dataesr/scientific-paragraphs-categorization huggingface dataset:
Screening schedule The screening was usually completed in four separate visits within a 90-day window. On certain occasions, this timeline was extended up to 180 days to allow for treatment of transient conditions, laboratory retesting and scheduling of other screening assessments. This allowed time for results to be received and evaluated against study eligibility criteria. Any clinically significant findings were passed on for follow-up to the participant's GP. Participants who were determined to have an active unstable illness, as defined by the inclusion/exclusion criteria, were excluded. Screening involved collection of demographic data which included age, ethnicity, education and occupational status. During screening, potential participants completed cognitive tests, the G-PACC and RBANS, and CDR including the study partner interview.
Data management The CPSS is conducted in accordance with Good Clinical Practice (GCP) Guidelines as such data are recorded and stored in a way that could be verified and reported in an accurate manner. All essential documents are filed in the trial master file/investigator site file. Source documents are kept in both paper and electronic formats. The main Electronic Data Capture system used in the current study is Medidata Rave. Both paper and electronic data are subject to daily and monthly internal audits based on standard operating procedures (SOPs). In addition, the investigator site files, paper source documentation and electronic source data are routinely monitored to maintain data accuracy collection to the highest degree. Open access participants to be no wider than 0. 72, assuming that the sample mean follows a Gaussian distribution.
Dietary patterns and other lifestyle variables will also be assessed to consider the impact of environmental exposures on AD development. Therefore, the CPSS will also allow for the exploration of environmental and lifestyle predictors of cognitive decline and impairment. The uniqueness of this study lies in its breadth and frequency (every 3 months) of assessments, as well as the planned explorations and comparisons of proposed cognitive composites for AD detection and tracking. The prolonged and detailed follow-up data offers opportunities for precise disease modelling and the evaluation of several methodological controversies within clinical trial design, such as the influence of practice effects on cognitive performance, in addition to mechanisms of reserve and resilience against cognitive senescence.
Acknowledgements The authors wish to thank the participants who continue to volunteer their time and commitment towards the CPSS. We thank the staff at various sites who assist in the collection of biological, cognitive and other clinical data, and we thank the sponsor for funding this work. Imperial College London is grateful for support from the  NIHR NW London Applied Research Collaboration. Contributors LM served as the ICL study site principal investigator. CWR served as the EDI study site principal investigator. CTU-M served as the lead author, with TW as co-lead. CTU-M, MK, JC, RP, GP, CAd-JL and HW were coinvestigators at  School of Public Health, Imperial College London. KR was coinvestigator at  Centre for Dementia Prevention, University of Edinburgh. All principal and coinvestigators contributed to the study design, coordination, data acquisition, development and critical review of the manuscript.
Overview of the CFD study The present study makes extensive use of CFD methods, through the ANSYS Fluent software, for the study of the PV system. The geometrical models are based on the existing design of the CFF general ventilation solution  [17]  with the PV circuit added in two different configurations: (1) frontal PV emplacement (Fig. 3a ) at 2D e in front of the occupant's nose and (  2 ) with the PV placed laterally (Fig. 3b ) at the same distance of 2D e from the face. The frontal PV solution, in addition to the study of the PV system itself, will be used to validate the CFD model's ability to reproduce the PV diffuser jet development by comparing it with the experimental velocity field measurements. For reasons of computational efficiency, the numerical study will use a previously studied methodology  [16, 17]  of simulating the ventilation circuit (called ventilation models -VM, in the present paper) without the occupant in the CQ by imposing the CFF operating curves as boundary conditions and thus obtaining velocity profiles at the diffuser grille and the PV duct connected to the plenum.
Funding: N. S. has received funding from the  European Union 's  Horizon 2020 research and innovation programme  under the  Marie Skłodowska-Curie  grant agreement No [ 846387 ]. This work was financed by  ANR - MAPKSEED  ( ANR-18-CE20-0019 ) and  ANR-KABAGRAPE  ( ANR-20-CE20-011 ). The IJPB also benefits from the support of  Saclay Plant Sciences-SPS  ( ANR-17-EUR-0007 ).
