dataesr/scientific-paragraphs-categorization huggingface dataset:
Data Source Data over a 15-year period (January 1, 2000 to March 31, 2016) were obtained from Health Facts ® (Cerner Corp. , North Kansas City, MO, USA), a Health Insurance Portability and Accountability Act (HIPAA)-compliant deidentified EHR database covering >60 million patients that is sourced from >600 participating facilities throughout the United States. The EHRs contained coded administrative records (eg, diagnostic, procedural, demographic, payer, and billing), data on drug administration while in a hospital or a clinic setting, as well as parameters from pharmacy, laboratory, microbiology, and hospital (including medical, surgical, and emergency department) systems. Patient records were linked through time as well as across facilities, allowing for longitudinal tracking of individual patients across all of the facilities covered by the database.
Study Population Patients aged ≥18 years with data in the Health Facts database who were diagnosed with knee OA and had been treated with hylan G-F 20 (SYNVISC ® ; Genzyme Biosurgery, Cambridge, MA, USA) or hylan G-F 20 single (SYNVISC ® ; Genzyme Biosurgery, Cambridge, MA, USA) were included. Patients were excluded if they died within 6 months after the initial hylan G-F 20 treatment; had no recorded visits in the database within 6 months before or 6 months after the initial treatment (to ensure the patient was treated within facilities covered by the database); were treated with a viscosupplement other than hylan G-F 20; had a major surgical procedure prior to initial hylan G-F 20 treatment; had received an antidepressant; or had a prior or current diagnosis of fibromyalgia, rheumatoid arthritis, ankylosing spondylitis, osteonecrosis, other arthritis or collagen disease, or OA in other regions of the body.
Study Design Patients' index dates were determined at the initial hylan G-F 20 treatment, defined as either 1-3 injections of hylan G-F 20 (2 mL) over the course of 21 days or a single injection of hylan G-F 20 (6 mL). Availability of patient data was calculated as the period between the earliest patient visit in the 6 months before hylan G-F 20 injection and the first injection of hylan G-F 20 for the beforetreatment period, and as the period from the third (or last if <3 injections of hylan G-F 20) injection within a 21-day period following the initial injection (hylan G-F 20), or the time between the initial treatment (hylan G-F 20 single) and either up to 6 months after, or patient dropout due to a major procedure, during the after-treatment period (Supplementary Figure  1 ).
Statistical Analysis Primary endpoints included days on opioids, MME, and the number of IA corticosteroid injections during baseline (6 months prior) and following (6 months after) hylan G-F 20 treatment. Endpoints were compared via paired t-tests for 6 months pre-and post-treatment, as well as for other equivalent time periods pre-and post-treatment (eg, comparing 3 months before hylan G-F 20 with 3 months after), based on data availability (Supplementary Figure  2 ). A two-tailed P value < 0. 05 was considered statistically significant. The paired t-tests were used for the differences in days on opioids, amount of opioids (MME), and the number of corticosteroid injections before and after index hylan G-F 20 treatment. The distribution of the differences between baseline and follow-up values was plotted to verify the assumptions of the paired t-test, and were found to be relatively symmetric.
Disposition The initial database analysis identified 2644 patients who had received either hylan G-F 20 or hylan G-F 20 single between January 1, 2000 and March 31, 2016; N = 1659 of these patients were diagnosed with knee OA. After applying inclusion and exclusion criteria, 513 patients were available for analysis (Patient attrition, Supplementary Figure  3 ; Patient demographics, Table  1 ). Of these 513 patients, 395 (77. 0%) patients had >2 months of baseline data available and, on average, had between 5 and 6 hospital/clinic visits within 6 months prior to hylan G-F 20 treatment.
Opioid Use Following Hylan G-F 20 Treatment Among the 72 patients who received opioids within the 6 months before treatment with hylan G-F 20, 50 patients had 6 months each of baseline and follow-up data available. Among these patients, the mean total number of days on opioids 6 months after hylan G-F 20 treatment was statistically significantly lower than 6 months prior to hylan G-F 20 treatment (N = 50; 5. 0 vs 13. 5; P = 0. 007). Forty-four of the 50 patients' records included opioid amounts. Of these patients, the total amount of opioids per patient during the 6 months after index treatment with hylan G-F 20 was significantly lower than the 6 months prior to treatment (N = 44; 165. 4 vs 493. 7 MME; P = 0. 013). Data for patients with <6 months of data before and after index treatment also showed significantly fewer days on opioids per patient after treatment with hylan G-F 20, with the exception of the group with data for 1 month before and after treatment with hylan G-F 20 (Figure  1A ; subgroups are labeled based on the number of months of data available for analysis during each of the baseline and follow-up periods).
