dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis. Results were expressed as the mean ± SEM and data were analysed by means of one way analysis of variance (ANOVA) and two-way ANOVA to assess the differences among various groups. Statistical calculations were performed with the help of Graph-Pad prism version 6. 0, Graphpad software Inc San Diego, California, USA. Significance was indicated as *** for P < 0. 001; ** for P < 0. 01 and * for P < 0. 05. Student T-test was used to study difference in biochemical parameters between different treatment groups. The difference with P < 0. 05 considered to be significant. Kaplan-Mier Curve was used for survival analysis of the treated groups.
Acknowledgements The authors are thankful to the  Coordinator of Interdisciplinary Biotechnology Unit, AMU, Aligarh, India  for providing the institute's research facilities. The authors gratefully acknowledge the financial support by the  Indian Council of Medical Research and DBT, Govt. of India  ( BMS/NTF/9/10/11  and  TATA Innovation (DBT, GOI)  fellowship to M. O. ). Syed Mohd Faisal is thankful to the  Department of Biotechnology, Govt. of India  for providing him a  DBT-SRF Fellowship.
Conclusion After tried to use the framework on the Glass Museum of Shanghai, we find out the positive comments from the staff and managers of the museum. The activities according to the framework will be carried out in the future and we will keep following the data and feedback. In this case, during the innovation process, design played a core role in the whole team and shows the leading innovative strategy in the process. From the feedback of the case, we tried to embed the double diamond design model (original from the British Design Council) into our innovation framework and tries to add more process value into it. (figure  18 ) In the coming future, we will be facing problems in a more complex, scalable and scoped level, and there will no longer be single correct answers to solve them.
* S Supporting Information The Supporting Information is available free of charge on the ACS Publications website at DOI: 10. 1021/acsomega. 8b03634. General experimental details, synthesis of known compounds, X-ray crystallographic data and NMR spectra for products, conversion/time data, peptide stapling data, and molecular modeling results (PDF) Empirical details of absorption correction using spherical harmonics and frame scaling in SCALE3 ABSPACK algorithm; list of runs; symmetry information for shelxl refinement (CIF) (CIF).
Background: Trends in cause-specific mortality in most African countries are currently estimated from epidemiological models because the coverage of the civil registration system is low and national statistics on causes of death are unreliable at the national level. We aim to evaluate the performance of the death notification system in Antananarivo, the capital city of Madagascar, to inform cause-of-death statistics. Methods: Information on the sex of the deceased, dates of birth and death, and underlying cause of death were transcribed from death registers maintained in Antananarivo. Causes of death were coded in ICD-9 and mapped to cause categories from the Global Burden of Disease 2016 Study (GBD). The performance of the notification system was assessed based on the Vital Statistics Performance Index, including six dimensions: completeness of death registration, quality of cause of death reporting, quality of age and sex reporting, internal consistency, level of cause-specific detail, and data availability and timeliness.
We redistributed garbage codes and compared cause-specific mortality fractions in death records and estimates from the GBD with concordance correlation coefficients. Results: The death notification system in Antananarivo performed well on most dimensions, although 31% of all deaths registered over the period 1976-2015 were assigned to ICD codes considered as "major garbage codes" in the GBD 2016. The completeness of death notification, estimated with indirect demographic techniques, was higher than 90% in the period 1975-1993, and recent under-five mortality rates were consistent with estimates from Demographic and Health Surveys referring to the capital city. After redistributing garbage codes, cause-specific mortality fractions derived from death notification data were consistent with GBD 2016 for the whole country in the 1990s, with concordance correlation coefficients higher than 90%.
