dataesr/scientific-paragraphs-categorization huggingface dataset:
Lung Histology For histopathological analysis, posterior lobes of lungs were fixed with paraformaldehyde (PFA 4%). Lungs were paraffin-embedded and lung section was stained with hematoxylin-eosin. Lung injury was scored based on remodeling and inflammation, as previously defined  [8]. Lesions in bronchial epithelium were also evaluated as cell desquamation, cellular lesions and dissociation. The percentage of altered epithelium was determined by using Image J software (NIH). For immunohischemistry (IHC), lung paraffin-embedded section was deparrafined with two successive baths of Safesolv (Labonord ref 00699464) and rehydrated with successive bath of ethanol (two bath of 100%, one of 90%, one of 80% and one of 50%) and one of water (during 5 min for each). The masking epitope was carried out in pH 6.
Statistical Analysis The data are expressed as mean ± sem. Results were statistically analyzed with prism software (GraphPad version 9, Prism, San Diego, Ca, USA) using the two-way ANOVA analysis. Virus effect was analyzed by Tukey's multiple comparison test, while CS or IL-20 KO effect was analyzed by Sidak's multiple comparison test. Differences were considered significant when p < 0•05, p = non-significant (ns). Significant virus effect (in comparison to non-infected mice) is symbolized by * p < 0. 05; ** p < 0. 01; *** p < 0. 001; **** p < 0. 0001. Significant CS or IL-20 -/-effect are symbolized by # p < 0. 05, ## p < 0. 01, ### p < 0. 001 and #### p < 0. 0001.
Acknowledgments: We thank the  Animal Resource Center (PLEHTA)  and the BICeL flow cytometry core facility of the  Institut Pasteur de Lille. We would also like to thank the animal Biosafety Level-2 facility, in strict accordance with Lille Pasteur Institute guidelines on animal care. We also thank  Marie-Hélène Gevaert  ( University of Lille ) for histological preparation of lung sections. The following reagent was obtained through the  NIH Tetramer Core Facility :  PBS57 -loaded CD1d tetramer PE-labeled.
Search strategy We developed a search strategy that covered three dimensions of the relevant studies for our review: 1) the policy area of interest (health financing strategies), 2) the object of interest (policy processes/change), and 3) the geographical coverage (SSA). We used a composite approach to construct each dimension separately before combining them together in each database when option was available [Table  1 ]. We gathered the health financing policy terms from key documents on UHC and the terms used by  Gautier and Ridde's (2017)  review of government ownership in health financing policy processes. We drew the policy process/change terms from the public policy literature on the stages of the policy process, the main variables of policy change (i. e. the three Is -interests, ideas, institutions), and other conceptual and empirical terms for policy actors and influences generally used in public policy theory, practice, and analysis  (Cairney 2020; Sabatier 2007).
We used a French dictionary of public policy  (Boussaguet et al. 2014)  to validate French language translations of these terms. We defined the countries included in our geographical zone of interest according to the World Bank's list of 48 countries in SSA (https://data. worldbank. org/region/sub-saharan-africa). The search strategy was developed iteratively by the first author through multiple rounds of testing different combinations of terms in databases, in consultation with co-authors, and with input from an expert in systematic and scoping reviews. It was also discussed and validated by the political science co-investigators of the wider project. The first author followed a three-stage process to independently screen and select studies for analysis, consulting with both co-authors for verification.
Consultation We included a consultation phase in the study, which is an optional step in scoping methodology  (Arksey & O'Malley 2005; Levac et al. 2010). We presented and discussed preliminary results with participants in a research workshop in Senegal in 2018. The participants included social science and public health researchers from Belgium, Canada, France, Mali, and Senegal as well as decision-makers from the health and mining sectors in the latter two countries. The research team and decision-maker partners in the larger project wanted to learn from challenges in using theories and conceptual frameworks to study health Upon completion of the initial data analysis, we carried out a survey among the first authors of the studies selected for analysis. Given the limited data collected in our review providing insights to the challenges and learning of authors using mid-range theories and conceptual frameworks to study health financing policy processes/change, we invited first/corresponding authors individually by email to respond to three open-ended questions.
The decision not to earmark some theoretical terms or authors (like windows of opportunity, streams, Kingdon, advocacy coalitions, Sabatier, path dependence, Baumgartner, etc. ) may have limited our results. Despite our best efforts to design and implement a systematic search strategy, this limitation regarding the search terms may explain why some papers on health financing policy analysis in SSA using public policy theory and conceptual frameworks are missing from the results. There are also limitations regarding related to the search for empirical material from the grey literature and non-indexed scientific production. Generally, scoping reviews cover a wider range of materials, with specific efforts to include grey literature. We did not include research that was not found in scientific databases, which generally excludes non-indexed journals.