Weight control and obesity Overweight is an emerging concern, given the changing BMI across the world's ageing population. New evidence supports the relationship between increased BMI and dementia from a review of 19 longitudinal studies including 589 649 people aged 35 to 65 years, followed up for up to 42 years. It reported obesity (BMI ≥30; RR 1•3, 95% CI 1•1-1•6) but not being overweight (BMI 25-30; 1•1, 1•0-1•2) was associated with late-life dementia. 109 In a further meta-analysis of individual level data from 1•3 million adults (aged ≥18 years), which included two studies from the meta-analysis cited above,  109  higher body mass measured before probable preclinical and prodromal dementia was associated with increased dementia risk (RR 1•3, 1•1-1•7/5-unit increase in BMI). 11.
PAF calculation We used a representative sample of over 10 000 UK community-dwelling adults, to calculate communality (clustering of risk factors) of 11 risk factors for which data existed,  159  to allow calculation of each factor's unique risk. As we could find no datasets measuring TBI, with the other 11 risk factors of interest, we could not calculate its communality. We therefore used the mean of the other 11 communalities to calculate a weighted PAF, so we could include TBI. We used cohabitation as a proxy measure for social contact, and urbanicity for air pollution exposure. Our analysis found four principal components, explaining 55% of the total variance between the eleven risk factors, suggesting substantial overlap. The appendix (p 2) shows the PAF formula and the steps in calculating communality and we detail our new meta-analyses.
Pollution A 2019 systematic review synthesised observational studies, finding consistently increased risk of dementia from air pollution, but heterogeneous comparator groups precluded meta-analysis. 24 We updated the search, using the same search terms and searching MEDLINE, Embase, and PsycINFO from Sept 20, 2018, (the end date of the last search) to Oct 22, 2019. We included longitudinal studies with assessment of all cause air pollution exposure; use of formal assessment of cognitive function at baseline; report of incident all-cause dementia, data from adults (age ≥18 years); and a minimum follow-up of 6 months. As meta-analysis was not possible, we used data from the only study of allcause air pollution with the outcome of all-cause dementia, with low-moderate risk of bias. This population-based, observational cohort was from Canada, where pollutant concentrations are among the lowest in the world and examined 2 066 639 people, with a mean baseline age of 67 years.
We have assumed a causal relationship between risk factors and dementia, although we have been cautious and not included risk factors with less good evidence. No single database exists with all 12 risk factors together, but we found 11 of the factors in a UK database and used the mean figure for communality calculations for TBI. We calculated communality for the other 11. We do not know how far findings of communality in other geographical populations might differ, or in those with a differing distribution of age groups or sex. We found that social isolation was not explicitly measured and had to use proxies, such as cohabitation when considering prevalence, which are approximate. Specifically, evidence for the association of alcohol misuse with dementia comes from HIC and future studies from LMIC are needed to complete the picture.
Fluid biomarkers PET imaging is very costly (US$3000 in the USA) and although used in some clinical settings remains the topic of research to understand its usefulness in broader populations. Fluid biomarkers-ie, blood and cerebrospinal fluid tests-have become a more practical focus of interest since it has become possible to measure specific proteins linked to the proteins associated with the neuropathologies of Alzheimer's disease. 188 A composite blood biomarker for amyloid tested in a discovery dataset and then a validation cohort of participants aged 60-90 years who were already taking part in studies in Japan or Australia had areas under the receiver operating Panel: Recommended strategies for dementia risk reduction Risks are particularly high in more socially disadvantaged populations including in Black, Asian, and minority ethnic groups.
