dataesr/scientific-paragraphs-categorization huggingface dataset:
Outcomes DP was defined as a TJC28 that exceeded the SJC28 by at least 7 (i. e. TJC28 À SJC28 ! 7), according to established criteria  [28, 32]. Other definitions were also explored: SJC:TJC ratio <0. 5  [29]  and a difference between PtGA and PhGA scores of at least 2 (PtGA À PhGA ! 2)  [33, 34]. The former was abandoned because, mathematically, it could not be used for assessment of patients with a TJC of 0 without imputing data for a sizeable number of patients. The latter was abandoned because the baseline characteristics of that subset were substantially different from the characteristics of patients selected using the other two definitions. For the RCTs, the proportions of patients with DP and demographic and disease characteristics according to DP status were reported at baseline. Outcomes assessed included persistence of DP after 12 and 24 weeks of treatment, clinical response at week 24 by baseline DP status [ACR 20/50/70 criteria, Clinical Disease Activity Index (CDAI) 10 low disease activity (LDA), 28-joint DAS using CRP (DAS28-CRP) 3.
Discussion In general, most physicians would expect joint pain due to RA to be associated with synovitis. There are accumulating data however that a proportion of pain experienced by patients with RA may not be directly ascribable to the magnitude of joint inflammation. Therefore DP can be considered as joint pain with little sign of inflammation and it is a clinically relevant issue for patients with RA. In our data, nearly one in four patients may have had a component of DP at baseline, a prevalence that was consistent across three sarilumab phase 3 trials. Similar findings were recently reported in a study of 243 Japanese patients with RA (NCT02293902), of whom 15% had DP at baseline. After 24 weeks, sarilumab treatment was associated with a reduction in the prevalence of DP and with better clinical outcomes in patients with DP compared with placebo  [35].
Since producing a high p T cc pair at the hard collision is suppressed by 1/p 2 T comparing the production of single fragmenting parton, the LP formalism in (1) covers only events where cc pairs emerge at distances longer than 1/µ 0 with µ 0 ∼ 2m c -the scale of non-perturbative input FFs, D f →J/ψ (z, µ 2 0 ). As shown in Sec. 3, the factorized LP contribution provides a good description of the published LHC data at high p T 60 GeV, but is far below the data when extrapolated to lower p T.
Conclusion We presented the first numerical calculations for J/ψ production in hadronic collisions in the renormalization group improved QCD factorization formalism, including the NLP contribution. We demonstrated that the LP contributions dominate the high p T region, while the NLP contributions are sizable at lower p T and necessary for describing the LHC data within the QCD factorization approach. With only two parameters (α, β) and K NLP , theoretical calculations in terms of QCD factorization are consistent with existing data while there is sufficient room to improve.
Acknowledgements We wish to thank  Hee Sok Chung  and  Nobuo Sato  for valuable discussions and their instructions to use INCNLO codes regarding Ref. [6]. J. W. Q. and K. W. thank  Nobuo Sato  for teaching them a way to implement LHAPDF6 interpolator [16]. K. W. would also like to thank  Jefferson Lab  for computational resources essential to perform this project. This work is supported by  Jefferson Science Associates, LLC  under  U. S. DOE  Contract No. DE-AC05-06OR23177. The work of K. L. and G. S. was supported in part by the  National Science Foundation , award 1915093.
flares, and current and previous treatments were collected by the physician. Patient-perceived burden was assessed using patient-reported outcome (PRO) questionnaires, which were completed on a voluntary basis and included the following instruments: Patient-Oriented Eczema Measure (POEM), Dermatology Life Quality Index (DLQI), EuroQol five-dimensional (EQ-5D), and Work Productivity and Activity Impairment (WPAI). Disease severity was subjectively assessed by physicians and was based on their own definition of the terms mild, moderate, and severe. Data were analyzed descriptively. Results: The physician-reported sample included 912 patients with moderate to severe disease from France (n = 314), Italy (n = 309), and the UK (n = 289); approximately 30% of patients provided PRO data. Across these countries, 22-41% of patients reported current.
Study Design, Data Source, and Study Participants A retrospective observational study was conducted using medical records and physician and patient survey data from the Adelphi Atopic Dermatitis Disease Specific Programme (DSP). Adelphi DSPs are large, multinational, crosssectional (point-in-time) surveys that provide current ''real-world'' data from both patients and physicians at clinical practice sites, including specialty physician practices  [29]. The survey collected data from adult patients (aged C 18 years) with physician-diagnosed AD who were either currently experiencing or had a history of moderate to severe disease. Data were also collected from their primary care providers [i. e. , general practitioners (GPs)] or specialist (dermatologist, allergist/immunologist) physicians. The survey was conducted in France, Italy, and the UK during the period August 2019 to April 2020.
