dataesr/scientific-paragraphs-categorization huggingface dataset:
Analytical Strategy Energy and nutrient intakes for NHANES participants were calculated using the Food and Nutrient Database for Dietary Studies 2011-2014. The primary nutrient outcome measures were selected based on their overall importance to current dietary recommendations  [29]. Some of the nutrients were in the NRF model but some were not. For example, fiber, vitamin D, calcium, magnesium and potassium (all in the NRF model) were identified in the 2010 Dietary Guidelines for Americans as nutrients of concern  [29]. Iron (also in the model) was identified as a nutrient of concern for adolescent girls and women capable of becoming pregnant. By contrast, the NRF model did not include nutrients of concern such as folic acid (women capable of becoming pregnant) or vitamin B12 (older adults)  [19, 24].
Breakfast food groups of interest were based on reported consumption frequency by children and adults and included milk, whole fruit and fruit juices, whole grains and low-fat dairy, soy, nuts and legumes, as well as ready to eat cereals (RTEC). All analyses were conducted using SAS software, Version 9. 4 (SAS Institute Inc. , Cary, NC, USA) and are representative of the US population. Differences between proportions were tested using X 2 tests. Differences in quantitative variables (such as intakes) were tested using Generalized Linear Models, adjusted as appropriate (without and with adjustment for energy at breakfast as well as energy at breakfast and socio-demographics characteristics). Pearson coefficient correlations between NRF9. 3 and HEI score, as well as between NRF9. 3 and all HEI subscores, were estimated.
Discussion The present analyses of first-day data from the 2011-2014 NHANES data asked what breakfast food patterns were associated with most nutrient-rich diets  [1, 4, 6, 19, 36] ? Shifting the focus from individual nutrients to food choices and food patterns can pave the way for future food-based dietary recommendations and guidelines. The 2015 Dietary Guidelines Advisory Committee has already delineated the relation between food patterns and health outcomes  [29] ; the emphasis on healthy food choices and healthy food patterns is likely to continue. First, breakfast consumption contributed to overall DQ. Four out of 5 of NHANES participants in the 2011-2014 database ate breakfast on the first day of dietary data collection. Breakfast consumption was associated with higher socioeconomic status and also with higher-quality diets.
Such desirable food groups may include fruits, nuts, seeds, whole grains and low-fat dairy. The notion of what constitutes a healthy food is being revisited by the US Food and Drug Administration. The limitations of this study are worth noting. First, all population based dietary data in the US and the 5 other countries were based on self-report. While self-reports may not reflect true dietary intakes, the fact is that most representative population based dietary intake data globally are based on self-report. Second, data analyses were based on the first day of the 2-day NHANES survey. One day recalls are a reliable way to assess nutrient intakes of populations but do not capture the habitual dietary patterns of the individual. Better able to address habitual dietary patterns are the national dietary surveys in France, based on 7-day diaries and those in the UK, based on 4 days.
Acknowledgments: The analyses of federal data were supported by the  General Mills Bell Institute of Health and Nutrition. The sponsor had no role in the design and conduct of the study; the collection, management, analysis and interpretation of the data; or the preparation or approval of the manuscript. C. D. R. and F. V. had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis. A. D. has received grants, honoraria and consulting fees from numerous food and beverage companies and other commercial and nonprofit entities with interests in nutrient density of the diet. C. D. R. has no conflict of interest to declare. F. V. is employee of  MS-Nutrition.
The result is a new data structure, the Visibility based WSPD (V-WSPD), that handles visibility to further reduce the number of shadow rays needed to compute indirect illumination. We show that this additional visibility criterion allows to significantly speed up rendering while keeping a similar image quality. Therefore, our main contributions can be summarized as follows: • an efficient data structure, the V-WSPD, optimized for fast rendering with a large number of VPL; • a view-independent construction algorithm, allowing to reuse the V-WSPD for a full sequence of images in case of static scenes; • an analysis of the performance scalability w. r. t. the visibility approximation accuracy. This paper is organized as follows: Section 2 reviews the existing many-light rendering techniques related to our approach; Section 3 gives the theoretical background introducing our work; Section 4 details our new data structure, the V-WSPD; Section 5 presents and discusses experimental results; Section 6 concludes and gives several future work lines.