However, some of the collisions may still lead to a further gain in mass, even though the sticking threshold has been overcome, e. g. , by mass transfer in high-velocity collisions (see  [90] , and references therein). [55]  described how cometary nuclei might have formed in such a scenario and compared it to the findings of Rosetta. An alternative scenario was presented by  [30]  who collected evidence from Rosetta data that the comet nucleus might still consist of the primordial pebbles, with fractal dust aggregates captured between the pebbles  [75] , which indicates that it formed by the gentle gravitational collapse of a concentrated cloud of pebbles  [111]  (Fig. 1 ). Without further collisional evolution, these two formation scenarios predict very different physical properties of the resulting planetesimals, e.
A major deficiency in Rosetta data is the lack of constraints on the physical properties of the surface and subsurface layers, and the relationship of volatiles and refractories at depth. This information forms an important input to our understanding of cometary activity, and is necessary to relate coma properties, such as chemical abundances and the dust-to-gas ratio, to intrinsic nucleus properties. Thermophysical parameters strongly depend on microphysical details such as, the material composition, the morphology and structure of the material (arrangement of the material, coordination number, void spaces) and the mixing of the different components (mineral phases, organics and ices)  [68]. One can envisage ices surrounding the dust component, ice matrices within dust matrices, and ices as a component isolated from the refractories.
Introduction In 2005, an estimated 1. 6 million persons were living with the loss of a limb in the United States. 1  Children, 18 years and under, accounted for 1. 6% of the total with an estimated 25,000 children living with limb loss. 1  Congenital limb deficiency, trauma, and cancer diagnoses are the main indications for amputation in children. McLarney and colleagues used a database of commercially insured children to estimate the rates of major lower extremity limb loss. They found a prevalence of 38. 5 cases per 100,000 per year between 2009-2015. 2 ongenital limb absence or difference accounted for 84% of the cases, followed by trauma at 13. 5%. 2  Although this diagnosis may initially be devastating to the family, with care from the multidisciplinary medical team, these children tend to do well.
6 Data on residual limb pain in pediatric patients is limited. Both targeted muscle reinnervation (TMR) and regenerative peripheral nerve interface (RPNI) were initially developed to control upper limb neuroprosthetic devices (see Upper Limb Prosthetics below); however, it was observed that adult patients reported less limb pain following these procedures. As a result, TMR and RPNI have gained popularity to prevent and treat both PLP and neuroma-related pain. For TMR, transected sensory and mixed motor nerves are transferred to nearby redundant motor branches allowing the severed nerves to grow in an organized fashion rather than form a neuroma. This can be done at the time of index amputation or in a staged fashion. A prospective randomized control study on adults showed improvement in PLP and residual limb pain at one year in the TMR cohort compared to those with cut nerves buried into muscle.
Analysis plan and statistical power All analyses plans were pre-defined and reviewed by the PI, the DO-HEALTH epidemiology team, and the head biostatistician. No interim analysis was planned, and none was requested by the Data Safety and Monitoring Board. All necessary programs for the analyses and for the preparation of datasets were coded in the SAS statistical language and stored as electronic SAS files in the designated storage folders. The epidemiology team at the DO-HEALTH coordinating center will conduct all statistical analyses using SAS v9. 4 statistical software (Copyright© 2004 by SAS Institute Inc. , Cary, NC, USA). To account for multiple comparisons, the significance threshold will be set at p<. 01 for the primary outcomes. In analyses for all other endpoints, a p-value of 0.
Analysis of treatment effects In this 2×2×2 factorial design, the primary aim is to compare the main effects and the combined effects of vitamin D, n-3s, and the SHEP on the 6 primary endpoints. All analyses will be performed based on intent-to-treat, so that all available data will be included for all study participants. Our general approach is to use longitudinal linear regression models for continuous outcomes (systolic and diastolic blood pressure, MoCa and SPPB), with changes from baseline at 1, 2, and 3 years as outcomes. Generalized estimating equations with an unstructured correlation matrix will be used to account for correlation within participants, over time. The models will include fixed effects for the interventions, as well as adjust for the stratification variables (study center, age, sex, prior fall), the appropriate baseline outcome measure, time as a categorical predictor, and baseline BMI as a pre-defined potential confounder.
