dataesr/scientific-paragraphs-categorization huggingface dataset:
Clinical data and inclusion for treatment Prior to inoculation all pigs had a respiratory score and a depression score of 0 and a body temperature ≤ 40. 0 °C. The last clinical examination prior to inoculation was performed one hour before the start of the experimental inoculation. Of the 36 nursery pigs included in this study, 35 animals developed typical clinical signs of porcine pleuropneumonia after challenge. One pig stayed clinically healthy without any signs of disease and another one met the criteria for euthanasia prior to treatment. These two pigs were therefore not treated, leaving 34 pigs for the intention to treat population (ITT); 11 pigs in group T1, 12 pigs in group T2 and 11 pigs in group T3. Two pigs were treated despite not fulfilling all inclusion criteria (respiratory scores of 1 instead of 2).
A measurement of the fragmentation functions of jets into charged particles in p+Pb collisions and pp collisions is presented. The analysis utilizes 28 nb -1 of p+Pb data and 26 pb -1 of pp data, both at √ s NN = 5. 02 TeV, collected in 2013 and 2015, respectively, with the ATLAS detector at the LHC. The measurement is reported in the centre-of-mass frame of the nucleon-nucleon system for jets in the rapidity range |y * | <1. 6 and with transverse momentum 45 < p T < 260 GeV. Results are presented both as a function of the charged-particle transverse momentum and as a function of the longitudinal momentum fraction of the particle with respect to the jet. The pp fragmentation functions are compared with results from Monte Carlo event generators and two theoretical models. The ratios of the p+Pb to pp fragmentation functions are found to be consistent with unity.
Event selection and data sets The p+Pb data used in this analysis were recorded in 2013. The LHC was configured with a 4 TeV proton beam and a 1. 57 TeV per nucleon Pb beam producing collisions with √ s NN = 5. 02 TeV and a rapidity shift of the centre-of-mass frame, ∆y = 0. 465, relative to the laboratory frame. The data collection was split into two periods with opposite beam configurations. The first period consists of approximately 55% of the integrated luminosity with the Pb beam travelling toward positive rapidity and the proton beam to negative rapidity. The remaining data were taken with the beams of protons and Pb nuclei swapped. The total p+Pb integrated luminosity is 28 nb -1. Approximately 26 pb -1 of √ s = 5. 02 TeV pp data from 2015 was used. The instantaneous luminosity conditions provided by the LHC resulted in an average number of p+Pb interactions per bunch crossing of 0.
Corrected UE contributions are then subtracted from measured distributions. The maximum size of the UE contribution is 20% for the lowest track p T (or z). The UE from the cone method is compared to an alternative UE estimation and the difference is found to be negligible. The subtracted UE contribution has no azimuthal variation in p+Pb collisions and no UE subtraction is performed for the pp measurement due to negligible UE contribution (less than 2% over the entire kinematic range measured). The measured D(z) and D(p T ) distributions are corrected for detector effects by means of a two-dimensional Bayesian unfolding procedure  [40]  using the RooUnfold package  [41]. The unfolding procedure removes the effect of bin migration due to the jet energy and the track momentum resolutions. Using the MC samples, four-dimensional response matrices are created using the particle-level and reconstructed p jet T , and generator-level and reconstructed track p T (z).
The systematic uncertainty due to the unfolding is typically much larger than the impact of the stability requirement, especially for the lowest p jet T values used in this analysis (discussed in Section 6). Following this criterion, 14 iterations are used for both the p+Pb and pp data sets. The analysis procedure is tested by dividing the MC event sample in half and using one half to generate response matrices with which the other half is unfolded. Good recovery of the generator-level distributions is observed for the unfolded events and the deviations from perfect closure are incorporated into the systematic uncertainties.