Nutrient and DOC samples were sterile filtered (0. 2µm) and stored at -20°C before measurements. Nutrients were measured in triplicates after using standard colorimetric on a nutrient analyser (QuAAtro https://doi. org/10. 5194/bg-2020-314 Preprint. Discussion started: 4 September 2020 c Author(s) 2020. CC BY 4. 0 License. Biogeosc. iences f ( §G~ U D1scuss1ons @ K;. : gi 39, SEAL Analytical, Germany) using the protocols No. Q-068-05 Rev. 12 for nitrate (detection limit = 0. 02 µmol L -1 ), No. Q-068-05 Rev. 12 for nitrite (detection limit = 0. 02 µmol L -1 ), No. Q-066-05 Rev. 5 for silicate (detection limit = 0. 07 µmol L -1 ), and No. Q-064-05 Rev. 8 for phosphate (detection limit = 0. 01 µmol L -1 ). The data were analysed using the software AACE. The nutrient analyzer was calibrated with reference seawater (Ocean Scientific International Ltd.
Ammonium is the primary product of bacterial regeneration N-compound after remineralization of DON. Nitrification was assumed to be absent, since the bacteria in our experiment are not known to be capable of nitrification. The model was written in R and all model equations are provided in the Appendix (Table  A6 ). The differential equations were solved using the ode function of the deSolve package  (Soetart et al. , 2010)  with the Runge-Kutte method. After sensitivity analyses using the sensFun function of the FME package (Soetart and Petzoldt, 2010) (Fig. B1 ), and collinearity tests using the collin function and pairs plots (Fig. B2 ) the parameters not available from the experimental data (14 for our model, 6 for G98) were fitted based on the data of both the axenic experiment (i. e. without remineralisation) and the bacteria enriched experiment (i.
The model was fitted manually to reach an optimal fit for both the axenic and bacteria enriched experiment using the same values for the parameters, (Fig. 3, 4 ), considering known limitations in the lag and stationary phase. The modFit function, using the Pseudorandom search algorithm, was based on the modCost function of the FME package and used to test whether potential substantial improvements of the model fit using different parameter values could be achieved, but this was not the case so manual fits were retained. The G98 model based parameters were kept for the tuning of the additional parameters of the extended model aiming to use the same parameter values for both experiments. Bacterial growth parameters (µbact, bactmax) were determined in the growth experiment. Silicate related parameters (Ksi, Vmax, smin) were constrained by the study of  Werner (1978).
Ammonium related parameters (Knh4, nh4thres) were constrained loosely by measured ammonium concentrations, and remineralisation parameters (rem, remd) were left unconstrained. Collinearity tests, and manual and automated parameter fitting were done as described for the G98 model. Eventually, the 14 parameters (Table  A3 ) were fitted against 160 data points (Table  A1 ). The model cost was estimated via calculating the root of the sum of squares normalized by dividing the squares with the variance (RMSE Eq. (C1),  Stow et al. , 2009).
Acknowledgements The project was supported by  ArcticSIZE  -A research group on the productive  Marginal Ice Zone at UiT  (project number  01vm/h15 ). We want to thank  Paul Dubourg  and  Elzbieta Anna Petelenz-Kurdziel  for the help with Nutrient and POC/PON analyses. DOC analyses was supported through a  Fulbright Distinguished Scholar Award  to HRH.
1  and Fig. S1  in the Supplementary Information). The overlapping Raman bands of wood and PEG were separated by principal component analysis of an image mosaic, where the first five principal components explained 86% of the variation within the dataset. The analysis clearly showed that, given sufficient time, all of the PEGs could impregnate the cell walls, irrespective of their molecular weight. The first principal component (Fig. 1a ) showed mainly differences between the lignin-rich middle lamella and the carbohydrate-rich cell walls. In addition, within the cell wall regions of the PEG-containing samples, the contribution of PEG to the CH/CH 2 stretching bands at 2700 to 3020 cm -1 (see Supplementary Fig. S1f  for spectra from PEG in D 2 O ) decreased the scores of the first principal component as compared to the cell walls of the native wood.
The glass vials were placed in a roller mixer at room temperature and the PEG concentration was increased up to 60% with about 1-week intervals (except for a 3-week stay at 30%). The air remaining in the glass vials was replaced by N 2 gas prior to closing. After reach- ing the target concentration of 60% (w/v), the samples were left in the roller mixer for 6 to 8 months prior to the SANS experiments. 0] [41]  were carried out at the D11 instrument of the Institut Laue-Langevin in Grenoble, France. Wood sections with the cell axis aligned vertically were immersed in quartz glass cuvettes with a light path of 2 mm. Each cuvette was filled with the PEG solution from the corresponding sample. The wood samples in PEG4000 solution were additionally measured after immersing them in fresh D 2 O about 3 hours prior to data collection.
