dataesr/scientific-paragraphs-categorization huggingface dataset:
ACKNOWLEDGMENTS We would like to thank all CST-International investigators including  Dr. S. P. Goswami ,  Dr. K. S. Shaji ,  Prof. Cleusa Ferri ,  Prof. Richard Walker ,  Dr. Catherine Dotchin , and  Dr. W. Keith Gray. We would also like to thank all those who acted as stakeholders in Brazil, India, and  Tanzania. Our  thanks to  Mr. Ssenku Safic ,  Dr. Marcella Yoseph ,  Mr. Aloyce Kisoli ,  Dr. Andrea Damas , and  Dr. Jane Rogathi  for their assistance with organizing stakeholder groups in Tanzania. We are grateful to  Dr. Renata Naylor ,  Dr. Maria Aparecida Guimarães ,  Dr. Valeska Marinho , and  Prof. Jerson Laks  for assistance with organizing stakeholder meetings in Brazil. Finally, we would like to thank  Dr. Arvind Kumar ,  Dr. Charan Singh ,  Dr. G. S. Grewal , and  Captain K. L.
For instance, a type of evolutionary therapy known as adaptive therapy uses drug holidays timed specifically to each patients' disease dynamics in an attempt to intentionally maintain a sufficient level of drug sensitive cells  [8, [11] [12] [13] [14]. Upon withdrawing therapy, these sensitive cells can compete with and suppress resistant cancer cells, thus prolonging drug efficacy. Continuous or maximum tolerated dose therapies quickly eliminate the entire sensitive population resulting in treatment failure as resistance cells can now grow unchecked. Adaptive therapy clinical trials are underway in multiple different cancers including trials in metastatic castrateresistant prostate cancer (NCT02415621, NCT03511196), in melanoma-NCT03543969, and in thyroid-NCT03630120. The design of these adaptive therapies is rooted heavily in the use of mathematical modeling, more specifically evolutionary game theory (EGT)  [15] [16] [17] , which helps us to model situations where multiple organisms interact and where interactions with individuals of different properties largely determine one's chances of survival (fitness).
Setting In the context of the global pandemic of SARS-CoV-2, we have set up clinical screening for SARS-CoV-2 infection when patients arrive at the dialysis center of the Hôpital de la Conception, Assistance Publique-Hôpitaux de Marseille (APHM), Marseille, France. We prospectively collected data from patients identified as suspects during this screening between March 12th and April 24th 2020. The suspected cases were all tested for the SARS-CoV-2 virus by nasopharyngeal real-time RT-PCR to determine whether they were COVID positive or negative. Positive RT-PCR were confirmed twice times. Presence of one of the following symptoms at arrival in the dialysis unit suggested SARS-CoV-2 infection: fever, cough, dyspnea, rhinorrhea, headache, asthenia, anosmia, ageusia, diarrhea, nausea and/or vomiting, myalgia, confusion.
Data sources For this pooled analysis, we used a database of cardiometabolic risk factors collated by the Non-Communicable Disease Risk Factor Collaboration (NCD-RisC). The database and its criteria for data inclusion and exclusion are described in the appendix (pp 39-42). We used data from the NCD-RisC database from 1985 to 2019 for analysis of BMI and from 1971 to 2019 for analysis of height. Children aged 5 years in data from 1971 were born in 1966, and hence were 19 years old in 1985, as were children aged 6 years in data from 1972 through to 19-year-old adolescents in data from 1985. Additionally, for analysis of height, participants aged 20-30 years were included and assigned to their corresponding birth cohort, because mean height in these ages would be at least that when they were aged 19 years, given that the decline of height with age begins in the third and fourth decades of life.
One multicountry study used cross-sectional height data in 53 community-based samples and reported height differences in children aged 10-17 years. We found three studies on trends in body-mass index (BMI) or overweight in children and adolescents, but only one of these studies separately reported trends for children aged 5-19 years. We found multiple studies in individual or small groups of countries on trends in height, BMI, or both. In terms of considering combined changes in height and BMI, the Lancet Series on the double burden of malnutrition used data on stunting in children younger than 5 years together with data on various measures of underweight and overweight at different ages, but did not have data on height in older children and adolescents, nor did it analyse trends.