CONCLUSION AND FUTURE WORK We have presented a new data structure for VPL clustering: the Visibility based WSPD (V-WSPD). It rede-fines the work of  (Bus et al. , 2015a)  on the WSPD by driving the construction with visibility queries (along with the existing geometric factor), while remaining view-independent. We have shown that the V-WSPD allows to achieve far better rendering times than the pure geometric WSPD while keeping a similar image quality. This improvement implies an increase in construction times, since it requires to cast rays to determine visibility. But, given that the V-WSPD is viewindependent, it could be reused for a full sequence of images, so that the loss in construction times is quickly amortized. Compared to the state-of-the-art method, Illumi-nationCut  (Bus et al.
Background: Observational research is essential to evaluate the real-life effectiveness of asthma treatments and can now make use of outcomes derived from electronic medical records. Aim: The aim of this study was to investigate the utility of several database outcome measures in asthma. Methods: This study identified cohorts of patients with active asthma from a UK primary care database -Optimum Patient Care Research Database -approximately 10% of which was prospectively supplemented with questionnaire data. The "Questionnaire cohort" included patients aged 18-60 years with valid questionnaire data and 1 year of continuous primary care data. Separate "ICS initiation" and "ICS step-up" cohorts included patients aged 5-60 years initiated on inhaled corticosteroids (ICSs), who had 1 year of continuous primary care data before, and after, this index visit.
Database measures of asthma symptom control and exacerbations were identified in the Optimum Patient Care Research Database and cross-tabulated with corresponding patient-reported (questionnaire) data. Responsiveness of the database outcomes was analyzed, using McNemar's and Wilcoxon's signed rank tests, and Poisson regression was used to estimate the association between database outcomes and future risk of database exacerbations, in the ICS initiation cohort. Results: The final study included 2,366 Questionnaire cohort patients and 51,404 ICS initiation patients. Agreement between patient-reported and database-recorded exacerbations was fair (kappa 0. 35). Following the initiation of ICS, database risk domain asthma control (based on exacerbations) improved (proportion of patients with uncontrolled asthma decreased from 24.
9% to 18. 6%; P<0. 001) and mean number of database exacerbations decreased from 0. 09 to 0. 08 per patient per year (P=0. 001). However, another measure of asthma control which includes short-acting beta-agonist prescription as part of the definition did not show this improvement. Patients with prior exacerbations had a higher risk of future exacerbation (rate ratio [95% confidence interval], 3. 23 [3. 03-3. 57]). Conclusion: Asthma control and exacerbations derived from primary care databases were responsive, with the exception of short-acting beta-agonist prescriptions, and useful for risk prediction.
6, 7 Tools such as Asthma Control Test (ACT), Asthma Control Questionnaire (ACQ), and the RCP Three Questions have been developed for assessing symptom control in clinical practice, and some (ACT, ACQ) are also recommended for use in RCTs. 8 There are no standardized tools for asthma risk stratification in clinical practice or RCTs, but some models have been described. 9, 10 ne of the strongest predictors of future exacerbations, independent of recent symptom control, is the occurrence of one or more exacerbations in the previous 12 months. 11 ublished asthma database studies have been designed to utilize outcomes that capture clinical care realities and reflect guideline-based definitions. 12, 13 The appropriateness and utility of these database outcomes, however, have not been discussed in the literature to the same extent as traditional asthma RCT outcomes.
14 Critical appraisal of database outcomes is necessary to establish their strengths and weaknesses, and consequently the strength of the resulting evidence, and to identify opportunities for refinement and improvement. Sustained quality assurance within observational research is essential to the establishment of meaningful, trusted and standardized outcome measures for the field. Furthermore, the development of standardized and consensually accepted database outcomes could improve research efficiencies and assist health care professionals in interpreting the clinical relevance and implications of reallife research findings. 15 7] [18]  In particular, we investigated the following: 1) how useful these database outcomes were for providing insights into asthma control, compared to information gained from patient-reported questionnaires; 2) how well the database outcomes performed in assessing response to effective treatment; and 3) how well the database outcomes predicted future asthma exacerbations.