Power The planned sample size for this study was N = 2152 older adults (Table  3 ). Based on prior experience and results of pilot studies the dropout rate was estimated at 32%, while 68% of participants were expected to complete the entire 36-month follow-up. Since the trial analysis is based on the intention-to-treat principle, partial study data would be available from the 32% of subjects who are expected to drop out early. Therefore, the estimated effective sample size for the analysis was 1807 people: full follow-up data on 68% of the 2152 enrolled older adults (0. 68 × 2152 = 1463), plus an average of half follow-up on the 689 of subjects who were expected to withdraw early (0. 5 × 0. 32 × 2152= 344). All assumptions about means and standard deviations of the parameters were based on the results of pilot trials to DO-HEALTH  37, 63, 87  and research of other authors  [88] [89] [90].
largest European healthy aging trial including 5 European countries. , were selected to be well-tolerated and affordable, allowing, if proven effective, to be implemented at a large-scale public health level. DO-HEALTH has several strengths. It targets the relatively healthy 70+ age segment of the population expected to grow rapidly in the coming years, at an age where disease rates are increasing exponentially. Further, as a healthy aging trial, it is powered for 6 primary endpoints DO-HEALTH was approved by local/national ethics committee and regulatory authorities of all 5 countries (Switzerland, Germany, Austria, France, Portugal). DO-HEALTH was a Phase III trial (therapeutic confirmatory) and was conducted according to the ICH GCP Guidelines (CPMP/ICH/135/95). Trial registration and funding DO-HEALTH is registered under the protocol NCT01745263 at the International Trials Registry (clinicaltrials.
gov), and under the protocol number 2012-001249-41 at the Registration at the European Community Clinical Trial System (EudraCT). DO-HEALTH was funded by the European Commission Framework 7 Research Program (project number 278588), the University of Zurich (Chair of Geriatrics and Aging Research), and independent, investigator initiated funds provided by DSM Nutritional Products, ROCHE Diagnostics (SCHWEIZ), NESTEC, Pfizer Consumer Healthcare, and STREULI Pharma. The funders had no role in the DO-HEALTH study design or writing of the report. Discussion Word count: 831 (max. 1500 words) DO-HEALTH is the The goal of the DO-HEALTH trial was to address 6 primary endpoints (systolic and diastolic blood pressure, non-vertebral fractures, SPPB 34 , MoCA 35 , and infections) relevant to healthy aging among community-dwelling adults age 70 years and older.
Abdourahim Abdillah: Conceptualization; Data curation; Investigation; Formal analysis; Methodology; Visualization; Writing the original draft. Stéphane Ranque: Conceptualization; Methodology; Investigation; Formal analysis; Resources; Supervision; Writing -review & editing. This research was funded by the Institut Hospitalo-Universitaire (IHU) Méditerranée Infection, the French National Research Agency under the program "Investissements d'avenir", reference ANR-10-IAHU-03, the Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI.
Author Contributions:Funding: Abdourahim Abdillah: Conceptualization; Data curation; Investigation; Formal analysis; Methodology; Visualization; Writing the original draft. Stéphane Ranque: Conceptualization; Methodology; Investigation; Formal analysis; Resources; Supervision; Writing -review & editing. This research was funded by the Institut Hospitalo-Universitaire (IHU) Méditerranée Infection, the French National Research Agency under the program "Investissements d'avenir", reference ANR-10-IAHU-03, the Région Provence Alpes Côte d'Azur and European funding FEDER PRIMI.
Context. Parker Solar Probe (PSP) measures the magnetic field and plasma parameters of the solar wind at unprecedentedly close distances to the Sun. These data provide great opportunities to study the early-stage evolution of magnetohydrodynamic (MHD) turbulence in the solar wind. Aims. In this study, we make use of the PSP data to explore the nature of solar wind turbulence focusing on the Alfvénic character and power spectra of the fluctuations and their dependence on the distance and context (i. e. , large-scale solar wind properties), aiming to understand the role that different effects such as source properties, solar wind expansion, and stream interaction might play in determining the turbulent state. Methods. We carried out a statistical survey of the data from the first five orbits of PSP with a focus on how the fluctuation properties at the large MHD scales vary with different solar wind streams and the distance from the Sun.
