dataesr/scientific-paragraphs-categorization huggingface dataset:
Study design This study was based on discharge data from organ recovery performed in the public hospital group 'Hospices Civils de Lyon' (HCL) from January 2010 to December 2011. Direct medical costs were estimated from the hospital's perspective. Direct nonmedical costs and indirect costs were not considered in this study. The timeframe considered was from the beginning until the end of the hospital stay during which the organ recovery was performed. Costs related to family management, liquid preservation, machine perfusion and organ shipment were excluded, because they were not directly related to the surgical procedure.
Study population All brain death donors who underwent kidney, liver, pancreas, intestine, heart, lung or heart-lung block recovery in the HCL between January 2010 and December 2011 were eligible for the analysis. To compare costs for the same donors according to the hospital cost accounting and ENC data, we established a patient selection algorithm that combined the hospital identification code, year of hospitalization, patient age, patient sex, patient DRG and number of procedures performed. Donors were excluded if the selection algorithm could not match them in both databases with certainty. Living donors and donors after circulatory death were also excluded.
Identification and quantification of cost components The resources consumed during the hospital stay and the organ recovery procedure were identified and quantified retrospectively from the hospital discharge abstracts and national discharge abstract database. The eight items identified were surgery, anesthesia, reanimation, intensive care, ongoing monitoring, biology, imaging and medical logistics. Reanimation, intensive care and ongoing monitoring are grouped into the critical care item. All items except medical logistics were subdivided into 5 subitems (medical staff, nonmedical staff, nursing staff, maintenance, depreciation and cost of block occupation). Medical logistics items were regrouped into sterilization, biomedical engineering, hygiene, vigilance and pharmacy.
Valuation of cost components All cost components were valued in euros at 2011 prices. The items identified were first valued using hospital cost accounting, followed by 2010 and 2011 ENC data, and then weighted using the 2010 and 2011 ENC data. All three methods use a top-down microcosting approach as the costing method. Top-down microcosting identifies all relevant hospital services at the most detailed level but values each hospital service per average patient  [15, 16]. Because top-down microcosting does not require patient-level data, statistical analyses of costs cannot be performed, and differences between patients cannot be detected  [15]. However, in France, organ recovery is not a procedure that is classified in a specific DRG. Thus, for the same organ recovery procedure, donors are affiliated with different groups of patients in the local database and with different DRGs in the national database according to different parameters.
The variability in donor distribution facilitates statistical analyses to determine whether cost valuation differences exist among HCL cost accounting, ENC and weighted ENC data for the same hospital stays. In all three cost valuation methods, each surgical procedure related or unrelated to organ recovery is characterized by relative cost indexes (RCIs). A RCI is used to assess the cost of a procedure performed in ideal conditions  [17]. These indexes are commonly used to break down the overall cost of surgical activities according to the number of RCIs specific to each procedure  [18]. For each donor, we assessed a ratio corresponding to the portion of RCIs related to organ recovery out of the total number of RCIs related to surgical activities. Then, surgery, anesthesia, biology, imaging and medical logistics were valued from the cost data weighted by the ratio assessed as related exclusively to organ recovery.
Regarding reanimation, intensive care and ongoing monitoring, discussions with hospital coordinators of organ and tissue procurement allowed us to elaborate the working hypothesis that expenditures related to organ recovery were exclusively focused on the last day of the hospital stay. Thus, reanimation, intensive care and ongoing monitoring were valued from the cost data weighted by the length of stay corresponding to each donor. The organ recovery procedure was assessed with the same approach to reveal the cost differences among the hospital cost accounting, ENC and weighted ENC data.
Analyses Three cost evaluations were conducted, each of which explored a specific characteristic of the organ recovery procedures. All costs are presented according to their means and standard deviations (SDs). • The cost of hospital stays during which an organ recovery procedure was performed was obtained for an overview of hospital costs related to organ recovery activity. • The cost of organ recovery procedures was obtained to assess the portion of the hospital stay costs related exclusively to organ recovery activities. • The costs of the eight items identified were also specified to identify whether the cost of one or more items differed more widely among the data sources. A Kruskal-Wallis test was used to detect whether at least two of the cost databases provided different results. Then, a Mann-Whitney test was used to compare "hospital cost accounting" versus ENC, "hospital cost accounting" versus weighted ENC and ENC versus weighted ENC.
Results From January 2010 to December 2011, 103 and 101 brain death donors were identified in the local and national databases, respectively. The selection algorithm matched 88 of these donors for inclusion in the analysis, corresponding to a total of 201 organs recovered (Table  1 ). The Kruskal-Wallis test highlighted the differences among the three cost data sources in the cost of hospital stay, the cost of organ recovery and all items of the organ recovery procedure except for imaging (Table  2 ). Thus, a Mann-Whitney test was applied to compare the three cost databases in terms of all costs except imaging costs (Table  2 ).
Nevertheless, the ENC data remain important and are widely used, and the economic information based on the ENC facilitates resource management for healthcare providers without their own cost accounting systems  [20]. Most authors agree that no perfect cost database exists for all research questions. Many of the currently available data sources, including administrative sources, have not been designed for medico-economic assessments  [21]. In the absence of guidelines, the authors recommend choosing the data source according to the study purpose and timing  [21]. The strengths and weakness of the chosen data source should be kept in mind and clearly debated. However, few studies have tested the impact of the data source on cost assessment. The case of colorectal cancer strengthens our results.