Data source and permissions This study used data from the UK's Optimum Patient Care Research Database (OPCRD)  19  -a primary care database containing anonymized, routinely recorded clinical data from over 4. 5 million patients. More than 600 primary care practices across the UK contribute routine EMRs to the OPCRD, and ~10% of patients with asthma within the database have complementary, linked patient-reported outcomes (PROs) captured through self-completed asthma questionnaires that were routinely mailed to patients after a clinical visit. The OPCRD is maintained by Optimum Patient Care and has ethical approval from the National Health Service (NHS) Research Authority to hold and process unidentifiable research data (Research Ethics Committee reference: 15/EM/0150). This study was approved by the Anonymised Data Ethics & Protocol Transparency (ADEPT) Committee, which is an independent scientific advisory committee commissioned by the Respiratory Effectiveness Group for the OPCRD (REG; www.
study aims To explore the clinical utility of asthma database outcome measures, we evaluated several outcomes from the published literature within the OPCRD  [16] [17] [18]  and compared them to semantically similar PROs, assessed their responsiveness to efficacious asthma therapy (inhaled corticosteroids [ICSs]) via change in end points before and after treatment initiation and step-up, and evaluated their association with future asthma exacerbation risk.
Database outcomes versus PROs To compare database outcomes and PROs, data from routine care EMRs were extracted within a week of patients being invited to complete an asthma questionnaire. PROs were obtained directly from questionnaires posted to the patients at the time their practice data were inputted to the OPCRD (typically returned within a month of questionnaire mailing), and comparator database outcomes were evaluated from routine care EMRs over the prior 12 months (a 13-month evaluation period in total). The patient cohort with linked EMR/PRO data is hereafter referred to as the "Questionnaire cohort".
Database outcomes: responsiveness and future risk Separate cohorts were used to investigate the responsiveness of asthma database outcomes to efficacious therapy and to assess the association between database measures and future asthma risk over a 2-year study period. The database outcomes were evaluated over one baseline year prior to an index date, defined as either 1) the date of a patient's first ICS prescription ("ICS initiation cohort") or 2) the date at which a patient received a ≥50% increase in the maintenance ICS dose ("ICS step-up cohort"). The same database outcomes were assessed in the year immediately following ICS initiation or step-up. Responsiveness was assessed through the evaluation of changes in outcome measures from baseline to outcome years (pre-/post-therapeutic intervention).
inclusion and exclusion criteria Patient data for the Questionnaire cohort were extracted over the period 2010-2013. Patients were included if they had an asthma diagnosis recorded in their EMR, with no subsequent "asthma resolved" Read code; had data available for extraction up to at least the year 2010 (to allow ≥13 months' complete outcome data at the time the study was conducted); were continuously registered in a practice throughout the study period; received at least one respiratory prescription in the 2 years prior to the index date; had full questionnaire data linked to their EMRs; and were aged 18-60 years at the time of index date. Patient data for the ICS initiation and step-up cohorts were extracted over the period 1985-2011. Patients were included if they were aged 5-60 years at the index date; had a Read code "asthma diagnosis ever"; and had at least 2 years' continuous data (at least 1 year before and 1 year after the index date).
Real-life database outcomes We examined the properties of three asthma-related database outcomes: a measure of asthma exacerbations and two measures of asthma control as described in brief below. Full details of outcome definitions can be found in the Supplementary materials. Database severe exacerbations were defined based on the American Thoracic Society/European Respiratory Society (ATS/ERS)  14  Task Force definition, as the occurrence of any of the following during the assessment period: asthmarelated hospital admission or emergency department (ED) attendance, or an acute course of OCS with evidence of respiratory review. Two database measures of asthma control, as defined in previously published asthma studies,  [16] [17] [18]  were evaluated: 1) risk domain asthma control (RDAC) and 2) overall asthma control (OAC).
comparison of PROs and database outcomes Database asthma control and exacerbation measures were cross-tabulated with corresponding patient-reported measures. The agreement between database-defined asthma control (RDAC and OAC) and patient-reported control was assessed using Somers' d-statistic, which estimates the improvement in predicting one variable when using the value of a second variable. 23 The agreement between databasedefined exacerbations and patient-reported exacerbations was assessed using Cohen's kappa coefficient, which estimates the percentage of agreement between two variables that is not expected by chance. 24.