The BMH issues burial permits, but does not officially register deaths; relatives need to report the deaths to the vital statistics office. All records were transcribed from registers maintained by the Municipal Office of Hygiene to build a database covering the period from 1976 to 2015. Previous research analyzed this database to document the mortality crisis that affected the country in the mid-1980s  [9]  or to provide an overview of trends in allcause and cause-specific mortality  [2, 10]. In this study, we evaluate the quality of the data based on a synthetic index called the Vital Statistics Performance (VSP) Index, developed by Phillips and colleagues  [11]. The VSP index has six dimensions: quality of cause of death reporting, quality of age and sex reporting, internal consistency, completeness of death registration, level of cause-specific detail, and data availability and timeliness.
We show that the system in place in Antananarivo performs reasonably well on these six dimensions. The VSP index mainly focuses on the characteristics of the data but does not examine the validity of registered causes of death. Validation studies are very complex, and in Madagascar, there is no solid, independent validation reference to evaluate death notification or registration. In the absence of such reference, we compare cause-specific mortality fractions (CSMFs) with estimates from the GBD 2016 Study. Because GBD estimates refer to the entire country, we expect some differences with death notification from the capital city, but the nature of these deviations can be anticipated. The capital city is better equipped with health facilities than the rest of the country and benefits from a favorable location in the central highlands of Madagascar resulting in few autochthonous malaria cases (malaria is endemic in coastal areas).
The office covers the six central districts of Antananarivo-Renivohitra, corresponding to about 1 million inhabitants in 2009, according to a provisional count made in preparation of the national census (postponed until 2018 due to political instability). The database contains about 315 000 deaths for the period 1976-2015 (excluding stillbirths), with 250 000 deaths of residents. About 82 000 deaths occurred in children under age  15. Deaths that took place in medical facilities are sometimes reported directly to the BMH with a code corresponding to the 9th revision of the International Statistical Classification of Diseases (ICD), as provided by the medical personnel. Other deaths, for which a cause of death was noted in plain text in the register, or for which a code corresponding to another revision of the ICD was available, were coded using ICD-9 by one physician to construct the database used for this study.
To estimate completeness of death reporting in children, we also computed under-five mortality rates from full birth histories collected in Demographic and Health Surveys, extracting from the datasets only the records of women interviewed in the capital city, and pooling all surveys together. Mortality rates were obtained based on a synthetic cohort approach  [21]. We compared trends in child mortality from death notification and DHS estimates. Finally, we compared mortality rates derived from death records with that of the GBD 2016 Study for the whole country. We estimated the population at risk of dying based on a mixture of administrative counts, censuses and sample surveys (see Additional file 1).
Comparison with the GBD estimates of cause-specific mortality Comparing cause-specific mortality fractions in GBD and in death records requires correcting for garbage coding in the registers. As part of the GBD, different algorithms are used for garbage code redistribution (based on fixed proportions, regression models, and proportional reassignment and based on multiple causes of death data). Most of the code used in the GBD 2016 Study for the garbage code redistribution is distributed in the public domain. However, to our knowledge, the full list of target codes (i. e. , valid causes) for each garbage code is not yet available. In addition, garbage code redistribution is an integral part of the estimation process of the GBD Study, and it is difficult to replicate only this segment of the analysis as an independent exercise on a specific database.
Fourth, ICD-9 codes from Chapter XVI (symptoms, signs and illdefined conditions) were redistributed pro rata across all GBD causes (excluding injuries) within each age group, sex, decade, and season of death. There are strong weather variations in Antananarivo, and mortality rates from infectious diseases and nutritional deficiencies in children peak in the hot and rainy season (from November to April), while mortality rates in adults from cardiovascular diseases and diseases of the respiratory system peak in the dry season. Fifth, we redistributed pro rata by age and sex injuries undetermined whether accidentally or purposefully inflicted and all ICD-9 codes from Chapter XVII (injury and poisoning) to the GBD categories for intentional and unintentional injury. Sixth, for all remaining level 3 garbage codes represented in our database, we identified the most likely level 2 cause (e.
