1996 , Boissier et al. 2003 , Dutton 2012 , Moster et al. 2013 , Dutton & Macciò 2014  for models and simulations). The mass of galaxies is generally measured through the stellar mass which is derived from optical and near-IR imaging data. Nevertheless other components are present. These include dark matter, which is generally dominant at large radius, the mass of the different gas phases (atomic and molecular hydrogen, helium, ionised and hot gas, metals), and the mass of dust. The unique dataset available for the HRS allows us to directly measure most of the baryonic components: Hi and CO data are available for most of the star forming galaxies of the sample  (Boselli et al.
3. Fabry-Perot data from the literature (grey symbols) are classified as: stars for galaxies belonging to the Loose Groups Survey, rhombuses for galaxies belonging to the Virgo Survey, and pentagons for galaxies belonging to the GHASP survey. the sample definition (see sect. 2) and the statistics (∼200 objects spanning a wide range in morphological type and mass), make the HRS the best sample available in the literature for this purpose. To study the dynamical properties of the sample in the context of galaxy evolution, we proceed as follows: i) Since we lack of Fabry-Perot data for 26. 4% of the HRS, we first test whether the Hi kinematical data can be used to derive the total dynamical mass of the missing galaxies without introducing any systematic bias. ii) We check the statistical significance of our sample by comparing the Tully-Fisher relations to those derived in other representative samples generally used in the literature.
iii) We then study the stellar and baryonic Tully-Fisher relations, the relation between the baryonic and dynamical mass, and finally we derive the main sequence for the first time using the dynamical mass 6. 5. 1. V max,HI versus V max,Hα  Boselli et al. (2014a)  published a homogenised catalog of Hi data for the whole HRS. Their work provides line widths W HI measured at 50% of the peak flux per galaxy. We compute V max,HI assuming that W HI = 2 V max,HI sin(i); where  Gómez-López, J. A. et al. :  An Hα kinematic survey of the Herschel Reference Survey -I. Fabry-Perot observations with the 1. 93m telescope at OHP. V max,HI is the maximal Hi rotation velocity. On the other hand, we compute the maximal Hα rotation velocity V max,Hα from rotation curves derived from 2D velocity fields, thus intrinsically calculated in a more accurate way.
It is indeed known that the slope of the TF relation is known to slightly change with luminosity (e. g. Schaye et al. 2015). We check hereafter how the statistically limited HRS compares with works which are generally based on several hundreds of galaxies. We directly compare the HRS data with two other samples, one in the optical  (Masters et al. 2006 ) and the other in the IR  (Sorce et al. 2014) , see Figures  8  and 9 , Tables  1  and 2. We use the  Masters et al. (2006)  sample because it has been qualified by these authors as a "template calibrator sample", by the way this sample is partially extracted from the  Giovanelli et al. (1997a,b)  sample. It consists of 807 galaxies while the present HRS consists of 123 objects. In order to fairly compare the results we compute the TF relationship on the whole  Masters et al.
Observational evidence indicates mass as a principal driver of galaxy evolution (e. g. Cowie et al. 1996; Gavazzi et al. 1996; Boselli et al. 2001). Consistently with these obervational results, hydrodynamic cosmological simulations suggest that the evolution of galaxies is mainly gouverned by the dark matter halo in which they reside  (Governato et al. 2012; Brooks & Zolotov 2014; Christensen et al. 2014). It would thus be  interesting to derive the main sequence using dynamical masses rather than with stellar masses. The stellar main sequence relation has been derived and analysed for the HRS by  Ciesla et al. (2014) ,  Boselli et al. (2015)  and  Boselli et al. (2016). Thanks to the unique dataset in our hands, we can compare for the first time in the literature the main sequence relations derived using M star , M bar , and M dyn (Fig.