In the UK, recruitment of primary care physicians (PCPs)/GPs included a small proportion [8/62 (13%)] with a self-defined special interest in dermatology, whereas recruitment of PCPs/GPs in France and Italy did not involve/require any special interest. Although data were captured for patients with mild, moderate, and severe disease, the analysis focused on those currently with moderate to severe disease. Physicians were identified from publicly available lists of healthcare professionals and invited to participate in the DSP. For inclusion in the study, physicians were required to be actively involved in AD drug management and have a minimum monthly workload of five adult patients with moderate to severe AD, including at least one patient with moderate and one with severe AD. Physicians were requested to complete a patient record form (PRF) for each of the next five consecutive eligible patients consulting with AD.
Study Variables Demographic and baseline disease characteristics, as well as information related to current comorbidities, were extracted from the PRFs. Comorbidities were split into atopic and nonatopic conditions, and information about them was based on the physician's knowledge of each patient and their medical records. The PRFs also provided data on disease flares (defined as an acute episode/temporary worsening of symptoms, as per clinical judgment), and on current topical therapy, including topical corticosteroids, topical calcineurin inhibitors, and topical crisaborole, all reported either as monotherapy or in combination. Systemic corticosteroids, conventional systemic therapies (which included cyclosporine, methotrexate, azathioprine, and mycophenolate mofetil but excluded systemic corticosteroids), and dupilumab were reported either as monotherapy or in combination with topical agents.
Data on PROs were obtained from the PSCs and included the self-administered measures described below. The POEM questionnaire assesses the frequency of AD symptoms over the past week. The instrument covers seven domains/items (itch, sleep, bleeding, weeping or oozing, cracking, flaking, and dry or rough skin), and total scores range from 0 to 28, with higher scores indicating greater severity  [32, 33]. The Dermatology Life Quality Index (DLQI) is a dermatology-specific questionnaire that measures the impact of skin disease on HRQoL. It comprises ten questions relating to daily life in the last week and covers six domains/items (symptoms and feelings, daily activities, leisure, work and school, personal relationships, and treatment). Total scores range from 0 to 30, with higher scores indicating a greater impact on the patient's life  [34, 35].
The three-level EQ-5D is a generic (i. e. , non-disease-specific) instrument that provides an assessment of general health/HRQoL  [36]. Health utility index scores are generated with country-specific algorithms and range from 0 to 1, with lower scores reflecting poorer HRQoL. The Work Productivity and Activity Impairment (WPAI) questionnaire measures the impact of disease on work productivity and daily activities over the past week. The instrument includes four items (absenteeism, presenteeism, loss of productivity and work, and impact on activities), and the score is expressed as a percentage, with higher scores indicating a greater impact  [37]. Data were collected on the percentage of patient body surface area (BSA) affected by AD. Data provided by physicians on the PRFs were used to calculate Eczema Area and Severity Index (EASI) scores  [30] , which provide an assessment of skin signs and extent of skin involvement.
RESULTS Of 1517 patients surveyed across the three countries (data provided by 307 physicians), 605 (40%) had mild disease, 746 (49%) had moderate AD, and 166 (11%) had severe AD on the basis of physician assessment. All patients with mild AD had a history of moderate to severe disease. Therefore, the current analysis included 912 patients who currently had moderate to severe AD on the basis of PRF completion (PRF population): 314 in France, 309 in Italy, and 289 in the UK. In the PRF population, 78-84% of patients had AD of moderate severity and 16-22% had severe disease (Table  1 ). Across the three countries, 45-66% of physicians were GPs or GPs with a special interest in dermatology (UK only), and the remainder were specialists, either dermatologists or allergists. In general, the proportion of specialists was higher for patients with severe than moderate disease, although this varied by country.
Similar to our findings, the EUROSTAD prospective observational study recently reported a high level of comorbidities, AD burden, and impact on work and daily activities  [26]. Although other recent real-world retrospective analyses  [25, 27, 28]  and a review of AD  [40]  also highlighted unmet treatment needs in this patient population, a unique and important strength of the current analysis is that both physician-and patient-reported outcomes were derived from the same patients. Another strength of the study is the use of the Adelphi DSP data source, a large international database that captures real-world country-specific information on disease characteristics and management, PROs, and HRQoL. The DSP also provides a tool to analyze unmet therapeutic needs. Additional methodological strengths include the enrollment of a sample of patients and use of standardized data collection tools across countries to facilitate between-country comparisons.