Statistical analysis We used a Bayesian hierarchical model to estimate mean height and mean BMI by country, year, sex, and age. The model is described in detail in a statistical paper  13  and related substantive papers  11, 14  and is summarised in the appendix (pp 43-45). Briefly, the model had a hierarchical structure in which estimates for each country and year were informed by its own data, if available, and by data from other years in the same country and from other countries, especially those in the same region and super-region, with data for similar time periods. The extent to which estimates for each country-year were influenced by data from other years and other countries depended on whether the country had data, the sample size of the data, whether they were national, and the within-country and within-region variability of the available data.
The model allowed for non-linear time trends and nonlinear changes in mean height and BMI with age, including periods of rapid growth during puberty, and the earlier age of these growth spurts in girls than in boys. We used observation year-the year in which data were collected-as the time scale for the analysis of BMI and birth year as the time scale for the analysis of height, consistent with previous analyses. 11, 14 For BMI, substantial societal changes that affect nutrition and physical activity might affect children of different ages simultaneously, whereas for height, these effects accumulate in each birth cohort and a cohort's height-for-age monotonically increases from childhood to late adolescence. The computer code for the model is available online, as are our country and regional estimates both in numerical format and as interactive visualisations.
younger age groups. BMI is an imperfect measure of the extent and distribution of fat in the body, but it has the major advantage of having consistent and comparable data in many population-based surveys, especially compared with measures such as body fat measured by dualenergy x-ray absorptiometry (DEXA), which is complex and costly and cannot be used in surveys. A systematic review reported that BMI and DEXA-measured body fat were highly correlated. 16 We compared height and BMI in each country with the median of the WHO growth reference. 12 Although the reference is the current international comparison tool, 17 unlike that of children younger than 5 years, it is not based on a multicountry sample of predominantly healthy and well-nourished children. 12 Consequently, the reference might be affected by slower growth as the sample children grew older.
Acknowledgments The  NCD-RisC database was supported by a Biomedical Resource  and  Multi-user Equipment Grant  from the  Wellcome Trust  ( 101506/Z/13/Z ) and was expanded to include children and adolescents with partial support by a charitable grant from  AstraZeneca Young Health Programme. The analysis in this paper was partly supported by the  STOP  project which received funding from  EU   Horizon 2020 research and innovation programme  under Grant Agreement 774548. The content of this publication reflects only the views of the authors, and the  European Commission  is not liable for any use that may be made of the information it contains. Editorial note: the Lancet Group takes a neutral position with respect to territorial claims in published maps and institutional affiliations.
Field survey method An ethnographic survey was conducted on urban family rituals regarding meals and food practices in the city of Casablanca 3. This 2017 survey 4 was based on three data collection methods: individual interviews, group interviews and participant monitoring during repeated meetings with the survey respondents. Forty interviews were conducted with women and 10 with men with children, all living in working-class neighbourhoods with average living standards. An assessment of food and its daily rituals in urban families enabled immersion into the respondents' personal lives, facilitated by the growing interest in food and eating in urban society. Food was found to be linked to various cultural, identity and emotional dimensions, while encompassing multiple aspects: social differentiation and associated values, pleasure, fear, attachment, indifference, etc.
follow-up survey With the aim of updating data on food consumption patterns, we conducted a questionnaire survey of 450 households recruited in all districts of Brazzaville in July and August 2018. The 2007 population and housing census served as the sampling frame. The sample encompassed all socioprofessional categories of the population. Finally, the fact that we were living in Brazzaville enabled us to conduct continuous informal participatory monitoring between the two surveys. During this period, we regularly observed changes in the meals and products consumed by talking with food processors, housekeepers, and consumers in their homes or by observing these different actors at eateries.