Internal consistency of data In 98. 9% of deaths (excluding those assigned to garbage codes), the GBD category of causes of death is consistent with the age and sex of the deceased. In 0. 6% of cases, the age at death is too young for the assigned cause. This is mostly the case of deaths among infants and classified as due to chronic obstructive pulmonary disease, originally coded in ICD-9 as bronchitis (490, 491. 8). In 0. 3% of deaths, the inconsistency is due to neonatal sepsis and other neonatal infections assigned to deaths that occurred in the post-neonatal period.
Trends in child mortality (for both sexes) from death notification data are compared with GBD 2016 estimates and DHS mortality rates for the capital city in Fig. 3a  [25]. Child mortality rates estimated for Antananarivo are lower than GBD estimates, but this pattern is expected, due to the city's more advantageous health and economic development. Estimates from death notification depict a similar rate of reduction as in DHS, but at a slightly higher level, indicating good completeness of registration of deaths in children. Figure  3b  displays trends in life expectancies from GBD and estimates from death records. The health crisis in the 1980s is instantly recognizable in these trends. A resurgence of infectious diseases, respiratory, and nutritional deficiencies resulted in a drop in life expectancy of 12 years for men (7 years for women) in the capital  [2, 9].
Level of cause-specific detail Death notification data provide information on 128 of the 132 causes of death (level 3) apparent in GBD estimates for Madagascar. The four causes that are not mentioned in the records are dengue fever, iodine deficiency, cystic echinococcosis, and conflict and terrorism. The level of detail has improved over time, from about 95 different causes reported annually before 1980 to about 102 annually since 2010.
94 to 0. 97) for men in 1990 and 0. 95 for women (95% CI 0. 94 to 0. 96). The largest difference in terms of percentage of deaths assigned to a specific cause relates to measles, which represents 4. 5% of male deaths (95% UI 1. 7-9. 4) and 4. 9% of female deaths (95% UI 1. 9-10. 1) according to the GBD, but caused only 0. 5% of male deaths and 0. 8% of female deaths in the registers in 1990. However, there is substantial uncertainty around annual estimates for measles due to its epidemic nature. In terms of broad categories of causes of death, there is a very high consistency between estimates in 1990 (Table  2 ), reflecting the fact that GBD estimates were modeled based on the same data from the capital in this period. Deviations between estimates from GBD and those derived from death registers increase over time, especially once death notification data are no longer available to inform the models (after 1995).
Discussion Outside of South Africa, death registration systems in sub-Saharan Africa are deemed rudimentary or non-existent, because most assessments are conducted at the national level. Due to the lack of registration data, cause-specific mortality rates are mainly estimated based on epidemiological models by UN agencies and academic groups, such as the Institute for Health Metrics  and Evaluation (IHME), which leads the Global Burden of Disease Study. For data-sparse countries, the World Health Organization now largely resorts to GBD estimates to obtain cause-specific mortality fractions  [22]. The GBD Study uses all the data available, including verbal autopsies studies in countries with incomplete vital registration, sometimes at the subnational level. However, persistent data gaps remain, and innovative ways to reduce these gaps are needed.
In this study, we evaluated the performance of the death notification system in place in Antananarivo. We estimated that the completeness of death reporting among adults was higher than 90% in the period 1975-1993. Recent estimates of under-five mortality are consistent with trends derived from Demographic and Health Surveys and even provide higher mortality rates than in DHS. We also showed that life expectancies computed from death notification data are similar to estimates in the GBD 2016 study for men (they fall within the 95% confidence intervals in the GBD), but we observed larger deviations in the levels of female life expectancy. In the period 1990 to 2015, the average difference between the female life expectancy according to GBD and that obtained from death notification was 4. 4 years, against only 0.
9 years among men. We can reasonably assume that life expectancies are higher in Antananarivo than in the whole country, so these differences go in the expected direction. However, differences by sex in the agreement between series could point to some level of underreporting of female deaths in the death notification system, or to low male-to-female sex ratios of mortality in the GBD, or a combination of both. The system also performs well on four other dimensions of the VSP index: quality of age and sex reporting, internal consistency, level of cause-specific detail, and data availability and timeliness. However, in terms of quality of cause of death reporting, an important proportion of deaths were assigned to major garbage codes (31% over the period. This proportion rises to 51% among older adults aged 70 and above.
