dataesr/scientific-paragraphs-categorization huggingface dataset:
Strengths and limitations The strengths of the study include the large sample size, the use of predominantly nationally representative datasets, and data from a large number of LMICs. However, the results should be interpreted in the light of several limitations. First, all data used in our analysis was based on self-report. Thus, there is the possibility of reporting bias (e. g. , social desirability bias, recall bias). In particular, it is possible for SCC to have affected the reporting of some variables due to memory problems (e. g. , diagnosis of disorders, sleep problems). Second, although a total of 10 chronic conditions were included in the analysis, there may be other conditions such as malnutrition  [48] , stroke  [49] , and HIV infection  [50] , which were not included in the analysis but may be important conditions in relation to cognition.
Third, it is also possible that there was misclassification for chronic conditions based on self-reported diagnosis especially in resource-limited settings as under-diagnosis may be common. Fourth, we used two questions on SCC to capture the level of SCC but there is currently no consensus on SCC measures, and what has been used in previous studies range from a single question to a complex assessment involving multiple questions. Therefore, the results may have differed if a different measure of SCC was used. Fifth, the data were collected in 2002-2004, and thus, it is possible that the results do not reflect the current situation as multimorbidity, demographic, and cognitive profiles of the populations studied could have changed. However, we are not aware of any other population-based data with such a large number of LMICs that can be used to investigate the Multimorbidity-SCC relationship.
In resource-limited countries, such maps are especially meaningful for allocation of limited medical resources to where most needed in a cost-effective manner. In this study, we aimed to apply geostatistical models to estimate the spatial-temporal distribution of RTIs at high spatial resolution in Bangladesh, based on both the survey data from Demographic and Health Survey (DHS) Programs in the years 2007, 2011 and 2014, and other datasets with environmental, climatic and socioeconomic information, providing policy makers with disease distribution data for this period, in order to inform interventions, and to provide a basis for future RTI research.
Data We obtained health related data of child-bearing age women in Bangladesh from three latest available Demographic and Health Survey (DHS) conducted in 2007, 2011 and 2014. DHS, began in 1984, are nationallyrepresentative household surveys that provide data of a wide range of issues including birth rate, mortality, migration, family planning, maternal and child health, nutrition, family living conditions and education in developing countries  [21]. Bangladesh DHS (BDHS) was conducted every three or 4 years from 1993 up to the present. The most recent survey was in 2017-2018, however, the corresponding data hasn't been published yet. A two-stage cluster sampling design was used. Firstly, the country was divided into a number of enumeration areas (EAs) and a certain amount of EAs were sampled according to the proportion of urban and rural areas.
EAs, known as survey clusters, are city blocks or apartment buildings in urban areas, or villages or groups of villages in rural areas, which cover an average of 100-120 households. Secondly, 30 randomly selected families from each cluster were surveyed, resulting in indicators not only nationally representative but representative at the lower level of DHS regions and urban/rural residence  [22]. Family-, women-(age , and health-related information, such as house situation, reproductive history, marriage, were collected using standard questionnaires. Coordinates of clusters were available as geo-located data. However, in order to maintain privacy of respondents, the geo-located data is displaced, which leaves the coordinates of all cluster containing random errors: i) urban clusters contain between 0 and 2 km of error; ii) rural clusters contain 0 and 10 km of error  [4].
These errors may result in misclassified assignments of predictor variables in geostatistical analysis. Nevertheless, according to the guidelines on the GPS data published by DHS, point extraction provided adequately unbiased estimates for most surface types other than highly non-smooth surface  [22, 23]. We fitted simultaneous autoregressive regression models, proposed by the guidelines of DHS (see Additional file 1 for details), to test the smoothness of the predictor surfaces in our study. In consistent with other researches, we defined a woman reported with RTI symptoms if she reported to have abnormal genital discharge or genital sore/ulcer during the last 12 months  [4, 24]. This and the potential influencing data (i. e. , wealth index of household, education years of women, education years of husbands, rate of births attended by a skilled provider, number of children in a household, coverage of improved toilet facilities) were extracted from DHS dataset at individual level.
