dataesr/scientific-paragraphs-categorization huggingface dataset:
Association of physical inactivity with dementia in relation to cardiometabolic disease Morbidity and mortality data for disease trajectories were available from five studies (appendix, page 12) and a total of 94 835 participants, of whom 90 038 were free from cardiometabolic disease at baseline and had no history of dementia at year 10. Out of these 90 038 participants, 300 developed dementia   without preceding incident cardiometabolic disease, and 77 first developed incident cardiometabolic disease (diabetes, coronary heart disease, or stroke) and then dementia. As shown in figure  6 , there was an imprecisely estimated excess risk of dementia after cardiometabolic disease in physically inactive versus physically active individuals, the age, sex, ethnicity and socioeconomic status/education adjusted hazard ratio being 1.
Comparison with other studies The validity of our approach is supported by the fact that our observed associations between physical inactivity and dementia are similar to those from most recent literature-based meta-analyses. The summary effect estimate for physical inactivity was 1. 4 (hazard ratio) in the present dataset for the first 10 years of follow-up and was 1. 3 (odds ratio)  7  and 1. 4 (hazard ratio)  5  in two previous meta-analyses of cohort studies in which most studies had a follow-up shorter than 10 years (13 of 16 and 19 of 21 studies, respectively). A slightly higher effect estimate, a hazard ratio of 1. 5, was reported in a meta-analysis of people aged 70-80 years. 6 This hazard ratio is similar to our findings for participants older than 60 in the <10 year follow-up. We believe these associations may be largely attributable to bias arising from changes in physical activity in the years preceding dementia and not from a true causal effect of physical activity on dementia risk.
Strengths and limitations of study Our study benefits from its large sample size, use of individual-level rather than study-level data in meta-analyses, and methodological triangulation in which multiple statistical approaches led to the same conclusion. Dementia ascertainment was based on electronic health records. This enabled all participants recruited to the study to be included in the analyses, rather than only those who continued to participate in follow-up examinations. 39 40 ome limitations to our study may have contributed to an underestimation of the effect of physical inactivity on dementia. We used a single, self reported measure of physical inactivity. This method is prone to reporting bias and does not capture cumulative effects of physical inactivity. We corrected for measurement error in physical inactivity using Rosner's method, although this may not be sufficient to account for our crude assessment of the exposure.
RESEARCH ARTICLE In situ growth of deep-sea octocorals after the Deepwater Horizon oil spill Fanny Girard *, † , Rafaelina Cruz * , Orli Glickman * , Tyler Harpster * and Charles R. Fisher * Although the role of deep-sea corals in supporting biodiversity is well accepted, their ability to recover from anthropogenic impacts is still poorly understood. An important component of recovery is the capacity of corals to grow back after damage. Here we used data collected as part of an image-based longterm monitoring program that started in the aftermath of the Deepwater Horizon oil spill to develop a non-destructive method to measure in situ growth rates of Paramuricea spp. corals and characterize the impact of the spill on growth. About 200 individual coral colonies were imaged every year between 2011 and 2017 at five sites (three that were impacted by the spill and two that were not).
Data collection Although the three impacted sites in Mississippi Canyon were monitored annually between 2011 and 2017, corals at AT357 were not monitored in 2017 because of logistical/weather constraints (Table  1 ). Because image quality was generally poor in 2013, images from that year were not used in this study. Moreover, corals in GC852 were only imaged in 2016 and 2017. Although the corals in MC294 were also imaged in 2010  (White et al. , 2012) , these images were not used for this study as image resolution was suboptimal for growth measurements and, because corals were still covered in floc at that time, branches were often not clearly visible. High-definition photographs of each individual coral colony were obtained using a digital still camera held in the manipulator arm of a Remotely Operated Vehicle (ROV).