Instruments & data processing There are four instrument suites onboard PSP. Here we make use of the Level-2 magnetometer (MAG) data from Fields Experiment (FIELDS) and Level-3 Solar Probe Cup (SPC) data from Solar Wind Electrons Alphas and Protons investigation (SWEAP). We refer to the five orbits of PSP as "Encounter 1, 2, 3, 4, 5," respectively, or "E 1, 2, 3, 4, 5" for short, as high resolution data are only produced near perihelions of the orbits (R ≤ 0. 3 -0. 4 AU). During the encounters, SPC measures the proton spectrum at a cadence of 0. 218-0. 874s and the time reso-   Row (d)  shows the spectral slopes of the magnetic field in Alfvén speed (blue) and velocity (orange). The two dashed lines mark the values 3/2 and 5/3 for reference. Row (e)-(h) are σ c , σ r , E b , and E v , respectively, as defined in Section 2.
Overview of Encounter 1, 4, & 5 In Fig. 1  we present the overview plot of Encounter 1 (left), 4 (middle), and 5 (right). We did not plot Encounter 2 & 3 due to the limited figure size and less data coverage during the two encounters. All quantities were calculated, either averaged or Fourier-analyzed, in the 2048 × 0. 874s ≈ 30 min time window as described in Section 2. Consequently, the magnetic switchbacks, whose typical time scales are several minutes long (Dudok de Wit et al. 2020), are absent from the magnetic field plot. We note that the large gaps in the last four rows (σ c , σ r , E b , and E v ) of the middle and right columns do not mean that the original SPC and MAG data have large gaps. The reason is, as mentioned in We note that the variation in latitude is small. The colors represent time such that PSP moves from the light-colored end to the dark-colored end.
Section 2, that we discarded the half-hour time windows with data gap ratios larger than 10%. So these large gaps are actually a result of frequent small data gaps in Encounters 4 & 5. There are several points that are worth underscoring here. (1) From Row (g)&(h), we can see that both magnetic and kinetic energies in the waves decrease as we move away from the Sun. This is a natural result, mainly of the spherical expansion of the solar wind but also of the energy cascade of the turbulence. A similar trend of E o and E i was reported in  (Chen et al. 2020 ). (2) The streams measured by PSP during Encounters 4&5 are  mostly of a very low speed (Row (b)). As is shown in Section 3. 2, the streams are actually still accelerating radially. (3) The ion thermal speed (Row (b)), or equivalently the square-root of ion temperature, is highly correlated with the radial flow speed, which is a well-known phenomenon that has already been observed by other satellites  (Grappin et al.
Thus, these kinds of measurements made uniquely by PSP can be used to quantify the acceleration of the solar wind in the future with increasing data volume. A couple of caveats should be noted here. First, in the corotating frame of the Sun, there is an additional longitudinal speed of the wind. Thus, the stream measured by PSP at a certain longitude should come from a region located at a larger longitude on the solar surface. For example, a 300 km/s wind drifts along longitude by ∼ 17 • after it propagates 50R s. Apparently, streams of different velocities and measured at different distances should drift by different amounts in longitude. In addition, considering the wind is accelerating, it is even more complicated to estimate this longitudinal drift. Second, the variation of the latitude of PSP, though not very large, may also account for the deformation of the longitudinal profiles of the measured streams.
Boldyrev et al. (2011)  conducted 3D incompressible MHD simulations based on the reduced equation set of Elsässer variables and they reproduced the different magnetic field and velocity spectra statistically. However, how the final status is established is still unknown from the simulations. In addition, PSP data show that the steepening of the magnetic field spectrum is quite slow. The top-left panel of Fig. 5  implies the steepening from 3/2 to 5/3 takes time for the wind to travel from R ∼ 30R s to R ∼ 70R s. This is much longer than the nonlinear time, that is the "eddyturnover" time or the Alfvén crossing time, of the turbulence. Thus, it is possible that in the solar wind, the differential evolution of B and V is controlled by some external mechanisms, such as stream shears and the spherical-expansion effect, which leads to different decay rates of the magnetic energy and kinetic energy  (Grappin & Velli 1996).