Data processing We summarized the DHS individual-level data to cluster-level (i. e. , EAs) according to the same coordinates. Environmental, climatic and socioeconomic data at the survey clusters were extracted using 'raster' package in R (version 3. 5. 0). Continuous data was standardized to mean zero and standard deviation one. To avoid collinearity in the subsequent modeling analysis, we calculated the Pearson's correlation coefficient between each pair of continuous variables. If the coefficient was greater than 0. 8 in one pair of variables, the one among the pair, which was more meaningful or with better data quality was retained. The environmental and socioeconomic data obtained from data sources other than DHS database were at high spatial resolution (1 × 1 km 2 ). Except data of water bodies, the data at pixels nearest to the clusters were extracted for the corresponding clusters.
Considering the identification issues between the main fixed effect and the random effects, we set a sum-to-zero constraint to the location-specific effect and an integrate-to-zero constraint to the spatialtemporal random effect  [32]. We adopted Bayesian inferential framework to estimate the parameters as well as hyperparameters. We fitted the model under Bayesian inferential framework using the INLA package in R (version 3. 5. 0) with INLA-SPDE approach  [28, 33]. We didn't have much information to specify precise prior distributions for the parameters. To avoid subjectivity in the choice of priors and to keep inferences in a reasonable range, we used minimally informative priors. They were set as distributions with large variances, covering a wide range of reasonable values according to the characteristics of parameters, thus provide good representation of genuine ignorance about the parameters and do not affect strongly on the posteriors.
We carried out 10-fold cross-validation to assess the model performance. Survey locations were randomly divided 10 times in 90% (training set) and 10% (validation set) splits and the following performance indicators were calculated: (i) mean error (mean of observed prevalence minus predicted one), and (ii) the percentage of observations covered by 95% Bayesian credible intervals (BCI) of posterior predicted prevalence  [17]. Estimates of RTI risk for each survey year were done using the INLA package over a grid of 7137 pixels across Bangladesh at 5 × 5 km spatial resolution. Pixel-level number of infected women was also calculated (Additional file 1). The results were mapped using ArcGIS 10. 0. Furthermore, we calculated population-adjusted prevalence (median and 95%BCI) for all the 8 divisions (administrative divisions of level one, ADM1) and 64 districts (administrative divisions of level two, ADM2) in Bangladesh, by summarizing the pixel-level estimates to the corresponding ADM1 or ADM2 level.
Data summaries We included the surveys of 46,701 women (i. e. , 10,996 in 2007, 17,842 in 2011 and 17,863 in 2014), aged 15-49 years old, who completed the DHS standard questionnaires, for the subsequent analysis. Summarized of the data resulted in a total of 1560 clusters, of which 361 in 2007, 600 in 2011 and 600 in 2014 (Fig. 1a, b  and c , respectively). One cluster with 33 women surveyed in 2014 was not included in the geostatistical analysis due to the absence of coordinates. A summary of the surveys is shown in Table  1.
A study shows that women with high income were more likely to use contraceptives than those with low income  [44]. In addition, women in the poor families were intend to have high-risk behaviors, such as earlier childbearing  [45] , earlier sexual debut  [46, 47] , and transactional sex  [48]. We also identified a negative association for RTI risk with elevation and a positive one with distance to the nearest fresh water body. To our knowledge, there are no literatures ever reveal direct relationships of the above environmental factors with RTIs. However, these factors may influence the behaviors and socioeconomic status of people  [49] , thus show significant association with RTIs. To obtain more accurate estimates of RTI risk, we made full use of available data from multiple sources, such as DHS, MODIS/Terra, WorldClim, SEDAC, SWBD and WorldPop (Table  S1 , Additional file 1).
However, other potential influencing factors, such as social attitudes  [50]  and the number of sexual partners  [51, 52] , are not available at sub-national level, thus cannot be included in our analyses. In addition, as we did not obtain data at high spatial resolution for wealth of household, we used district-level data for risk prediction, by assuming levels of wealth within districts were similar, which may lead to spatial misalignment issue and result in bias. For WorldClim and SEDAC data, the data periods are before 2000 and corresponding updated data are unavailable, thus temporal misalignment may exist. Nevertheless, our final model had reasonable predictive ability, as model validation suggests that it was able to correctly estimate 74. 49% of locations within a 95% BCI coverage.