The application of machine learning (ML) technologies in medicine generally but also in radiology more specifically is hoped to improve clinical processes and the provision of healthcare. A central motivation in this regard is to advance patient treatment by reducing human error and increasing the accuracy of prognosis, diagnosis and therapy decisions. There is, however, also increasing awareness about bias in ML technologies and its potentially harmful consequences. Biases refer to systematic distortions of datasets, algorithms, or human decision making. These systematic distortions are understood to have negative effects on the quality of an outcome in terms of accuracy, fairness, or transparency. But biases are not only a technical problem that requires a technical solution. Because they often also have a social dimension, the 'distorted' outcomes they yield often have implications for equity.
Such availability bias occurs, for example, when doctors' diagnoses are biased by what they see more often in their specific patient population, which may be older or younger than average, or more prone to specific health problems  [9]. At the same time, doctors and researchers also increasingly recognise that the use of ML in healthcare can also increase certain biases. For example, the so-called GIGO ("garbage in, garbage out") problem refers to the fact that any ML algorithm will only be as good as the data that it is trained with; if there are biases in the training datasets then these will be reiterated-and possibly exacerbated-by the ML application. Bias, therefore, has come to play a prominent role in the discussion around the implementation of ML technologies in healthcare more broadly.
What is a bias? Generally speaking, in medical research, bias refers to "a feature of the design of a study, or the execution of a study, or the analysis of the data from a study, that makes evidence misleading"  [10, p. 104 ]. In clinical research studies, for example, biases could lead to results that overstate the effectiveness of a therapy. But biases also occur in medical practice where they take the form of open or implicit prejudices or errors in human reasoning that can influence the diagnostic process and health professionals' decision-making more generally  [9, 11]. It is important to emphasise that biases in ML are not merely technological problems that can be solved by technological means. For example, the composition of patient dataset used for research in imaging (e. g. population imaging) is influenced by who has access to radiology services in the first place.
The higher rate of false negatives in these patients suggests that health inequities are not only due to these groups' diminished access to services but are also influenced by radiologists' stereotypes and prejudices  [12]. The problem of cognitive bias in diagnosis and decision-making is not a new one; in the age of digital health, however, cognitive biases can find their ways into datasets and potentially get "automated" through ML technologies. This, for example, can occur when the labelling of images is influenced by cognitive biases. The quality of an image-a single data item-depends, on the quality of imaging technologies and their correct use. Ideally, images have a proper resolution and are free of artefacts. But good-quality images also need to be labelled correctly. In order for an algorithm to accurately detect a disease in question, the training database has to be composed of properly labelled images-at least in supervised and semi-supervised ML.
A misleading labelling or error of delineation of the organ increases the inaccuracy of an algorithm. This is sometimes referred to as misclassification or measurement error  [20]. While image quality is partly a technological challenge, there is also a social component to it. As we have pointed out, culturally influenced cognitive biases about people or groups can have an impact on what radiologists see and what they do not see in images. This means that through the interpretation and labelling of images, radiologists' cognitive biases can translate into data biases. Healthcare data more generally is likely to reflect the-often unconscious-discrimination of certain groups of patients along the line of socio-economic status, gender, race, and other social categories. Datasets are biased in a qualitative way if they include data about misdiagnoses, and these misdiagnoses are structural, because they particularly affect patients belonging to a specific social group, such as the socio-economically disadvantaged, women or racial minorities.
We have the moral obligation to mitigate the harmful consequences also of implicit and unconscious bias, and to avoid structural discrimination. Awareness, however, is not enough precisely because our own biases are often not visible and accessible to us. From a perspective of relational justice, therefore, it is important to include a wide range of experiences and perspectives in the process of generating data and developing technologies. This means that we need to ask who is generating data, analysing datasets and building technologies, and for whose benefits-both in radiology and computer science. For example, radiology, like medicine in general, has been a domain in which women and minorities have traditionally been underrepresented, and they still are  [23, 24]. Also computer science remains a male-dominated field  [25].
Qualitative vs. quantitative biases We have shown that a problematic labelling of images can lead to qualitatively biased datasets, biased ML technologies and ultimately inequitable outcomes. ML technologies, however, can also be biased because of quantitative misrepresentations in datasets. In such cases, every single data item can be of high quality (e. g. , every image is correctly labelled), but the dataset as a whole can still be skewed. This happens, for example, when an image dataset does not adequately represent all patient sub-populations in terms of numbers. Quantitative misrepresentation is not a new problem in medical research and practice, of course: in the context of clinical trials, for example, the systematic underrepresentation of the elderly, ethnic minorities or women is a well-known problem and has attracted criticism  [28, 29].