48, and used as a proxy for the total number of branches. As for size, this number was adjusted the following years by removing branches that broke off. As coral size could not be directly measured at GC852, a polynomial regression model, including coral size as a response variable and the total number of branches as explanatory variable, was used to predict the size of each coral colony at GC852 based on their total number of branches (Figure  S1 ). Data collected on P. biscaya colonies at MC294, MC297 and MC344 were used for this regression model. To allow for comparisons with other studies, the height of each coral colony (distance between the base of the colony and the tip of the longest branch) at MC294, MC297, MC344 and AT357 was measured in ImageJ 1. 48 using the scale provided by the ball in the images.
Therefore, the number of polyps on each terminal branch was counted and used as reference to detect/confirm growth. Two growth measurement methods were tested (Figure  2 ). Method 1 (measurement between consecutive years). Images were digitized to measure growth between every consecutive year that the corals were imaged, with the  a An asterisk indicates images that were digitized (the other images were used to identify growth which was subsequently digitized on images identified with an asterisk). b Images used to compare growth rates between reference site GC852 and impacted sites MC294, MC297 and MC344. A larger number of images were used for the 2016-2017 independent comparisons, as coral colonies discovered after 2012 could be included in that dataset. c Not applicable. exclusion of 2013.
Methods comparison Images of 21 coral colonies at MC294 were used to test for differences in growth detection between the two growth measurement methods (Table  1 ). This site was chosen for several reasons: it was dominated by Paramuricea biscaya, the species the most affected by the spill; corals were monitored throughout the 2011 to 2017 time period; and, of the three Mississippi Canyon sites, preliminary data indicated that corals at this site grew the most. Differences in the average growth proportion measured on MC294 corals with Method 1 and Method 2 were tested with Sign tests. The proportion of branches detected with Method 1 but missed with Method 2 and vice versa were estimated between each consecutive year or between 2011 and 2017 and compared with two-proportion z-tests. For both tests a Bonferroni correction of a = 0.
The effect of size on the amount of linear growth (in centimeters) was not tested, as it did not follow a normal distribution and no transformation could be applied to approximate a normal distribution due to the large number of zeros in the dataset. A binomial error distribution with a logit link function, commonly used for proportion data with many zeros, was used for all generalized linear models. A quasibinomial instead of binomial distribution was used when overdispersion was detected (the residual deviance was greater than the residual degree of freedom, meaning that the variability in the data was larger than the variability expected under the assumed binomial distribution).
Age estimation The measured average annual growth rates for each site were used to estimate the age of all corals at a given site based on their total size in 2011 (before impacted corals started to lose significant numbers of branches). Ages were then compared to the ages estimated with radiocarbon dating by  Prouty et al. , (2014)  for Paramuricea biscaya or Paramuricea sp. B3 at the same sites. For comparison, ages were plotted against height (data on total coral size were not available in  Prouty et al. , 2014). For each site, a linear regression model with coral age as response vari-able and height as explanatory variable was fitted to the data. When the assumptions of homoscedasticity and normality were not met, a Box-Cox transformation of the response variable was applied. All statistical analyses were performed in R (R Core Team, 2014).
The much larger dataset reported here contains many larger corals at each site and extends the longevity estimates from  Prouty et al. , (2014)  for both species. While  Prouty et al. , (2014)  estimated that the two specimens of P. sp B3 that they collected (with heights less than 25 cm) were about 60 years old, we found that some of the largest colonies at this site (between 50 and 75 cm in height) could be over 700 years old. The oldest P. biscaya colony measured by  Prouty et al. , (2014)  was collected at MC344. It was about 25 cm in height and estimated to be 660 years old. Based on average growth rates determined from images, the larger colonies at MC344 are over 2000 years old (Figure  7 ). In fact, the two largest colonies analyzed at this site could be as old as, or even older than, the oldest coral reported to date (Leiopathes sp.
, 4265 years old;  Roark et al. , 2009). Importantly, ages presented in our study were estimated based on a constant growth rate throughout the life span of coral colonies, as our data did not show an effect of size at most sites. As octocorals generally demonstrate declining growth with age or size  (Coma et al. , 1998; Cordes et al. , 2001; Lasker et al. , 2003; Doughty et al. , 2013) , additional study will likely refine these estimates. Because a small amount of growth may not have been detected when polyps were not visible, especially when branches were unhealthy, the growth of impacted corals may have been underestimated. Overall, growth measurements were more accurate when the 2012 and 2017 images were compared directly than when growth was measured between every consecutive year and then summed.