Acknowledgements Except for chapters 3 and 7, this book was translated from the  French by David Manley. The authors thank him for his precious collaboration and the quality of his work. The editors of this book would like to thank all those who helped enhance the narrative of this book through their expert reviews:  Amélie Aubert-Plard  ( Danone Nutricia Research ),  Damien Conaré  ( Montpellier SupAgro ,  UNESCO Chair in World Food Systems ),  Jesus Contreras  ( University of Barcelona, ODELA ),  Dominique Desjeux  ( Argonautes, ANTHROPIK, CEPED ),  Tristan Fournier  ( CNRS, IRIS ),  Véronique Pardo  (CNIEL, OCHA),  Amandine Rochedy  ( University of Toulouse-Jean Jaurès, CERTOP ),  Christophe Serra-Mallol  ( University of Toulouse-Jean Jaurès, CERTOP ) and  Sophie Thiron  ( CIRAD, UMR MOISA ).
This translation was publicly funded through  ANR  (the  French National Research Agency)  under the  Investissements d'avenir programme  with the reference  ANR-10-LABX-001-01 Labex Agro  and coordinated by  Agropolis Fondation. They are also grateful to the  French Centre national interprofessionnel de l' économie laitière (CNIEL) , and particularly the  CNIEL Observatory of Eating Habits (OCHA) ,  Danone Nutricia Research , the  French Agricultural Research Centre for International Development (CIRAD)  and the  UNESCO Chair in World Food Systems of Montpellier SupAgro and CIRAD  for their financial support for the publication of this book.
Acknowledgements The author would like to thank  Farid Ghrich , who contributed to the field survey via interviews of men and teenage girls, for his intellectual involvement throughout this phase, as well as the student researchers  Saad Jafri ,  Soufiane Lachhab ,  Kchikech Zakia ,  Soumia Ezzikouri  and  Errifaiy Amina , who facilitated contacts in the field and helped transcribe the interviews. I also thank  Nicolas Bricas ,  Olivier Lepiller ,  Audrey Soula  and  Chelsie Yount-André  of the  CIRAD joint research unit (UMR) MOISA  for their support and critical rereading of this chapter. This research was carried out as part of a collaboration between  CIRAD  and  Danone Nutricia Research  and benefited from fruitful discussions with teams in Morocco and France.
Acknowledgements The author would like to thank  Claudia Giacoman  for her contribution to the survey analyses, as well as the entire editorial committee of this book, and particularly  Chelsie Yount-André  and  Audrey Soula  for their painstaking revision of this chapter. She also thanks  Danone Nutricia Research, OCHA  and  CIRAD  for their involvement in organizing the Eating in the City symposium in Paris, as well as the  UNESCO Chair in World Food Systems , and especially  Nicolas Bricas  and  Damien Conaré  for their initiatives and support.
Acknowledgements This study was carried out as part of the Project for  Supporting Family Farming Through Institutional Purchasing in Togo , implemented by  Acting For Life (Estelle Dandoy)  with the  Togolese NGO OADEL (Tata Ametoenyenou ) and financed by the  Fondation de France, CFSI  and  AFD (Pafao Programme ). The author warmly thanks  Nicolas Bricas  ( CIRAD ) for scientific supervision of this study and his advice during the drafting of this text, as well as  Ami Ezin  for conducting, transcribing and translating the interviews in Lomé. Finally, the author expresses her gratitude to the coordinators of this book, the proofreaders and appraisers for their invaluable advice, corrections and suggestions for improvement.
Acknowledgements The author would like to thank  Agrisud International , and especially  Yannick Huet  for his support for the research presented in this chapter, as well as  Alain Leplaideur ,  CIRAD ,  IRD  and  AFD  for providing crucial scientific and financial support. She would also like to thank  Simplice Dossou-Gbété  ( University of Pau, France ) for data processing training, as well as the appraisers for their enhancements of this chapter.
Acknowledgements The author would first like to thank the families of  Kampung Melayu  for the wealth of exchanges that laid the foundations for this research. The field investigation was greatly facilitated by the team of the  Department of Health Education and Behavioural Sciences, Universitas Indonesia ,  Dr Evi Martha ,  Tiara Amelia  and  Andham Dewi. This research was cofunded by  Agropolis Fondation  in the framework of.