Prediction of future outcomes Poisson regression was used to assess the association between baseline asthma control, or their disaggregated components, and exacerbations during the outcome year. Variables were fitted univariably, and unadjusted rate ratios (RRs) with 95% confidence intervals (CIs) were estimated. For the main analysis, this was carried out separately in the ICS initiation and step-up cohorts. A sensitivity analysis was carried out in a subgroup of the ICS step-up cohort -patients who had stable therapy in the outcome year, defined as no add-on therapy and/or a ≥50% increase in the existing ICS dose. All analyses were carried out using IBM SPSS Statistics version 21 (IBM Corporation, Armonk, NY, USA), SAS version 9. 3 (SAS Institute, Cary, NC, USA), and Microsoft Office Excel 2013 (Microsoft Corporation, Redmond, WA, USA).
Questionnaire cohort Questionnaire cohort patients had a mean age of 44. 4 (SD, 11. 1) years, 40. 5% were male, 29. 7% had a body mass index [BMI] of 30 or above and 24. 6% were current smokers (Table  1 ). There was large disparity between the proportion of patients with a database-recorded diagnosis of rhinitis (8. 5%) and the proportion with patient-reported rhinitis symptoms (80. 8%). During the study period, <1% of patients had asthma-related hospital attendance, according to EMRs. Two or more prescriptions of acute OCS with evidence of respiratory review were identified in 2. 2% of patients via the database, while two or more instances of OCS use were reported by 8. 7% of patients.
ics initiation cohort Patients initiating ICS at the index date had a mean age of 29. 1 (SD, 17. 0) years, 46. 5% were male, 21. 7% had a BMI of 30 or above, and 14. 4% were current smokers Notes: a BMi data were missing for 1. 82% of patients; smoking status was unknown in 0. 1% of patients. b asthma-related refers to records coded with a lower respiratory code, including asthma and lower respiratory tract infection codes, or a generic hospitalization Read code which has been recorded on the same day as a lower Respiratory Consultation (defined in the supplementary materials). c Defined as all courses that are definitely not maintenance therapy; and/or all courses where dosing instructions suggest exacerbation treatment (eg, 6, 5, 4, 3, 2, 1 reducing, or 30 mg as directed); and/or all courses with no dosing instructions, but unlikely to be maintenance therapy with a code for asthma or a lower respiratory event; and/or no or undefined dosing instructions but definitely not maintenance therapy, where "maintenance therapy" is defined as daily dosing instructions of ≤10 mg prednisolone or prescriptions for 1 mg prednisolone tablets.
ics step-up cohort Patients stepping up existing ICS therapy at the index date had a mean age of 28. 9 (SD, 18. 0) years, 48. 0% were male, 21. 9% had a BMI of 30 or above, and 14. 6% were current smokers (Table  2 ). Less than 1% of patients had any asthma- Notes: a BMi data were missing in 25. 1% of patients in the ics initiation cohort and 22. 9% of patients in the ics step-up cohort; smoking status was unknown in 4. 8% of patients in the ics initiation cohort and 3. 7% of patients in the ics step-up cohort. b asthma-related refers to records coded with a lower respiratory code, including asthma and lower respiratory tract infection codes, or a generic hospitalization Read code which has been recorded on the same day as a Lower Respiratory Consultation (defined in the supplementary materials).