SANS measurements and data analysis. SANS experiments Confocal Raman imaging and analysis. Transversal sections with a thickness of 20 µ m were cut from the pine (2) samples (with and without PEG) using a rotary microtome. The sections were placed on glass slides together with a droplet of PEG solution from the corresponding sample, covered with a glass coverslip (thickness 0. 17 mm), and sealed with nail polish. Raman mapping was performed using a confocal Raman microscope (WITec alpha 300 RA, WITec, Germany) equipped with a 532-nm frequency-doubled Nd:YAG laser and a DU970-BV EMCCD camera behind a 600 lines/mm grating. Raman images  43  with a size of 45 µm × 45 µm and 175 lines per image and 175 points per line were acquired with a 100× immersion-oil objective (numerical aperture 1. 25, coverslip correction 0.
The partitional K-means clustering segregated the pixels based on their correlation with the mean of each cluster. A predefined number of clusters was chosen based on the mean correlation of pixels with the respective cluster centroids (Supplementary Fig. S1e ) and Euclidean distance was used to select the first centroids furthest away from the centre of the score space. The scores and pixel classes were refolded back to the image dimension to allow visualization of the results. Multivariate data analysis was performed through a combination of in-house Matlab (MathWorks, Inc. ) scripts and commercial functions from the PLS Toolbox (Eigenvector Research, Inc. ).
Acknowledgements The authors thank  Emil Aaltonen Foundation  (P. A. P. ) and  Academy of Finland  (P. A. P. grant no. 315768 , L. R. grant no. 309881 ) for funding. Institut Laue-Langevin  is thanked for beamtime (experiments  TEST-2747 ,  INTER-378 ,  DIR-175 ). This work made use of  Aalto University Bioeconomy Facilities. We are grateful for the support by the  FinnCERES Materials Bioeconomy Ecosystem. We thank  Dr. Yoshiharu Nishiyama  from  CERMAV/CNRS  for valuable suggestions on the manuscript and  Aleksi Zitting  from  Aalto University  for helping with the SANS measurements of the PEGs in solution.
The velocity is determined from the flight time (T) the particle needs to travel a known distance, d TOF in the detector geometry, and the energy is determined from the solid state detector pulse height. We will hereafter refer to this combination of energy and time of flight simply as E-TOF. The mass resolution enables the distinction between various ion species but, as noted above, is not sufficient to distinguish between nitrogen (N), oxygen (O), and carbon (C) which have very similar atomic masses. This data product from RAPID is therefore frequently referred to as CNO. For the same reason, we are not able to distinguish between different isotopes of a particular ion species. Whereas the regular ion telemetry products on RAPID deliver count rates (and, thanks to calibration, ensuing fluxes) and up to eight broad energy channels for each of the three species (H, He, CNO), the Direct Event (DE) product provides the full information on the energy deposited in the solid state detector for the events (256 energy bins, representing energies between approximately 30 keV to 4 MeV) and the time of flight (80 ns in 256 bins), but only for a limited number of events (detected ions) due to constraints in telemetry.
A third drawback to the DE product is that for reasons of limited on board storage and telemetry bandwidth, only 20 complete DE records per spin can be transmitted in nominal mode, and 106 records per spin in burst mode. Although a priority scheme ensures that the rarer events (heavier ions) are transmitted first, it is not possible to reproduce count rates or fluxes from the DE product. Note also that in the initial years of the Cluster mission, only about 50% of the science data were being downloaded. Starting in 2003, additional ground telemetry stations were made available for the mission, and it was decided to download data from all times. Except for planned maneuvers and scheduling, eclipses and occasional instrument reboots or failures, a near 100% planned data return has been achieved in later years.