The systematic uncertainties from each source are taken as uncorrelated and combined in quadrature to obtain the total systematic uncertainty. The JES uncertainty is determined from in situ studies of the calorimeter response  [37, 42, 43] , and studies of the relative energy-scale difference between the jet reconstruction procedure in heavy-ion collisions and the procedure used in pp collisions  [44]. The impact of the JES uncertainty on the measured distributions is evaluated by constructing new response matrices where all reconstructed jet transverse momenta are shifted by ±1 standard deviation (±1σ) of the JES uncertainty. The data are then unfolded with these matrices. Each component that contributes to the JES uncertainty is varied separately. In total, 45 and 51 independent systematic components constitute the full JES uncertainty in the analysis of p+Pb and pp collisions, respectively.
The magnitude of both of these uncertainties is typically below 5% except for the highest z and track p T bins. The uncertainties related to the track reconstruction and selection originate from several sources. Uncertainties related to the rate of secondary and fake tracks, the material description in the simulation, and the track's transverse momentum were obtained from studies in data and simulation described in Ref. [47]. The systematic uncertainty in the secondary-track and fake-track rate is 30% in pp collisions and 50% in p+Pb. The contamination by secondary and fake tracks is at most 2%, the resulting uncertainty in the fragmentation functions is at most 1%. The sensitivity of the tracking efficiency to the description of the inactive material in the MC samples is evaluated by varying the material description.
Summary This paper presents the first measurement of the jet charged-particle fragmentation functions in a p+A collisions system. The jet charged-particle fragmentation functions are reported for |y * jet | < 1. 6 and p The measurement utilizes 28 nb -1 of p+Pb data and 26 pb -1 of pp data. The pp fragmentation functions are compared to predictions from the Pythia6, Pythia8 and Herwig++ generators. The generators show deviations from the pp data of up to approximately 25%, depending on z and the choice of generator. Pythia8 with the A14 tune and NNPDF23LO PDF set matches the data most closely. The pp D(z) distributions are also compared to two theoretical calculations based on next-to-leading-order QCD and DSS07 fragmentation functions. The calculations are systematically higher than the data and agree generally within 20-30%, with larger deviations at small and large values of z.
Data collection Data were collected through three different sources. At baseline, patients had a structured face-to-face interview with a pharmacist to collect the sociodemographic (birth date, gender, highest educational level completed, coresidence status and number of people living in the subject's household), anthropometrics (weight and height were measured at enrollment by pharmacy staff in order to calculate the body mass index [BMI] which was categorized as underweight: ,18. 50 kg/m 2 , normal: 18. 50-24. 99 kg/m 2 , overweight: 25. 0-29. 9 kg/m 2 and obese: $30 kg/m 2 ) and clinical characteristics (age at time of T2DM diagnosis, usual diabetes outpatient clinical care [eg, primary care, hospitalspecialized diabetes care appointments, private practice], T2DM treatment [dose and prescribed posology of inception GLD and other current and past treatments], diabetes-related complications [eg, retinopathy, nephropathy, diabetic foot], comorbidities and concomitant therapy).
Over the study period, data regarding persistence and adherence (eg, refill dates and medication possession) were collected by an electronic data capture system developed for the electronic extraction of pharmacy records, from the pharmacy where the participant was recruited. Follow-up data also included structured telephone questionnaires conducted 2 weeks, 3 months and 6 months after the reported index date (starting date of the inception-monitored GLD), where information about the real pattern of use was asked. When the subjects were identified as potentially nonpersistent through the pharmacy records database (which was checked prior to each telephone questionnaire administration), the questions for confirmation of nonpersistence status (eg, acquisition of inception GLD from a different pharmacy, and if yes, the number of packages refilled and the corresponding refill dates) were asked.
Data analysis Discrete variables were summarized by absolute and relative counts. Continuous variables were summarized using central tendency measures and dispersion (mean and standard deviation [SD]; median and interquartile range [IQR]). Chisquare test or Fisher's exact test was used to compare the characteristics between subgroups, as well as the distribution of refusals with the distribution of participants. Overall persistence and adherence were calculated through two different methods: Method 1 -using pharmacy records exclusively and Method 2 -using pharmacy records and their combination with patients' self-reported data on persistence collected during the telephone follow-up questionnaires (inception GLD refilled in a different pharmacy). The proportion of pharmacy-loyal patients, defined as those who always refilled the inception GLD at the same pharmacy, was calculated.