The DSP was conducted in compliance with the ICH Declaration of Helsinki. Freely given, specific and informed consent, was obtained from each respondent to take part in the DSP and for the processing of their personal data. All data provided by physicians and patients were anonymized. Data availability. The dataset supporting the conclusions of this article are included within the article (and its additional files). Open Access. This article is licensed under a Creative Commons Attribution-NonCommercial 4. 0 International License, which permits any non-commercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
Pfizer's December 2021 post marketing data demonstrate an increased risk of myocarditis and pericarditis in men under 40 years of age and that the potential long-term sequelae are not yet known (p. 7). [65]  On the European database of suspected adverse drug reaction reports, put online by the EMA, the adverse events at the European level for the four vaccines, as of July 9, 2022, are 65,669 for the Janssen vaccine; 506,221 with AstaZeneca; 312,013 with Moderna; and 1,043,308 with Pfizer-BioNTech,  [57]  as of September 10, 2022, are 69,075 for the Janssen vaccine; 523,696 with AstaZeneca; 337,429 with Moderna; and 1,118,906 with Pfizer-BioNTech. [57]  However, these effects could be largely underestimated, as evidenced by the vaccine adverse event reporting system database, which suffers from significant underreporting of adverse events.
441-443,458). [95] nce the beginning of COVID-19, much scientific data and expert opinion have been censored or labeled as false or misleading by many internet platforms. [33, 75, 123] In France, the mainstream media only peddle the government's messages, without any field investigation, but rather censoring and discrediting all the reputable scientists in their specialties who dare to question the public health guidelines (p. 247-263). [120] In contrast, KOLs are often invited in the media. [135]  June 2019, the World Economic Forum (WEF) and the United Nations signed a partnership (2030 agenda). In the field of health, this alliance is designed to combat key emerging global health threats and achieve universal health coverage. [161] In October 2019, in New York City, the Johns Hopkins Center for Health Security and its partners the WEF and the Gates Foundation, hosted Event 201, a fictional coronavirus pandemic.
Acknowledgements This work was supported by the  French Development Agency (AFD)  under Grant  AFD/ACH -2015-092. The global research project was entitled ''The implications of the rise of the middle classes in developing and emerging countries" and covered four different countries ( Brazil, Ivory Coast, Turkey and Vietnam ). We would like to thank  Anda David  the coordinator of the project at  AFD  as well as the different members of the steering committee of the project. This study has also received financial support from the  French State  in the framework of the Investments for the Future pro-.
" 2. Illustrate how cities contribute to overall global raw material consumption reduction by quantifying the material import dependencies (MID) through the use of raw material equivalents. 3. Combine these two approaches to come up with a CE jobs measure that will serve as a globally replicable first step to developing a circular economy plan at city level. This methodology is illustrated in two different cities. The first is a city from the Global North, center of the European Union, and capital of Belgium: Brussels. The second is a coastal city from the Global South, capital of the province of Pernambuco, and the fourth-largest agglomeration in Brazil: Recife. The results are then presented, followed by the opportunities on future areas of work, particularly on data and the informal sector.
Collection of Data The city of Recife, with an estimated population of 1. 6 million and an employment rate of 43. 9% is 2  the capital city of the Brazilian state of Pernambuco. It is also the heart of the fourthlargest urban agglomeration in Brazil and serves a metropolitan population of about 4 million, many of whom work in the city. A network of rivers largely defines Recife's urban form. Brussels in this report refers to the Brussels Capital Region (BCR), a metropolitan area that includes 19 municipalities 3  , including the City of Brussels, which is the capital of Belgium. The BCR, when compared to Recife, is smaller in terms of land area and population. But beyond the over 1 million population of the BCR, the metropolitan area also serves the wider urban area extending toward Ghent, Antwerp, Leuven, and Walloon Brabant, which is home to over 5 million people.
BCR has an unemployment rate of 17. 8% 4. The choice of Recife and Brussels to demonstrate the combined methodology emerged from various discussions and a list of criteria co-developed between UNEP and Circle Economy. We wanted to work with: 1. one city in the global north and another in the global south. 2. cities in our existing pool of cities-those with whom we had a familiarity in terms of the data availability and data management framework. Both Recife and Brussels had the appropriate data "ecosystem" favorable for this type of analysis. UNEP worked with both cities in previous occasions on resource flows and urban metabolism. Circle Economy also worked with Belgium (including Brussels) on circular economy jobs. 3. cities where we have a good understanding of the local context 5  and where there is a local partner willing to help if necessary.
This gives insight into the current state and allows for the identification of relevant economic sectors to further accelerate the transition toward a circular economy. The entire process can be summarized in Figure  1  below, and then discussed in detail further below. One of the premisses for the construction of this model was that the required input data to feed the model should be easily accessible for as many cities as possible in order to: (a) leave no city behind  (Acuto and Parnell, 2016)  and (b) break the cycle of systematic-bias of urban-science toward large agglomeration of the global north  (Creutzig et al. , 2019). The presented method requires only 3 datasets, two of them should be available only at a national level. The third input dataset is the hardest of all three to get.