2020). (3) Closer to the Sun, σ c increases toward 1, confirming that the turbulence is dominated by outward propagating Alfvén waves in the young solar wind. However, there are periods where σ c is quite low even at very close distances to the Sun (below 35R s ). (  4 ) For some solar wind streams, for example, those observed during Encounters 4&5, σ r evolves from negative values toward 0 at close distances, suggesting that the turbulence is actually magnetic-dominated at its origin and then gradually relaxes to a more balanced status in these streams. The above conclusions are based on the statistical results using all high-resolution data from PSP's first five encounters. While they help us depict an average picture of the evolution of solar wind turbulence, it is still necessary to examine the turbulence from different time periods so that we can have deeper insights on how the turbulence varies in different streams.
2020). In Fig. 10 , we plotted these two quantities for Encounter 5, where blue is the SPC ion density n p and orange is the QTN electron density n e. We can see that n p is systematically lower than n e and the difference can be as large as ∼ 30% for some time periods. As we expect that the QTN measurements are more accurate than the SPC measurements, this indicates that the real ion density is larger than the SPC data used in the current study. As a result, the magnetic energy density E b = b 2 /µ 0 ρ calculated here is larger than real, leading to an overestimate of the magnetic energy excess over the kinetic energy. Thus, we used the QTN-derived density to reconduct the calculation of the magnetic energy, σ c and σ r. The result is not presented here but we confirm that the effect of this density difference is not significant and does not change the low-Alfvénicity in E5.
In Fig. 11 , we show the SDO/HMI image of the whole disk of the Sun taken on June 16, 2020. During most of Encounter 5, PSP was flying over this side of the Sun, which is very quiet as can be seen from the image. We note that this image was not taken during the period that PSP data were analyzed (May 30-June 13, 2020) since PSP was not on the Sun-Earth line during E5 so there is a time lag between the encounter and when SDO was looking at the solar surface over which PSP flew by. In the right panel of Fig. 11 , we show the map of magnetic pressure at R = 1. 2R s , which was calculated using the PFSS model with the source surface set to R ss = 2. 5R s and the SDO/HMI measurements as input. The blue diamond is the direct radial projection of PSP to the source surface and the blue crosses are the foot points of the magnetic field lines connected to PSP on the source surface.
Conclusions In this study, we have analyzed data from the first five orbits of PSP. We focus on the properties of the MHD-scale turbulence and how they vary with the large-scale solar wind streams. A general nonlinear steepening of the magnetic field spectrum from a -3/2 slope toward -5/3 slope is observed statistically. The progress of the steepening depends on both the wind speed and the radial distance to the Sun, suggesting the existence of a "turbulence age" that controls the steepening process (see Fig. 5 ). The slope of velocity spectrum, on the contrary, remains almost constant at -3/2. The observed spectral evolution indicates that, on average, the magnetic field and velocity have similar spectra in the very young solar wind and their spectra evolve differently. Better theoretical models are still needed to explain this differential evolution of velocity and the magnetic field and they will be a future research topic.
Background Assessing the quality of care has become mandatory in many healthcare systems and is an intrinsic component of quality improvement. In 2017, the European Society of Cardiology (ESC) Association for Acute Cardiovascular Care (ACVC) published a position paper defining quality indicators (QIs) for acute myocardial infarction (AMI)  1  with the aim of supporting quality improvement, and based on the assumption that rigorous measurement is fundamental. This was the first QI initiative undertaken within the ESC by one of its constituent associations, concordant with the mission statement of the ACVC to 'improve the quality of care of patients with acute cardiovascular disease'. The ACVC Study Group on QIs decided that QIs should not only reflect high-grade recommendations in ESC guidelines but also should consider the domains of care for which there is potential room for improvement, and where measurement can be performed using existing registries or databases.