However, the mean error, slightly larger than zero, may suggest a mild underestimate of the model. Considering omitted variable-bias (OVB) issue that results from the elimination of important variables a process where only one of the two highly correlated continuous variables is retained, we went back to the results and found that during this process, only one variable (i. e. , education years of husbands) was dropped due to its highly correlated (with correlation coefficient 0. 92) with the variable "education years of women". From a professional point of view, the education of women themselves may influence the RTI more directly than that of their husbands, thus omitting this variable may cause little OVB. Due to the unavailability of the newest Bangladesh DHS data in 2017, we analyzed the data from 2007 to 2014, which may not fully reflect the most recent situation.
Data and Applications Center; SWBD: Shuttle Radar Topography Mission Water Body Data; VIP: Ventilated improved pit; WOCBA: Women of childbearing age; WorldPop: World population Availability of data and materials The datasets supporting study are available in the following hyperlink: Demographic and Health Surveys (DHS), available at:http://dhsprogram. com/; Moderate Resolution Imaging Spectroradiometer (MODIS)/Terra, available at:http://modis. gsfc. nasa. gov/; WorldClim, available at: http://www. worldclim. org/current; Socioeconomic Data and Applications Center, available at: http://sedac. ciesin. org/; Shuttle Radar Topography Mission Water Body Data (SWBD), available at: http://gis. ess. washington. edu/data/vector/worldshore/index. html; World population, available at: www. worldpop. org.
Rather than ascribing the delay to political economy factors as did Gourevitch, we believe that the impact on these sectors was not immediately as negative as previously thought and that these sectors were more resilient than supposed. We proceed by analyzing several new datasets of dis-aggregated French exports and imports for the decades surrounding 1860. We begin with an analysis of the 104 broad products listed in French official trade statistics. We decompose changes in imports and exports into intra-industry movements, increased specialization and loss of previous specializations using a technique due to  Bastos and Cabral (2007). This decomposition establishes that increased intra-industry trade was paramount in the wake of liberalization. We then turn to an analysis of the top nine largest exports in the broad product classification scheme during the period.
Data To study the impact of liberalization, we assemble three different datasets for French trade between 1848 and 1877. 17 Each data set covers a slightly different range of products or has a different level of disaggregation. Our main data source is the Tableau général du commerce de la France avec ses colonies et les puissances étrangères (Tableau général du commerce et de la navigation after 1896). We use "commerce special" (i. e. , specific trade)" and not "commerce general" (general trade). Data from specific trade includes the value of goods imported for national consumption and the value of national production exported. Ostensibly "specific trade" excludes goods in transit. Trade is based on calendar years. Our first data set covers 104 "broad" products for total imports and exports.
These are broad classifications equivalent to the 2 or 3 digit level in the SITC classification. For these data, we have total values of exports and imports in current French francs. For about 24 of these products customs revenue is available throughout the period 1856-1872. The limited range of products in the broad dataset with customs revenue is due to the fact that the Tableau regularly reported the top import revenue products using broad classifications. These products comprised between 90 and 95% of all tariff revenue collects. 18 With the tariff 17 We provide further details in the data appendix. Data are based on calendar years. 18 The Tableau reports tariff revenue for all other products but at a much more disaggregated level (SITC 4-6 digit level) and bilaterally. It was beyond the scope of this project to collect these observations which would number in the thousands and would require aggregation to the broad product level.
Within this dataset we focus on the top nine exports for France as of the 1850s. These broad products include silk, woolen and cotton cloth, wine, leather products, clothing, tools, raw silk and raw wool. The share of these top nine exports on French total exports is very significant: 53. 1 % in 1848, 56. 1% in 1860, and 50. 4% in 1870. This "top nine" data set will provide a foundation for studying the impact on the most significant industries/products from an export-driven view of the economy. Competition with Great Britain was expected to have been very intense in some of these industries, especially cotton cloth and clothing. For each of these nine products, we have the total value of exports, imports and tariff revenue collected in current French francs. With the tariff revenue for these nine products we can calculate the average tariff within the product class and for all of these products together.