In clinical trials, such underrepresentation of specific groups is a problem when results of research is generalised even though a tested treatment might be less effective or less safe for the populations that were not included in the trial. Similarly, the quantitative misrepresentation of groups in datasets used for research in imaging could do harm when it leads to algorithms or decision aids that do not "fit" the groups that were underrepresented. In contrast to clinical trials data, digital health data is often collected in routine healthcare situations. This means that biases concerning the representation of specific groups in such datasets are often difficult to detectalso because they are entangled with a variety of social and economic factors. Regarding data stemming from electronic health records, Prosperi and colleagues  [30, p.
10 ] emphasised that such data is "inherently biased by the patient population structure, frequency of healthcare visits, diagnostic criteria, and care pathways. " This also applies to imaging data that is collected in everyday healthcare settings. Glover and colleagues  [31] , in a study on missed imaging appointments in the United States, found that racial minorities and people with low socioeconomic status missed more appointments than other groups. The authors attribute these results to multiple factors including those groups' limited financial resources and geographic barriers, which make it more difficult to make appointments. Missed radiology appointments are associated with a later detection of disease and therefore higher mortality. The disadvantage of these groups, however, is even further exacerbated by the fact that they are underrepresented in the imaging datasets of their hospital or healthcare provider and an algorithm developed with this data might be less accurate for patients from these groups.
Is quantitative misrepresentation always inequitable? Who is represented in imaging datasets in quantitative terms is shaped by the access different people and groups have to healthcare. The distribution of resources such as income and wealth influence access to healthcare decisively. Often, socioeconomic factors are entangled with other categories, such as race or gender. Differences among socioeconomic groups in their access to healthcare are an inequity because their cause is socially unjust. If access to care is inequitable, an algorithm based on data collected in healthcare settings could be seen as inequitable as well. This is because people with restricted access to healthcare are "missing" in the dataset and the decisions the algorithm suggests will likely be less adequate for those groups missing.
When is a dataset unbiased? Similar to the difficulty to define what cognitively unbiased humans are, however, it is not always clear what an unbiased dataset would look like. Is a dataset unbiased when it adequately represents all populations that are treated at the hospital for which the software is developed? Is it unbiased only when it adequately represents the wider population in the local area (because the hospital may already be "biased" in that it treats mostly wealthy patients, for example)? There are no right and wrong answers to these questions if they are asked in the abstract. They always need to be answered within a specific context; considering also issues of qualitative bias in combination with quantitative bias. Consider the case of rare diseases: all people with a particular rare disease X could be perfectly represented in a dataset and yet the people with this disease might receive lower-quality healthcare than people with common diseases.
This is because the group of people with rare disease X might be so small that the amount of data about them is not sufficient to build an algorithm that produces reliable results. People with the rare disease X receiving lower-quality healthcare due to the small number of people suffering from the disease, however, can still be inequitable. In this case, the inequity occurs if we had the power to correct for the unjust differences in healthcare quality but failed to do so. This means that efforts to ensure the best possible diversity of patient data are essential for the development of equitable ML technologies even if this includes a departure from the ideal of quantitative representativity. Special concern must be given to the improvement of databases by detecting omissions and filling potential data gaps.
Another example would be to deliberately over-sample people with darker skin for a dataset training an algorithm detecting skin diseases as certain colour contrasts may be less easy to discern on dark skin than on light skin. In these cases, biases are explicitly equitable. They would be inequitable if we knew about the discriminatory effect of an algorithm-due to social factors or not-but had failed to take action against those outcomes (Table  1 ). Although radiologists have been aware of certain biases for a long time, the implementation of diagnostic ML algorithms and the potential translation of cognitive and data biases into these technologies raises the problem of bias to a new level. This is because a ML algorithm has the capability to "autonomously" identify patterns and continuously readjust its own decision making on the basis of what it has "learned".
Finally, biases may be relationally unjust if concerns about the use of algorithms or their outcomes are not being taken seriously and people's concerns are dismissed. In order to assess whether biases are inequitable or not points to the importance of reflecting upon the social categories that influence the generation of data, the quality of datasets as well as practices of building and applying technologies. It is not problematic as such that data and machines are influenced by social categoriesthis is often unavoidable. If the social condition, however, is characterised by inequities and there is a lack of awareness about them, technologies might contribute to their "automisation"  [43] , meaning that inequities get perpetuated and solidified on a large scale while at the same time they might get harder to detect.
(  4 ) and some of their values are indicated in the Abbreviations. The thermal balance (4) may be written as the differential equation: The plate temperature T pl starts from the initial ambient temperature T amb (0) at t = 0. The differential Eq. (  5 ) involves the unknown convection coefficients h i and h e,pl to be determined through a comparison between the experimental and simulated plate temperatures. The differential Eq. (  5 ) is indeed solved using Scilab software and the explicit Euler method, first with initial guesses of the unknown convection coefficients h i and h e,pl. Then the Scilab routine 'data- fit' is applied, which consists to minimize the sum over the experiment times of the squared difference between the experimental and simulated plate temperatures: where J is the overall number of experimental time points, T pl,exp (j) the experimental plate temperature at time t j , and T pl,sim (j) the simulated plate temperature at time t j.