Questionnaire cohort: database outcomes versus PROs asthma control Two-thirds of patients (67. 7%) whose asthma risk was classified as "controlled" in the baseline year according to the database measure RDAC were also classified as having "controlled" (either well or partly) symptoms in the previous 1-4 weeks, based on patient-reported symptom control (Figure  1 ). Among patients classified as "uncontrolled" over the previous year in terms of RDAC and OAC, fewer than half were also considered "uncontrolled" based on patient-reported symptoms in the 1-4 weeks before questionnaire completion (47. 3% and 47. 1%, respectively). The agreement between RDAC and patient-reported control was low: Somers'd was 0. 121 (95% CI, 0. 083-0. 158). The agreement between OAC and patient-reported control was slightly higher, but still low: Somers' d was 0.
exacerbations Of the patients who had no database records of exacerbations, 80. 6% reported no exacerbations via questionnaire (Figure  2 ). Of those who had one or more database-defined exacerbations, 78. 9% had one or more self-reported exacerbations. Overall, the agreement between patient-reported exacerbations and database-defined exacerbations was fair, with a Cohen's kappa of 0. 35 (95% CI, 0. 31-0. 40).
ics cohorts: response to therapy asthma control After initiating ICS treatment, the proportion of patients with uncontrolled asthma by RDAC decreased from 24. 9% during baseline to 18. 6% during the outcome year (P<0. 001; Table  3 ). In the ICS step-up cohort, the proportion of patients with uncontrolled asthma by RDAC decreased from 33. 4% during baseline to 25. 7% after the increase in their ICS therapy (P<0. 001). In contrast, the proportion of patients with uncontrolled asthma by OAC paradoxically increased from baseline to outcome years for both cohorts, from 33. 1% Figure  1  comparison of database asthma control (RDac and Oac) and patient-reported symptom control categories. Note: *Patient-reported symptom control "controlled" category also includes patients whose asthma was "well-controlled" or "partly controlled" based on the 2014 gina definition.
exacerbations After initiation of ICS treatment, the mean number of database-defined exacerbations decreased from 0. 09 (SD, 0. 32) in the baseline year to 0. 08 (SD, 0. 35) in the outcome year (P=0. 001; Table  3 ). After a step-up in the ICS dose, the mean number of exacerbations decreased from 0. 21 (SD, 0. 60) in the baseline year to 0. 16 (SD, 0. 52) in the outcome year (P<0. 001).
Discussion This study examined real-life database asthma outcomes published within the peer-reviewed literature. The findings showed that database measures of RDAC and exacerbations are responsive to initiation or increases of asthma therapy with proven efficacy (ICSs) and may be useful in assessing future asthma risk. However, limitations were identified for the database measure of OAC because of the inclusion of an SABA-based proxy for recent asthma symptoms within the definition.
Database outcomes versus PROs One of the novel features of this study was the comparison of patient-reported questionnaire outcomes with databasereported outcomes for a large number of respondents (n=2,366). Despite no previous formal validation, the OPCRD questionnaire items were derived from validated questionnaires such as the GINA 7 assessment of asthma symptom control in the past 4 weeks (modified in this study to 7 days) and ATAQ  21, 22  for patient-reported exacerbation measures in the past 12 months. In this study, patient-reported questionnaire outcomes were compared against databaserecorded control outcomes. While there are published data from a previous study designed to validate database-recorded asthma measures against questionnaire reports, 25 to the best of our knowledge, the current study is the first to compare database against patient-reported asthma control measures in a large observational study design.
65 SABA prescriptions in 12 months and 800 μg/day corresponds to 14. 6 saBa prescriptions in 12 months. Abbreviations: CI, confidence interval; ICS, inhaled corticosteroid; RDAC, risk domain asthma control; OAC, overall asthma control; A&E, accident and emergency; OPD, outpatient department; saBa, short-acting beta-agonist. There was poor agreement between patient-reported symptom control and the database-derived control outcomes (RDAC and OAC) for the same patients, particularly in the identification of patients with uncontrolled symptoms. The poor agreement was to be expected, as the database outcomes were evaluated over a 12-month period, whereas the PROs related to a period of up to 1 month. Furthermore, these outcomes were assessing two different domains of asthma control: future risk and symptom control, respectively.