A second data set covers bilateral exports and imports of these "top nine" products for over 151 different countries and territories. Quantities, values and unit values are available. With these data we are able to follow the evolution of bilateral trade at the product level prior to and after the signing of various bilateral trade agreements. A third dataset disaggregates further within the top nine specializations. For the period 1848-1877, we digitized trade data for all sub-products within the top nine specializations. This is the highest level of disaggregation possible in French trade statistics. For this period, the dis-aggregated nomenclature is relatively homogeneous and stable. We have the following sub-product counts within the broad product classes: 4 for clothing, 5 for raw wool, 48 for tools, 24 for leather, 16 for raw silk, 24 for cotton cloth, 20 for wool cloth, 44 for silk cloth, and 4 for wine.
In addition to studying the evolution of MIIT, IPS and SS we also evaluate the impact of liberalization by assigning each product in the broad product dataset a quantitative treatment level. We then relate changes in tariffs and prohibitions to changes in the level of intra-industry trade GL in the spirit of a difference-in-differences test. At the broad product level (104 products) we have tariffs for 24 products prior to and postliberalization. However, many products were prohibited prior to 1860 with a range of tariffs applied post-1860. We cannot reasonably assign a pre-treaty tariff rate to prohibited products. However, we can use a prohibition indicator as a measure of the change in liberalization. We compare the "treated" pre-1860 prohibited goods to all other goods that were not prohibited, and then in robustness checks compare only to goods for which average tariffs stayed relatively constant.
The constant and period dummies allow for common shocks in the post-liberalization period. With this specification we can interpret the coefficients as a difference-in-differences with the preliberalization period of interest 1856-59 and the post-liberalization periods of 1861-64, 1865-68 and 1869-72. Specifications take the following form:   =  + (manufactured  x   ) +   +   with i indexing a product in the set of 104 products in our dataset and t indexing the three post-liberalization periods. Period dummies are collected in. Results for these regressions are presented in Table  2. For manufactured goods in general, the MIIT share was slightly higher in the years following Cobden Chevalier. The column labelled IPS-SS shows that specialization in manufactured goods tended to increase in the years following Cobden Chevalier.
6 mo). Patients were reclassified by risk using International Metastatic RCC Database Consortium (IMDC) criteria. Results and limitations: Sunitinib alone was noninferior to nephrectomy-sunitinib (hazard ratio [HR], 0. 97; 95% confidence interval, 0. 79-1. 19; p = 0. 8) and demonstrated longer median OS (19. 8 mo vs 15. 6 mo, respectively). For patients with two or more IMDC risk factors, OS was significantly longer with sunitinib alone than with nephrectomy-sunitinib (31. 2 mo vs 17. 6 mo, respectively; HR, 0. 65; p = 0. 03). For patients with one IMDC risk factor, OS was longer for nephrectomy-sunitinib versus Please visit www.
20)  [4]. While the CARMENA trial provided practice-changing evidence that patients with intermediate-or poor-risk disease should be not be given CN before starting systemic treatment  [5] , questions remained over the benefit for intermediate-risk patients, given the clinical evidence indicating that selected patients may still benefit from upfront CN  [6, 7]. The International Metastatic RCC Database Consortium (IMDC) risk classification system is a prognostic model for mRCC  [8]. Unlike other risk classification systems such as the MSKCC prognostic model, it is based on outcomes across a wide range of settings (eg, clinical trials, community centers, different countries) in patients who received modern, targeted treatments for mRCC  [9]. In a validation study, the IMDC prognostic model most accurately predicted the number of deaths at 2 yr compared with other widely used models (including the MSKCC criteria)  [9].