Logistic regression was used to explore the factors associated with nonadherence. Univariate and multivariate odds ratio (OR) and Wald's 95% CI were estimated. In the model-building strategy, all variables were evaluated univariately, as the first step. Subsequently, a stepwise selection was implemented (significance levels of 0. 20 and 0. 25 for a variable to enter and to stay in the model, respectively). Model diagnoses comprised the calculation of Hosmer and Lemeshow goodnessof-fit test and VIF. In-depth residuals analysis including plots with deviance residuals was performed. All tests were two-sided and the statistical significance level adopted was 5%. Data analysis was performed using SAS ® software.
ethics and data protection This study was approved by the Portuguese Data Protection Authority (5339/2014) and by the Ethics Committee of the Institute of Public Health of the University of Porto (CE14021), and was conducted in accordance with the ethical principles stated in the Declaration of Helsinki. A written, signed informed consent form was obtained from all participants prior to initiation of any study procedures. This study was registered in the European Network of Centres for Pharmacoepidemiology and Pharmacovigilance E-register of studies (ENCEPP/SDPP/8433).
Adherence and potential factors associated with nonadherence At the end of the study, when adherence estimates considered patient self-reported information (Method 2), 73. 6% (95% CI: 70. 9-76. 3) of participants were adherent (PDC $80%). No significant differences were observed between subgroups ( p=0. 1614). However, when adherence rates were estimated exclusively considering electronic data from pharmacy records (Method 1), a significant higher proportion ), yet no differences were observed between subgroups irrespective of the method used. In the multivariate regression analysis, although differences were found between the two methods, factors associated with nonadherence (Table  4 ) were overall similar with those found in the risk of persistence failure analysis. In both methods, living alone and using an oral inception GLD were factors associated with nonadherence.
Discussion Contrasting with most published studies that use claims databases to measure persistence and adherence, we used an intensive monitoring design that collected information from the first day of drug use and explored the differences between two methods, namely pharmacy records and their combination with patient self-reported data, each yielding different estimates. The use of patient self-reports also allowed us to explore the important features of medication-taking behavior, namely reasons for treatment switching or discontinuation, which are not often captured in administrative databases. As with other chronic diseases, low persistence rates for new GLD were found: 38. 7% considering information exclusively from pharmacy records and 65. 6% in combination with self-reported information.
Almost two-thirds of patients were identified as nonadherent when considering pharmacy records exclusively, which decreased to almost one-quarter of patients, when including patient self-reported data. Overall, considering the persistence and adherence estimates obtained in this study, together with sensitivity analysis   results, we believe that the "true" persistence/adherence rates reported for the new GLD might possibly be between the lower limit of estimates retrieved from pharmacy records exclusively and the upper limit of estimates retrieved from the combination of pharmacy records and patient self-reported information. From this perspective, it is assumed that, on the one hand, patient self-reported information on medicationtaking behavior could overestimate the results. On the other hand, over a 6-month period, given that, in Portugal, patients can go to more than one pharmacy and there is no single database that records all prescription refills from different pharmacies at an individual patient level, estimates retrieved exclusively from pharmacy records could be underestimated.
More than one-third of patients who started the monitored GLD self-declared at least one refill in a different pharmacy where they were recruited. Regardless of these assumptions, at 6 months from therapy initiation, persistence and adherence rates observed, although very low, were in line with, or to some extent lower than, the existent literature. A recent systematic review of observational studies revealed a persistence mean rate to T2DM medication of 56. 2% (95% CI: 46. 1-66. 3). 27 However, it should be noted that the included studies in the review had a longer follow-up period than our study, and pharmacy or prescription records data used in the studies were centralized. Concerning adherence, in another recent systematic review of 27 studies published between 2003 and 2014,  28  it was found that the levels ranged from 38.