This leads to a so-called cold-start water ebullition test. The system (  9 )-(  10 ) of differential equations is solved using the Scilab software and the explicit Euler method, first with initial guesses of the unknown parameters k e , h e,h and h e,v. The optimal values of these parameters k e , h e,h and h e,v are determined applying the Scilab optimisation rou- tine 'datafit' , which consists to minimize the sum over the experiment times of the squared differences between the experimental and simulated plate and pot temperatures: where J is the overall number of experiment times, T po,exp (j) the experimental water temperature at time t j , and T po,sim (j) the simulated water temperature at time t j. The system (  9 )- (  10 ) is finally solved with the optimal values of the parameters k e , h e,h and h e,v.
The system (  14 )-(  15 ) is solved using the Scilab software and the explicit Euler method, first with initial guesses of the unknown parameters k e , h e,r , h i , h e,h , and h e,v. The optimal values of these parameters k e , h e,r , h i , h e,h , and h e,v are determined applying the Scilab optimisation (  13 ) k e S po,h -S hole T r (t) -T po (t) dt + h i S hole T fu (t) -T po (t) dt + F b, (14) dT r (t) dt = 1 m r C p,r ⎛ ⎜ ⎜ ⎜ ⎝ h i S r,f � T fu (t) -T r (t) � + F b,r fl S b T 4 fl (t) -h e,r � S r,f -S po,h �� T r (t) -T amb (t) � -(S r -S po,h ) pl T 4 r (t) -k e � S po,h -S hole �� T r (t) -T po (t) � ⎞ ⎟ ⎟ ⎟ ⎠ , ( 15 ) dT po (t) dt = 1 m po C p,po + m wa C p,wa ⎛ ⎜ ⎜ ⎜ ⎜ ⎜ ⎝ k e � S po,h -S hole �� T r (t) -T po (t) � +h i S hole � T fu (t) -T r (t) � +F b,hole fl S b T 4 fu (t) -h e,v S po,v � T po (t) -T amb (t) � -h e,h S po,h � T po (t) -T amb (t) � -po S po,t T 4 po (t) ⎞ ⎟ ⎟ ⎟ ⎟ ⎟ ⎠.
Study design and participants ISAAC Phase I and Phase III were multicentre, multicountry, cross-sectional, population studies in schoolaged children, following standardised methods that have been well described  11, 12  and are highly replicable. 13 This enabled comparisons of prevalence and severity of asthma symptoms between ISAAC Phase I and Phase III and GAN Phase I. Centres that completed GAN Phase I and ISAAC Phase I, Phase III, or both were the study centres for this analysis, and key personnel at the GAN Global Centre (Auckland, New Zealand) were the same throughout ISAAC Phase I, ISAAC Phase III, and GAN Phase I. Each GAN Phase I investigator completed a registration document and followed the GAN manual. 14 hey gained approval from their local ethics committee and replicated the methods that had been used in their centres for ISAAC; this was documented in the centre report,  14  which enabled checks to ensure the use of the same geographical sampling frame, sample size, age groups, method of selecting pupils, time of year for data collection, and translations as had been used in ISAAC.
Implications of all the available evidence Asthma is a major worldwide public health problem in school-aged children. Implementing strategies to reduce the prevalence and severity of asthma symptoms (and the associated avoidable asthma morbidity and mortality and subsequent burden of related diseases, including chronic obstructive pulmonary disease) should be a priority for the 21st century. to the question "in the past 12 months have you (has this child) had a dry cough at night, apart from associated with a cold or chest infection?" Data and the centre report from each participating centre were submitted to the GAN Global Centre, and quality control checks were completed. Centres with    minor deviations from the methods were included in analyses, and these deviations are specified in footnotes in the tables, as in ISAAC.
18 The data were then transferred to one of two designated GAN Phase I data centres for checking and analysis: Murcia (Spain) for Spanish-speaking and Portuguese-speaking centres and London (UK) for all other languages. A uniform approach to data processing, checking, and analysis was developed, using Stata versions 13-15. 19 he mean date of questionnaire completion in each centre was used, rather than the year alone as had occurred in ISAAC time-trend studies. 7, 8 High levels of participation were required for inclusion because absent school pupils might be away from school due to asthma symptoms: response rate was required to be at least 80% for adolescents and 70% for children. 9, 11, 18 The response rate was defined as the number of core asthma symptom questionnaires returned with at least some symptom data, divided by the number of pupils in the age group.