Acknowledgements We would like to thank the crews of the ships and deep submergence facilities involved in this study as data collection would not have been possible without their expertise. We also thank  S. Vohsen ,  C. Du Preez ,  R. Dannenberg ,  J. Mentch ,  M. Kurmann ,  D. McKean ,  S. Berlet ,  S. Smith ,  A. Weinheimer ,  C. Bashaw ,  A. Yang ,  K. Anderson , and  M. McGuigan  for their assistance at sea and in the lab. We are particularly grateful to  T. Boyer ,  T. McElroy ,  L. Bullock ,  G. DiNicola  and  T. Albertson  for their invaluable help with growth measurements. This.
The flat field images quantified the instrumental artefacts and noise that were used to correct the Xray radiography images according to Equation (1). The corrected rocking curve images were analyzed using an in-house software code based on LabView®. Figure  2  shows the typical rocking curves extracted from one arbitrary detector pixel. Open circles indicate the measurement without and filled A Si(111) analyzer crystal was placed in the beam path between the specimen and the camera system, shown in Figure  1 , to perform refraction radiographs. The analyzer crystal reflect the beam coming out of the specimen into the detector system if the incidence angle is set to the Bragg angle, θ B = 2. 2664 • at 50 keV. By tilting the analyzer crystal around an axis perpendicular to the scattering plane (the scattering plane of the Si(111) crystal lies in the plane of Figure  1 ), the so-called rocking curve is recorded.
The corrected rocking curve images were analyzed using an in-house software code based on LabView ®. Figure  2  shows the typical rocking curves extracted from one arbitrary detector pixel. Open circles indicate the measurement without and filled circles with the specimen in the beam, respectively. The peak maxima are normed to unit to show the increase in beam divergence due to the refraction effect at interfaces (e. g. , cracks and/or pores) inside the specimen. The analysis software delivered the values of the rocking curve integral, the peak height, peak position and the Full Width at Half Maximum FWHM (see Table  1 ). By using the image calculating software "Fiji Image J"  [24]  the attenuation (µ•d) and the refraction value (C m •d) were evaluated for each pixel according to Equations (  1 ) and (2), respectively.
By using the image calculating software "Fiji Image J"  [24]  the attenuation (µ•d) and the refraction value (Cm•d) were evaluated for each pixel according to Equations (  1 ) and (2), respectively. A detailed description of the data processing and evaluation can be found in  [25, 26]. 𝜇 • 𝑑 = ln 𝐼 𝐼 ( 1 ) 𝐶 • 𝑑 = 1 - 𝐼 • 𝐼 𝐼 • 𝐼 (2) The influence of the specimens' thickness d is eliminated by dividing the local refraction value (Cm•d) by the local attenuation property (µ•d). This yielded the relative specific refraction value (Cm/µ), which is proportional to the relative specific internal surface of the specimen up to a calibration constant, depending on the instrument, the material and the experiment (geometry and energy).
Acknowledgments: Authors are thankful to  Saint-Gobain CREE  for the supplying of materials. We thank  HZB  for the allocation of synchrotron radiation beamtime. We thank  HZB colleagues  for their support as well as  Ralf Britzke  and  Thomas Wolk  ( BAM ) for their assistance during beam time at BAMline. Acknowledgments: Authors are thankful to  Saint-Gobain CREE  for the supplying of materials. We thank  HZB  for the allocation of synchrotron radiation beamtime. We thank  HZB colleagues  for their support as well as  Ralf Britzke  and  Thomas Wolk  ( BAM ) for their assistance during beam time at BAMline.