5% to 93. 1%, based on surveys on specific instruments or on claims databases. When considering only studies that used the medication possession ratio methodology (in general, for a follow-up duration of 12 months), the prevalence of adherence ranged between 46. 0% and 89. 8%. With regard to factors that could contribute to nonpersistence and nonadherence, irrespective of the differences found between each method, they were broadly in line with what has been described in previous studies elsewhere. When combining pharmacy records with patient self-reported information, it was observed that increasing age was the only factor associated with persistence failure risk. 11 Yet, when analyzing pharmacy records only, patients living alone,  29  living in a suburban/urban setting  30, 31  and using an oral inception GLD had a higher risk of persistence failure.
The later disputes findings from previous studies,  32, 33  although comparison studies using the new GLD for persistence and adherence, are scarce due to its recent availability in the market. The overall clinical demographics of our study participants were generally similar to a large Portuguese T2DM patient cohort analyzed in 2013, in a primary care setting. From the data available, we found that our study population had comparable gender distribution, mean age and similar distribution regarding the most frequent comorbidities. 34 The prevalent new users subgroup, representing almost two-fifths of all participants, had a significant higher T2DM duration, a higher prevalence of diabetes complications, use of insulin and use of specialist care visits, compared with incident new users. This was not surprising, as T2DM is a progressive condition, and therefore, prevalent new users are more likely to be complex patients.
22; 95% CI: 1. 19-1. 26). 37 The positive association between pharmacy loyalty and persistence or adherence has also been demonstrated with other therapeutic groups, such as that including antipsychotic treatment  38  and cardiovascular medication. 39 imilarly, a study from the USA that included older adults with Medicare Part D demonstrated that filling prescriptions at multiple pharmacies, which was a prevalent phenomenon (38. 1% of beneficiaries), was associated with lower medication adherence across a range of chronic medications. 40 ver the study period, almost one-quarter of participants stopped using the inception GLD. These results are slightly lower than some reports from other studies, where a higher stopping rate of 31. 4% was found. 27 Our study adds information on commonly reported reasons for discontinuation from the patients' perspective, which are not frequently captured in database studies.
43, 44 Even when including patient self-reported information, the rates found were still very low; that is to say, the majority of patients did not show reluctance to declare they had stopped taking the medication. Second, persistence estimates were calculated on an individual drug level, and the impact of switching to other antidiabetic medication during the study period was not assessed. Despite the mentioned limitations, this study has several strengths. Data presented were collected through a pharmacy-based intensive monitoring model, which is a noninterventional inception cohort study mirroring the real use of new GLD, prescribed by both general practitioners and specialists, with no limiting inclusion (with the exception of a T2DM treatment indication) or exclusion criteria compared with clinical trials.
Further, although self-selection of pharmacies could have occurred since participation was not mandatory, it seems reasonable to assume that the study sample is representative of the overall country population of TDM2 patients taking new GLD, given the similarities found with the reference population and since pharmacies included were representative of the Portuguese pharmacies. Also, refusals had a similar age and gender distribution as compared to the study participants, and several strategies to minimize patients' nonsystematic selection were put in place during the enrollment period (namely pop-up reminders at the pharmacy software whenever a new GLD was dispensed). Finally, this study provided a good source for persistence/ adherence investigation because, on the one hand, data were collected directly from pharmacy dispensing, rather than physicians' prescriptions that may never be dispensed.