Arm A: nephrectomy + sunitinib (n = 127) Arm B: sunitinib alone (n = 139) HR (95% CI) p Overall 19. 0 (16. 0-25. 3) 27. 9 (21. 1-34. 7) 0. 94 (0. 70-1. 24) IMDC risk factors (n = 64) (n = 76) 0. 65 (0. 44-0. 97) 0. 03 17. 6 (13. 7-21. 5) 31. 2 (20. 5-40. 4) 50 events 55 events HR (95% CI) 1. 69 (1. 11-2. 57) 0. 88 (0. 59-1. 30) p 0. 02 0. 5 CI = confidence interval; HR = hazard ratio; IMDC = International Metastatic Renal Cell Carcinoma Database Consortium; ITT = intention to treat; OS = overall survival. E U R O P E A N U R O L O G Y 8 0 ( 2 0 2 1 ) 4 17 -4 2 Post hoc analysis of overall survival in the ITT population stratified by IMDC risk group classification The numbers of patients in the IMDC intermediate-and poor-risk groups were 127/226 (56%) and 99/226 (44%), respectively, in the nephrectomy-sunitinib group, and 139/224 (62%) and 85/224 (38%), respectively, in the sunitinib alone group.
4 3. 3. 43 Arm A: nephrectomy + sunitinib (n = 127) Arm B: sunitinib alone (n = 139) HR (95% CI) p Overall 19. 0 (16. 0-25. 3) 27. 9 (21. 1-34. 7) 0. 94 (0. 70-1. 24) IMDC risk factors (n = 64) (n = 76) 0. 65 (0. 44-0. 97) 0. 03 17. 6 (13. 7-21. 5) 31. 2 (20. 5-40. 4) 50 events 55 events HR (95% CI) 1. 69 (1. 11-2. 57) 0. 88 (0. 59-1. 30) p 0. 02 0. 5 CI = confidence interval; HR = hazard ratio; IMDC = International Metastatic Renal Cell Carcinoma Database Consortium; ITT = intention to treat; OS = overall survival. E U R O P E A N U R O L O G Y 8 0 ( 2 0 2 1 ) 4 17 -4 2 Post hoc analysis of overall survival in the ITT population stratified by IMDC risk group classification The numbers of patients in the IMDC intermediate-and poor-risk groups were 127/226 (56%) and 99/226 (44%), respectively, in the nephrectomy-sunitinib group, and 139/224 (62%) and 85/224 (38%), respectively, in the sunitinib alone group.
Data The variables used in this study are carefully selected based on the availability of data and economic theory  (Du et al. , 2019; Su and Moaniba, 2017). We use the annual data for the period  for the EU-15 countries 10 : Austria, Belgium, Denmark, Finland, France, Germany, Greece, Spain, Spain, Ireland, Italy, Luxembourg, the Netherlands, Portugal, the United Kingdom, and Sweden. Our dependent variable is CO2 emissions (expressed in metric tons per capita). In this study, we rely on the work of  (Du et al. , 2019; Su and Moaniba, 2017)  using patent data -'technologies related to the environment' -to approximate environmental innovations (INNOV). The aggregated category of these technologies includes climate change technologies, water-related adaptation technologies, and environmental management technologies.
Control variables include the share of renewable energy in the total consumption level. The latter is used as a proxy for the renewable energies consumption (REC) and represents a composite variable of consumption of solar, hydroelectric, geothermal, biomass, and wind energy in the total energy consumed. Other determinants include per capita of Gross Domestic Product (GDP) (US $), and the degree of economic openness (OPEN) measured as the sum of imports and exports as a percentage of GDP. With the exception of patent data (OECD database) 11 , all variables were extracted from the World Bank Group Development Indicator Database 12. Descriptive statistics of the variables used in our model are shown in Table  1. We converted our sample to a panel data format, which has the particularity of taking into account the temporal dynamics (adjustment delay, anticipations, etc.
They show that NPE patent portfolios are disproportionately made up of patents that are granted by "lenient" patent examiners who spend relatively little time reviewing and narrowing patent claims. Similarly, Cohen et al. (2019) find that NPEs disproportionately assert patents just before their expiration dates, which they consider an indication of low-quality lawsuits. Tucker  (2014) presents a case study of how the actions of Acacia Research Corporation, a well-known NPE, affect technology sales of US firms in the field of medical imaging technology. By exploiting data provided by the HIMSS Analytics Database, she finds evidence that, relative to similar products made by the same firm but not covered by patents litigated, sales of medical imaging software protected by patents affected by litigation with Acacia fall by around one-third.