Acknowledgements This chapter was based on the results of thesis research conducted under the supervision of  Dominique Desjeux , which partly benefitted from the financial support of  Danone Nutricia Research. Prof. Yang Xiaomin , the team from the  Guangdong University of Foreign Studies  and  Prof. Wang Lei  facilitated the analysis of the findings of the surveys conducted in Guangzhou (China). Finally, the author would like to thank the entire symposium and editorial team for their support and advice.
Acknowledgements The author would like to thank-for facilitating the conditions under which the survey was carried out-the institutions to which she is affiliated, including IEDES (particularly  Pierre Janin ) and the  Nutriset Group , the people who familiarized her with bâbenda and helped her in the data collection (especially Alizeta, Adjara, Judith and Colette), as well as all the bâbenda producers and eaters she met. She also thanks the organizers of the Eating in the  City Symposium  for the opportunity to contribute, to the coordinators of this book, as well as the reviewers and evaluators of this chapter for their proofreading and suggestions for improvement, and the translator for his translation. Finally, she would like to thank  Jean-Pierre Héron  and  Samuel Legendre  for their support, proofreading and advice during the writing of the many versions of this paper.
Acknowledgements This research was carried out as part of a collaboration contract between UCAC, CIRAD and  Danone Nutricia Research , which aimed to highlight food styles among children aged 0 to 3 years in African cities. The author would like to thank  Ms Ghisleine Ouokouomi Djouonang  and  Ms Anne Laurence Hye Ndindjock , MSc students in Project Management and Development in Africa, who contributed to the collection of ethnographic data in Douala and Yaoundé. This chapter also benefited from the criticism and comments of pediatricians and nutritionists attending the feedback workshops in Abidjan (July 2013) and Yaoundé (March 2014).
Acknowledgements The author would like to thank the  UNAM   Post Doctoral Scholarship Program  for supporting this research over the September 2015 to August 2017 period, as well as the  PROSPERA Social Inclusion Program  and its beneficiaries who devoted their time to interviews. She also thanks  Miriam Bertran  and all members of the permanent seminar of the  Food Observatory in Mexico  for their comments throughout the research process, in addition to the anonymous evaluators and the Eating in the City symposium and book editorial team for their help in enhancing this chapter.
The Sentinel-3A/B tandem phase lasted from early June to mid-October 2018 and provides a unique opportunity to increase knowledge of payload differences, homogenise datasets by defining appropriate adjustments, and to reduce uncertainties when comparing data  [3]. It was followed by a short drift phase during which the Sentinel-3B satellite was progressively moved to a specific orbit phasing of 140 • separation from the sentinel-3A satellite. This study bases on the results of a homogenisation and harmonisation methodology for the two OLCI-A and OLCI-B payloads developed through the Sentinel-3 for Climate Study (S3TC), an ESA-funded project (s3tandem. eu), and described in  [4]. Homogenisation first adjusts the slight spectral and spatial differences of the two sensors. The comparison of homogenised L1 reflectances acquired during the tandem phase provides evidence of radiometric biases of 1 to 2% between the two sensors, OLCI-A being found brighter than OLCI-B.
As the biases revealed by the tandem configuration were not expected from the in-flight calibration, we have all latitude to think that the root cause of the calibration differences between OLCI-A and OLCI-B may not have the same impact over the mission lifetime. Therefore, it is of high interest to search for a method that could provide the best achievable accuracy in the monitoring of such unexpected calibration differences. The precision obtained from the tandem phase comparisons (better than 0. 5%) is hardly achieved by traditional radiometric validation techniques  [7]. However, the precision obtained in the tandem comparisons provide a cross-calibration "truth" against which such traditional techniques can be tested, validated, and eventually improved. This makes the tandem phase dataset a very valuable tool for the calibration/validation community, and presently for our purpose.
Instruments, Datasets, and Selection of DCC Observations The selection of DCC observations, among other cloudy targets, relies notably on an estimation of the altitude of the cloud, which can either be obtained from strong absorption features (O 2 , H 2 O absorption channels) or by thermal information. A common selection criterion of DCC targets employs the 10-12 µm window region brightness temperature  [9] , an alternative in  [12]  uses an empirical threshold on the strong water vapour absorption channel at 1375 nm. Although the objective of this study is focused on OLCI, the provision of 10-12 µm brightness temperature is provided by the synergetic use of SLSTR on-board Sentinel-3, both instruments are briefly described below along with the datasets provision and criteria for the selection of DCC observations.