In order to downscale the national-level IO table (input one) to the city level, we require: (a) employment data at a national-level (input two) and (b) similar constructed employment data at a city level. By far, the most difficult input-data-set to get is the city-level employment data. Working with many cities, we have identified the national level micro dataset behind the national labor force survey to be the most suitable dataset to extract this type of information. The model modifies the final demand vector, as estimates for circular final demand. The output required by industry to meet this circular final demand is calculated using the Leontief Inverse, and is our proxy for circular output of the economy. To determine circular activity per sector we take the proportion of this circular output over the original economic output, and we apply this proportion to the number of jobs in the sector to determine the circular employment.
A. GATHER AND PREPARE DATA Here are the steps we took in order to gather data and proceed with the calculation We gathered the following data: • Macro-economic data: -National IO Tables: all national IO tables are retrieved from  EXIOBASE Tukker et al. (2009 , 2011 , 2013)  To facilitate replicability, we used both national level employment data and demographic data from national statistics agencies. We then used EORA 12 , a global database estimating emissions and resource extractions by industry, to populate our national level input-output tables. In the case of Brazil and Belgium, the databases of the statistics agencies were free, but this may not always be the case in every country. As a precursor to the calculation, all economic sectors are classified into either core circular sectors, or enabling circular sectors as per the previous section on the key elements of the circular economy (see Appendix III for a full list of sector codes with classifications).
We scale down our national datasets to regional scale using the socioeconomic data as scaling factors. We use employment data to scale down the IO table. Employment coefficient of a sector E i = E i,city ÷ E i,national (1) Population coefficient of a city P i = P i,city ÷ P i,national (2) 9 Other available data sources include: WIOD Timmer (2012), EORA  Lenzen et al. (2012 Lenzen et al. ( , 2013)) ,  Moran and Wood (2014).
CALIBRATION One of the strongest arguments made against this method among its developers what the downscaling process of data to city level. The mayor issue with this downscaling process was that one of the computation input data-set is not scalable to this level. This fact creates a computational bias toward large countries with low international material dependencies. This is the case of Brazil. In the following pages, we present a simple comparison of results by interchanging the "home" country-input-dataset (Brazil for Recife and Belgium for Brussels). This comparison is interesting because we can see how national level context influences the city level performance. When using the Brazil input data, both cities achieve a higher  circularity measure (see Figures  3, 4 ), proving so, the existing bias toward large economies.
Data Analysis The results of the combined methodology reveal that the percentage of circular economy jobs in Brussels and Recife are 8. 51% and 9. 96% respectively (Figure  5 ). A further analysis of these results will show that these numbers serve not as a basis of comparison between two very different contexts such as that of Recife and Brussels but rather as a starting point for the city to improve. Through policy development and action, cities can track their circular economy transition through this method. Interpreting the numbers Table  4  shows the aggregated number of circular jobs by EORA activities for the city of Recife. Education, Health, and Other Services is the economic activity with the highest number of circular jobs in the city, with a total share of 27% of all circular jobs.
This level varies from city to city, depending on which level the input data is available. In this case, the data resolution of Brussels provides a much lower aggregation level. A direct comparison between cities at this level of detail is therefore much more complex. Table  6  shows the top-10 number of circular jobs for the city of Recife. The table present at the top-10 almost only service oriented activities. Nonetheless, on this table, we also see the construction of buildings making the top-10 ranking. Table  7  shows the top-10 number of circular jobs for the city of Brussels. Similar to Recife, we see here almost only serviceoriented sectors. We see many administration jobs, management, and restaurants.
Informality Along the same lines of trying to capture a just transition to a more circular economy, UNEP and Circle Economy faced challenges in including informality in this methodology. With little to no data available on this topic, measuring the impact of the circular economy transition on this vulnerable section of the population becomes difficult. Not having data on the informal sector thus skews the available data on employment. The databases used in this analysis do not include any information related to the informal sector. Thus, jobs in sectors that would otherwise be contributing to the circular economy (particularly those referenced as "core sectors") are not captured in the analysis. Employment data used for Recife, for example, is arguably incomplete because informality in Recife's labor market was not taken into account.
We are currently piloting this expanded work, looking at the inclusion of informality in Bogor, Indonesia, where the informal sector is estimated to account for 34 percent of the working population. 6. 1. 3. Data: City typologies, bottom-up data and supporting global databases One of the main considerations in this work is the availability of data, and the issue of data-scarcity that most impacts global south cities. To "leave no city behind" (see  (Acuto and Parnell, 2016)  we need to develop methods that can be applied across the large range of cities around the globe. With the aim of implementing this method in both datarich and data-scarce cities, this paper used downscaled data from available national-level datasets. This process has largely been aided by existing global data management solutions like EXIOBASE and EORA.