Lowering the percentages of deaths assigned to garbage codes (especially ill-defined cardiovascular diseases, ICD-9 codes for symptoms, signs and ill-defined conditions, or injuries undetermined whether accidentally or purposefully) is the main way to improve the system in place in Antananarivo. Despite this high percentage of garbage codes, after redistribution, we observed strong concordance between cause-specific mortality fractions in GBD estimates and death records in 1990 (concordance correlation coefficients were higher than 90% in both sexes). This suggests that a simplified algorithm to redistribute garbage codes can provide plausible estimates. Authors of the GBD Study had access to some death counts from the same registers, from 1984 to 1995. Hence, this high concordance is largely a result of GBD estimates being modeled on the basis of notification data during this period, although one would have expected larger differences since data from the capital city should not be considered representative of the national level.
Differences between the two series are more pronounced in 2015, with concordance correlation coefficients in cause-specific mortality fractions declining to 0. 74 (95% CI 0. 66-0. 81) for men and 0. 81 (95% CI 0. 74-0. 86) for women. This decline is presumably due to the absence of mortality measurements in the input database for Madagascar in recent years. The epidemiological transition seems to have progressed much faster in Antananarivo than according to GBD estimates for the country. This could reflect real transitions. However, the decline in concordance over time, as the availability of mortality measurements is reduced, also suggests that there remains room for improvement when predicting the distribution of causes of death based largely on covariates. Our evaluation of death notification data has three main limitations.
First, the population at risk of dying is difficult to estimate in the absence of recent census data. Under-five mortality rates and trends in life expectancy inferred from the death notification could be biased if our population estimates are inaccurate. However, this should not affect our observations about gender differences in life expectancy. Madagascar has conducted a new census in 2018, and this new census will help in estimating population exposure for future analysis. Second, deaths associated with garbage codes were redistributed by adopting an approach that differs from redistribution algorithms used in the GBD. More research efforts should be devoted to developing a publicly available list of target codes (i. e. , valid causes) for each garbage code and facilitating the redistribution of garbage codes in specific databases without replicating the whole GBD exercise.
Conclusion This study illustrates the potential of urban vital registration for real-time mortality monitoring, and improvement of disease burden estimates, provided that biases related to the urban location of the data source are adjusted for. Although we report on the situation in Antananarivo only, other major cities in Madagascar have a similar system in place, and some have carefully preserved death records (such as in Antsirabe and Fianarantsoa). These records remain largely untapped, and to our knowledge, the quality of data collected in these cities has not been evaluated. There are significant differences in the structures and processes of the municipal health offices across the country, but all offices tend to be in short supply of resources to store the registers and encode them in databases.
Forcing data The model required meteorological data (including air temperature, humidity, pressure, wind speed, rain and snow precipitation, and incoming long-wave and short-wave radiation) to provide the atmospheric forcing at the upper boundary of the model domain. We used the same forcing dataset as  Westermann et al. (2016) , which covers the period from 1979 to 2014. These data are based on in situ observations from Samoylov Island for the period from 2002 to 2014  (Boike et al. , 2019). Downscaled ERA-Interim reanalysis data were used to infill gaps during this period and to obtain forcing data for the period from 1979 to 2002 (see  Westermann et al. , 2016 for details). In order to allow long-term simulations under present-day climatic conditions, the dataset was extended to 2040 by repeated appending of the forcing data from the period between January 2000 and December 2014 to the end of the original forcing dataset.