Length f n1 = 3. 516 l 2 D b (α) ρ b bh b + 2ρ p bh p , (12) in which, ρ b and ρ p are the density of the host beam and that of the piezoelectric patches, respectively. From equation (  12 ) it can be seen that the bending stiffness D b is positively correlated to the resonant frequency, therefore the latter can be used as an indicator of the former. The first bending mode of the clamped-free unit is also numerically studied using the finite element method (FEM). The simulations are done in the COMSOL Multiphysics software. 3D quadratic Lagrange elements are used in the FEM model, as shown in figure  2. The control law in equation (  11 ) is applied on the actuators as electric boundary condition. Figure  3  shows the variation of the resonant frequency of the first bending mode when α changes.
Persistent wind-accreting Supergiant HMXBs Persistent wind-accreting supergiant HMXBS (sgH-MXBs) are systems with an early type (O or B-star) supergiant companion, losing large amounts of mass through a stellar wind. The compact objects in these systems accrete from this dense wind, and, while they tend to show a large variability (up to a factor of 100), they are always active and some were among the first HMXBs discovered. INTEGRAL contributed to the knowledge about this source class by the merit of long observations of fields containing these sources -frequently not directly targeting the sources themselves. INTEGRAL data in the hard X-ray band have also been important to disentangle intrinsic variations of the X-ray source flux from the effects of absorption when studying X-ray variability.
The energy-dependent profile of the X-ray light curve during the eclipse ingress and egress also reveals details of the OB stellar wind structure (see e. g. ,  White et al. , 1995). These light curves reveal complex eclipse ingresses and egresses that are understood mostly as being caused by accretion wakes. These results constitute a database to be used for population and evolutionary studies of HMXBs and for theoretical modeling of long-term accretion in wind-fed Xray binaries. Determining the equation of state (EoS) of matter at densities comparable to those inside NSs is one of the most challenging problems of modern physics and can only be addressed based on observations of astrophysical sources. Models proposed in the past years can be tested against observational results, especially by evaluating the highest NS mass that each EoS model is able to sustain (see e.
Acknowledgments Based on observations with INTEGRAL, an  ESA  project with instruments and a science data center funded by  ESA  member states (especially the PI countries:  Denmark, France, Germany, Italy, Spain, and Switzerland) , the  Czech Republic , and Poland and with the participation of Russia and the  USA. The INTE-GRAL teams in the participating countries acknowledge the continuous support from their space agencies and funding organizations: the  Italian Space Agency ASI  (via different agreements including the latest one,  2019-35HH , and the  ASI-INAF  agreement  2017-14-H. 0 ),the  French Centre national d'études spatiales (CNES) , the  Russian Foundation for Basic Research  (KP,  19-02-00790 ), the  Russian Science Foundation  (ST, VD, AL;  19-12-00423 ), the  Spanish State Research Agency  (via different grants including  ESP2017-85691-P ,  ESP2017-87676-C5-1-R  and  Unidad de Excelencia María de Maeztu -CAB   MDM-2017-0737 ).
Remote and mountain atmospheric measurements are needed to assess background mole fractions because they are performed far from anthropogenic sources and/or are located in the free troposphere. Such " global-scale " data are of great value for monitoring the global atmospheric GHG build-up and estimating global-scale fluxes. However, they are not designed to capture the regional-scale signals necessary to assess local-to regional-scale fluxes. The specific purpose of the European Integrated Carbon Observation System (ICOS) is to establish and maintain a dense European GHG observation network to monitor long-term changes, assess the carbon cycle, and track carbon and GHG fluxes. Inverse atmospheric methods combining tall tower network measurements and transport models are important tools for assessing surface GHG fluxes Published by Copernicus Publications on behalf of the European Geosciences Union.
exchanged with the biosphere and oceans, and estimating the anthropogenic emissions  (Broquet et al. , 2013; Kountouris et al. , 2018). They also offer independent ways to improve the bottom-up emissions inventories required by the international agreement under the United Nations Framework Convention on Climate Change  (Bergamaschi et al. , 2018; Leip et al. , 2018; Peters et al. , 2017). ICOS was established as a European strategic research infrastructure which provides the high-precision observations needed to quantify the greenhouse gas balance of Europe and adjacent regions. It is now a widespread infrastructure made up of three integrated networks measuring GHGs in the atmosphere, over the ocean and at the ecosystem level. Each network is coordinated by a thematic centre that performs centralised data processing.