Overview of Data By the time of writing, RAPID is still in operation on all spacecraft, but DE results are available from SC1 only until 2007, and for SC3 until 2009. Coverage details along with solar activity are shown in Figure  3. In general, DE data are available whenever RAPID is in operation, that is, essentially all the time except for spacecraft maneuvers or eclipses. We will hereafter focus on measurements from SC4, which has the best coverage with almost continuous measurements from 2001 to 2018. This period corresponds to the peak of solar cycle 23 and almost the complete solar cycle 24. We have stored each individual DE from all four spacecraft in a relational database. Stored quantities from each Cluster spacecraft are: date and time, the polar look direction, the spin sector, the time-of-flight bin (0.
255, corresponding to 0-80 ns), the energy bin (also 0. 255) and Cluster spacecraft position. Information about solar wind, IMF, solar illumination (the F10. 7 index), and geomagnetic activity is stored in a second table in this database. A third table contains the moon position and its magnetic connection to Cluster as well as information about time and position of meteor swarms throughout Earth's orbit around the Sun (see section 4).
For these intervals, we then calculated an occurrence rate as described in section 2. 3 for the years 2002-2018. As a comparison, we also calculated a similar occurrence rate when the Moon was outside the magnetosphere. If lunar sputtering is modulated by the solar wind, one would expect a higher sputtering activity outside the magnetosphere, but the magnetic connectivity to Cluster does not exist (or cannot be established), and Cluster should detect fewer Fe ions. As the moon traverses the magnetotail within a few days, there are obviously far more observations during non-moon-in-magnetotail periods. A comparison between Fe detection rates during these two conditions did not give any meaningful results, however, so while the Moon may be a source of the Fe we detect with RAPID, we are not able to quantify this contribution with our data set.
Summary We have utilized the Direct Event (DE) diagnostics product from the RAPID instruments on board the Cluster satellites to investigate the presence of Fe ions in geospace. The DE product provides full energy and time of flight for a limited number of particles each spin period. From this information, the mass and thus the species of the incident ion can be determined. A prioritization scheme ensures that heavy ions are prioritized. Plotted in an energy versus time-of-flight map, Fe emerges as a pronounced and distinct population, so the identification of ions with atomic or molecular mass close to that of Fe is robust. During the 18 years of observations, approximately 122,000 hr of DE data from Cluster SC4 were analyzed in detail. On average, Fe is detected during 9. 1% of the observation time.
Acknowledgments Cluster RAPID was supported by the  European Space Agency through the Cluster Science Archive  activities and by the  Deutsches Zentrum für Luftund Raumfahrt (DLR)  under Grant  50 OC 1602. I. Dandouras  thanks  CNES  for its support to the Cluster project at IRAP. Cluster data, including the RAPID DE mode data, are available from the  Cluster Science Archive  (https://www. cosmos. esa. int/web/csa).
ACKNOWLEDGMENTS The authors thank  Thomas Fennel  and  Christian Peltz  for enlightening and helpful discussions. Excellent support from  IOAP  and  DESY  machine shops is acknowledged. The experiments received funding from  BMBF  (Grant No. 05K13KT2/05K16KT2 ) and  DFG  (Grant Nos. MO 719/13-1, 14-1 , and  14-2 ). Further support from the  Leibniz Grant  (No. SAW/2017/MBI4 ; D. R. and M. S. ), the  NCCR MUST  of the  SNF  (D. R. and A. C. ), the  Australian Research Council Centre of Excellence in Advanced Molecular Imaging  (No. CE140100011 ; L. F. ), the  Volkswagen Foundation  via a  Peter Ewald fellowship  (T. G. ). , the  U. S. Department of Energy, Office of Science, Basic Energy Sciences, Chemical Sciences, Geosciences, and Biosciences Division , the project  Structural Dynamics of Biomolecular Systems  (No.
This third expedition (COL19) was motivated by the poor sediment recovery of COL17-1 within the upper part of the section (from 0 to 8 m depth). Altogether, we obtained a total cumulative thickness of ∼126 m sediments, reaching a maximum corrected depth of 52 m: 14. 7 m (COL17-1) -51 m (COL17-2) -50 m (COL17-3) -2 m (COL18) -8 m (COL19) (Fig. 2 ). Despite gaps in core sections, we built a composite sequence using all reliable sedimentary intervals from the five holes. Depth adjustments were required to correct for depth shifts among cores induced by distance separating each hole and the different devices used for coring/drilling. We first calculated coring offsets based on fieldwork notebook and data logging of the two cores, COL17-2 and -3, in order to obtain a common depth scale for these two cores.