In situ measurements The in situ measurement data came mainly from the longterm records of the Samoylov Permafrost Observatory described in  Boike et al. (2019). This dataset contains vertical soil temperature and soil moisture profiles of different microtopographic units of the polygonal tundra (one profile for centre, slope, rim, and ice wedge, respectively; see Fig. 1  for the location of the measurement polygon), as well as water table (WT) records for an adjacent polygon centre. We also used the active layer thickness (ALT) time series from the Samoylov Circumpolar Active Layer Monitoring (CALM) site which cover different microtopographic units of polygonal tundra, including polygon centres ("wet tundra", n = 39 measurement points) and rims ("dry tundra", n = 80 measurement points)  (Boike et al.
Together with the companion paper by  Aas et al. (2019) , this work marks a significant step forward for the representation of small-scale spatial heterogeneity in permafrost landscapes within the land surface schemes of ESMs. Code and data availability. The model source code used for the simulations in this work has been archived on Zenodo  (Nitzbon et al. , 2019a). The measurement data from  Boike et al. (2019)  used for evaluation of the model results are available from PANGAEA  (Boike et al. , 2018). Table  C1. Topological parameter values assumed for the long-term (60-year) runs in this study. The total area of a polygonal structure (A tot = 140 m 2 ) and the areal fractions γ α are based on estimates for Samoylov Island given in  Muster et al. (2012). All other topological parameters are calculated from these estimates using the formulas given in Appendix C.
Acknowledgements. Jan Nitzbon  is thankful to the POLMAR graduate school and the  Geo. X Young Academy  for providing a supportive framework for his PhD project. Jan Nitzbon  is thankful to  Tress Academic  for helpful courses on scientific writing and project management. The authors gratefully acknowledge the  Climate Geography Group at the Humboldt University of Berlin  for providing resources on their high-performance-computer system. This work was supported by the  Research Council of Norway  through the  PERMANOR  project (no. 255331 ). This work was supported by the  Federal Ministry of Education and Research (BMBF) of Germany  through a grant to  Moritz Langer  (no. 01LN1709A ). The article processing charges for this open-access publication were covered by a  Research Centre of the Helmholtz Association.
Objectives: To evaluate the clinical and economic burden of head and neck squamous cell carcinoma (HNSCC) in France. Methods: All 53,255 incident adult patients discharged with a first diagnosis of HNSCC in 2010-2012 were identified from the 2008-2013 French National Hospital Discharge (PMSI) database. We conducted a retrospective longitudinal analysis of prognosis and direct costs attributable to HNSCC. Results: Direct medical costs attributable to HNSCC care amounted to 665 million euros in 2012 in France. The majority (62%) of incident patients were 64 years old or less at HNSCC diagnosis and incurred 1. 3-fold higher mean direct costs as compared to elderly patients (41,909 vs 32,221 euros over 3 years, respectively; p<0. 001). HNSCC stage at initial treatment was the major driver of mean (SD) direct costs over 3 years (p<0.
Data source The data source was the French National Hospital Discharge (PMSI) database in the years 2008-2013. The database contains all public and private hospital claims for acute and post-acute care. The standardized discharge summary includes: patient's demographics (gender, age, postal code of residency); primary and associated discharge diagnosis codes according to the WHO International Classification of Diseases, tenth revision (ICD-10); medical procedures performed; length of stay, entry and discharge modes (including in-hospital death). Using unique anonymous identifiers, we conducted a retrospective longitudinal analysis of prognosis and direct costs attributable to HNSCC care. 15, 16.
Study population We included all adults residing in metropolitan France and discharged with a primary or associated discharge diagnosis code of HNSCC (ICD-10: C00-C06; C09-C14; C30. 0; C31; C32) in the years 2008-2012 (see the full coding dictionary in Supplementary Table  S1 ). To minimize a possible misclassification bias of a relapse case as an incident case, we excluded all patients first diagnosed with HNSCC in 2008-2009 or recorded with a personal history of cancer at first diagnosis of HNSCC in 2010-2012. 17 For all selected patients, hospital discharge data of the year 2013 were used to ascertain vital status and refine direct costs until fixed censoring at July 1, 2013 (see Supplementary Methods and Tables  S1-S4 ).