Site location The OPE atmospheric station (48. 5625 • N, 5. 50575 • E WGS84, 395 m a. s. l. ) is located on the eastern edge of the Paris Basin in the north-east part of France, western Europe, as shown in Fig. 1. The landscape consists of undulating eroded limestone plateaus dissected by a few SE-NW valleys. The station is on top of the surrounding hills in a rural area with large crop fields, some pastures and forest patches. According to Corine Land Cover 2012, the dominant land cover types in the 25/100 km surrounding area are arable land/crops (39 %/44 %), pastures (14 %/18 %) and forest (44 %/34 %). Based on the GEOFLA database from Institut national de l'information géographique et forestière (IGN), the mean population density within a 25/100 km radius from the station is 26/64 inhabitants km -2.
Another short-term working standard called the reference (REF) gas is also used every 10 h to correct shortterm variability. The mole fractions of the standard cylinders cover the unpolluted atmospheric range following ICOS Atmospheric Station specifications  (Laurent, 2017). The standard gases are supplied via SCOTT nickel-plated brass regu- The raw data from the analysers along with the distribution system monitoring parameters are transmitted to the ATC database on a daily basis. Data are then processed following  Hazan et al. (2016)  including a specific water vapour correction for the remaining humidity, as well as a stationspecific automatic flagging process. Data products are then generated and data quality control is carried out on a regular basis. Additionally manual flagging is performed by the station's principal investigator (PI) on the raw data and on the hourly aggregated data.
The objective is to provide users with continuous time series, combining valid measurements in order to minimise the data gaps. Before merging the time series, each instrument is quality controlled individually, and only measurements which are validated by the automatic data processing and the PI are considered for the combined dataset. For each measurement we indicate the reference of the measuring instrument (unique identifier in the ICOS database), providing the user with analyser traceability. To build these time series from various analyser datasets we used the priority order given in Table  2  for CO 2 and CH 4 and Table  3  for CO. The priority order is defined a priori by the station PI considering which analysers are fully dedicated to the station for long-term monitoring purposes.
It is the responsibility of the station manager to change the priority list in the ICOS database if needed. Merging the individual time series in such a way implies that the merged time series show steps in their uncertainties as individual analysers have different performances (see Sect. 3 for details about the steps in repeatability performance). Various instruments were used in parallel for some time and it is thus possible to assess systematic differences between the data for these common periods. The instruments may have shared sampling tubes, calibration and quality control gases but may have also used a different air distribution system and different cylinders. Consequently, differences may occur due to problems associated with time synchronisation, air sampling (sampling and flushing pump efficiencies), calibration and water correction or other causes not yet identified.
The hourly means of the two analysers showed a general good agreement, with mean differences of the order of 0. 04 ppm (with a standard deviation of 0. 40 ppm). However significant deviations of several parts per million were also found. 3 Data quality assessment QA-QC protocols are applied at several steps in the measurement system. Every day, a conservative quality control is conducted from two complementary standpoints: firstly, intrinsic properties of the spectrometers are verified, and secondly the sampling system parameters are checked. On a weekly to monthly basis, the field performance of the spectrometers is also checked. A flask programme also runs in parallel and is used to expand the atmospheric monitoring to other trace gases and to assess the quality of the continuous measurements.
Up to now, flask data were not fully available or were contaminated, and thus have not been used in  the present work. A complementary approach to assess compatibility uses round robin or cucumber cylinders circulated between stations within the ICOS European network. Finally, the station compatibility is also assessed during in situ audits using a mobile station and travelling instruments  (Hammer et al. , 2013; Zellweger et al. , 2016). In this section we used two metrics defined in Yver  Kwok et al. (2015)  for quality control assessment of the data. These two metrics are usually calculated under measurement repeatability conditions where all conditions stay identical over a short period. Continuous measurement repeatability (CMR), sometimes called precision, is a repeatability measure applied to continuous measurements.