For the sphere, the coefficient correlations depend only on the Reynolds number. High fidelity models involve a tight coupling of the flow field to the flying debris motion. These models use a computational fluid dynamic (CFD) to solve the Navier-Stokes equations around the moving debris in an Eulerian frame of reference  [7, 10] , and the forces and moments are computed from the flow solution. The computational costs of such models are higher than those for models uncoupled from the flow field, especially as the flow Reynolds number increases. However, high fidelity models allow the computation of the trajectory of any kind of shape and do not require experimental databases or correlations for aerodynamic forces and moments. Also, high fidelity models take into account Reynolds number effects and lift and drag fluctuations (10% to 20%) caused by vortex shedding  [11, 12]  in the case of flow separation around debris.
Both models are complementary and need research developments to better assess the risk of impacts. The goal of the present paper is to investigate the relative importance of four dimensionless parameters: the Reynolds number, the Froude number, the Tachikawa number, and the mass moment of inertia parameter. A secondary objective of the paper is to study the effect of launching time on the trajectories. The focus is on the start of the motion, when aerodynamic forces on the ice fragment are high. A trajectory database is built for a 2D rectangular plate at Reynold numbers around 7000, but with other parameter values typical of inflight aircraft deicing. The plate makes an initial angle with the flow field such that flow separation occurs. The CFD solver uses an immersed boundary method (IBM) to model the flow around the plate geometry  [17].
A Cartesian grid is used for the computational domain. The plate boundaries are modelled by adding continuous forcing terms directly to the flow equations using the penalization method  [18] [19] [20]. The paper is organized as follows. First, the penalized Navier-Stokes equations are presented in Section 2, together with the numerical method based on a Vortex-In-Cell (VIC) scheme. The model used to compute the forces and moments on the plate, along with the appropriate governing equations for fluid-solid interactions, is also described. In Section 3, test cases are presented to validate computations of forces and moments against literature results. In Section 4, a database is created to study the influences of the four parameters and initial angle of attack on the trajectories of a 2D rectangular plate.
Plate Trajectories Database. For the parametric study, a sharp corner plate is used, as in Figure  2. During these simulations, the rotation axis of the rectangular plate coincides with its barycenter. The effects of , Fr,   , and Re on flat plate trajectories are studied in order to identify the important parameters at the start of the trajectory computation. A full factorial design with four factors and two levels  [28]  is used to build the test matrix. The simulations are performed using  =  = 1. × 10 -3 and a domain of [-3, 8] × [-3, 5], with the plate's center initially located at (0, 0), and with starting angles of ±/3. For each factor, minimal and maximal values are selected around a base value: Re = 7000,  = 30, Fr = 57. 7, and   = 8. 44 × 10 -2. Except for the Reynolds number, these are the typical values for shed ice pieces around an aircraft in a deicing situation  [29].
For   , two values of the aspect ratio ℎ/ are used, namely, 0. 05 and 0. 15. This is only a 1% variation, but this is a significant thickness change. Table  1  gives the 16 run factors. The 16 runs are executed for two plate starting angles and two release times. Thus, the database consists of 64 trajectories. The starting angle creates a separated flow around the plate. Vortices are shed periodically at low frequencies. The flow is first solved around a static plate, which goes on until the periodic flow is well established. The plate is released into the flow either at time  = 20. 0 s or at time  = 21. 1 s. The dimensionless position   of the plate when it crosses the   = 1. 125 × 10 -3 line will be studied. The computed trajectories are illustrated in Figures  9  and 10. To help visualize the individual trajectories, the results are separated into four.
The two histograms are placed almost symmetrically around the   = 0 line, indicating that gravity is not the dominant force of the problem at the beginning of the motion. The initial plate orientation is the parameter that has the most effect on the trajectories. To study the effects of the other parameters, the database is split into two sets of 32 runs: set one for the starting positive angle, and set two for the starting negative angle. Box plots are used to compare parameter effects  [28]. Figure  14   the box plot and the probability histogram for the results of the flat plate with a starting negative angle. The central mark is the median of the 32 runs. The edges of the box are 1, the 25th percentile, and 3, the 75th percentile. The whiskers extend to 1 -1. 5(3 -1) and 3 + 1. 5(3 -1), or the most extreme data points not considered outliers.