Datasets As in  [4] , the tandem phase datasets used in this work were collected in the frame of the Sentinel-3 Tandem for Climate project. The project data archive contains Sentinel-3A and Sentinel-3B data for all instruments, collected one day per week (Monday) between 25 June 2018 and 18 March 2019. This includes tandem (6 June 2018 to 15 October 2018), drift (22 October 2018 to 19 November 2018) and post-tandem phases. More details on the project can be found in  [4]. These 'baseline' products were collected from the Payload Data Ground Segment (PDGS, Land and Marine centres). For our purpose, we use OLCI L1B Full Resolution (FR) products from which the harmonisation methodology in  [4]  has been developed. SLSTR S8 brightness temperatures (BT) are provided from the L1 products available over the same period.
We only make use of tandem data from this archive. More recent data are taken from the rolling archive of the Sentinel-3 Mission Performance Centre  [15] , it covers the months of November 2019 as well as January, March, and May 2020. Preliminary choice has indeed been made to analyse one month over two. None of these datasets are provided with the radiometric alignment of OLCI-A and OLCI-B performed in  [4]  as the objective of this work is to assess the ability to retrieve the cross-calibration information extracted from the tandem analysis but, in fine, without the use of the tandem configuration. All OLCI and SLSTR acquisitions are taken at Full Resolution (FR), it is however convenient to consider blocks of 20 × 20 pixels (in the following called 'macropixels') instead of individual pixels, all the more as it facilitates the handling of a very large volume of data.
Data Preparation, Selection of DCC Observations, Saturation Tagging OLCI L1 radiances and SLSTR L1 BT products are subdivided into granules, elements of orbits corresponding to three minutes of acquisition (about 1000 km along-track). As in  [4] , it is most useful and relevant to reproject acquisitions over the same grid prior analysis (see  [4]  for details), discarding pixels for which any considered sensor is missing. This reprojection is performed similarly for all useful parameters, owing to the L1B geolocation information: radiance measurements, auxiliary data (ozone content, solar irradiance, geometry of acquisition, detector indices). For tandem phase products, OLCI-A, OLCI-B, and SLSTR-A are coregistered through such reprojection. Out of tandem, OLCI-A and SLSTR-A (resp. OLCI-B and SLSTR-B) are coregistered similarly, individually per pair of Sentinel-3 instruments as the Sentinel-3A and Sentinel-3B platforms are on separated orbital tracks.
One reference band is considered at 412 nm (band Oa02) where saturation very seldom occurs. Saturation shows up with very abnormal values (see Figure  2 ) which are traced within the OLCI processing chain from raw data packets up to the next levels. DCCs are among the brightest targets on earth, as shown above it is not uncommon to spot reflectance higher than unity induced by optical effects. Consequently, saturation may occur at detector level when the input radiance exceeds the energy flux allowed by the detector sensitivity. It is worth mentioning that such radiance levels can be considered for optical sensors as upper limits for the dynamic range allowed in sensors design. In OLCI, most differences in the sensitivity to high radiances are found between the spectral bands as each band is designed with a sensitivity that depends on its dedicated application.
One reference band is considered at 412 nm (band Oa02) where saturation very seldom occurs. Saturation shows up with very abnormal values (see Figure  2 ) which are traced within the OLCI processing chain from raw data packets up to the next levels. Figure  2  highlights the difference between OLCI-A and OLCI-B, the latter being much less affected by saturation. Indeed, a statistics of the saturated pixels is provided below in Figure  3  per spectral channel and detector index from a large number of DCC observations for OLCI-A (left) and OLCI-B (right). The figures obtained for OLCI-B are relevant to the expectation of the mission design: all bands seldom saturate to the exception of the bands with strongest absorption (Oa14 for O 2 and Oa20 for H 2 O) which are designed to be sensitive to absorption in clear atmospheres.