All of these elements can explain the differences observed between valuation based on HCL's cost accounting and that based on ENC data. Some limitations of our work should be noted. Although the number of hospital stays was limited to cases of a single public institution, the HCL was chosen based on its several years of regular participation in the ENC, which reflected its engagement in data collection. Extension of this comparative study to other hospitals and the inclusion of a longer observation period would strengthen our results concerning the status of the ENC as a cost referential. Another limitation concerns the costing methods used in the hospital cost accounting system and the ENC. Although bottom-up microcosting is known to be the best method to assess hospital costs, all databases analyzed in our study used a top-down microcosting approach.
Conclusions The choice of cost data sources is a challenge common to all countries wishing to conduct quality medicoeconomic assessments. Due to the growing number of available databases, this choice is increasingly complex. Researchers are waiting for clear recommendations that will allow a choice between different data sources and thus facilitate comparisons between future national and international studies. Moreover, readers, hospital managers and decision makers require a fundamental understanding of the strengths and weaknesses of each database used to allow interpretation of the results in an informed context.
Here, we considered the scope 1 territorial emission as well owing to the city-level data accessibility in China. The territorial emissions describe the current CO 2 emission induced within one country/region's administrative boundary. The territorial emissions are usually used for the emission feature analysis and for the reduction policy-making. Territorial emissions are the foundation of the other emission approaches, in that production-and consumption-based emissions are calculated on the basis of territorial emissions. The territorial CO 2 emissions of cities in this study included both the fossil fuel-related and process-related emissions. The fossil fuel-related emissions were induced by the fossil fuel combustions. Per the definition of territorial direct CO 2 emissions, the cities' CO 2 emissions in this study did not include the part induced by imported/purchased electricity and heat.
CO 2 emission calculation method IPCC (29) provided methods for CO 2 emission accounting based on mass-balance theory. The emissions were estimated as the product of activity data (energy consumption or industrial productions) and the corresponding emission factors. The method is widely used by researchers. On the basis of the IPCC method, the National Development and Reform Commission of China (NDRC) designed an emission account system for Chinese provinces  (35). Furthermore, in the "Global Protocol for Community-Scale Greenhouse Gas Emission Inventories" and "International Local Government GHG Emissions Analysis Protocol", WRI et al. (37)  and Local Governments for Sustainability (ICLEI)  (38)  provided a bottom-up approach for higher-precision city-level emission accounting. The ISO 14064 and 37120 series of standards also provided guidelines for emission accounts at the enterprise level  (39).
43 ) used nighttime light imagery to estimate city CO 2 emissions. These approaches can potentially provide more detailed emissions or full-coverage emissions for Chinese cities. However, they have higher data requirements compared with the IPCC methods, making them difficult to implement. Furthermore, citylevel emissions calculated by these methods are not comparable with the national emission inventories due to differences in methods and scopes. Here, we adopted the IPCC and NDRC methods. The CO 2 emissions from fossil fuel combustion (CE energy ) were calculated in Eq. 1, while the process-related emissions (CE process ) were calculated in Eq. 2. CE energy ¼ ∑ i ∑ j CE ij ¼ ∑ i ∑ j AD ij Â NCV i Â CC i Â O ij ð1Þ In Eq. 1, CE ij refers to the CO 2 emissions by fossil fuel type i and sector j; AD ij , the activity data, means the corresponding fossil fuel consumption.
Activity data collection and source The 46-sectoral fossil fuel consumptions (AD ij ) were collected on the basis of the "energy balance table" and "industrial sectoral energy consumption table" from the city's statistical yearbook. We collected the energy consumption data of the nonindustrial sectors (that is, farming, service sectors, and household energy use) from the energy balance table. The energy balance table illustrates the energy data of one region, such as production, import, input and output transformation, final consumption, and loss (see table  S5 ). Because of the poor data quality at the city level, only 10% (or 17) of the 182 cities have the energy balance table in their statistical yearbooks. The remaining 90% (or 165) of the cities do not have the table. Considering the completeness and consistency of cities' emission inventories, we followed our previous study to scale down the corresponding provincial tables to obtain the city-level energy consumption data of these nonindustrial sectors  (47).
We used the GDP for the farming, construction, and service sectors, assuming that the city has the same farming/construction/service energy intensities as its province, and used the urban/rural population for the urban/rural household energy consumption, assuming that the city has the same per-capita residential energy consumption as its province. The data of GDP and population were collected from cities' and their corresponding provinces' statistical yearbooks. As for the energy consumption data from the 39 industrial sectors, the energy balance table only provides the total amount of the 39 sectors (shown as one industry sector). Here, we collected the energy consumption of the cities' 39 industrial sectors from the industrial sectoral energy consumption table, which were investigated by the cities' statistics office at the enterprise level.
Consistency with the national and provincial inventories in China The city-level emission inventories in this study were constructed by the territorial scope, IPCC calculation method, and framework that were consistently used in China's official emission accounts and our previous studies of the national and provincial level. Such consistency makes the multiscale emission inventories comparable. The consistent, transparent, comparable emission inventories at multiscales provide robust data support for China's emission control and the emission trading scheme. First, the cities' CO 2 emissions in this study had the same scope and framework as China's national and provincial territorial emissions. We included the CO 2 emissions from the fossil fuel combustion and industrial processes only. The emissions from the electricity and heat generation (which are non-fossil fuels) were allocated to the power plants, rather than to their final consumption sectors.
The scope and framework were consistent with the NDRC guidelines for provincial greenhouse gas inventories, which were widely used in China's emission accounts and studies  (25, 35). Second, our cities' CO 2 emissions were calculated using the IPCC equation, in which the emissions were equal to activity data times by emission factors. We used China's most up-to-date emission factors to calculate the cities' emissions  (44). The emission factors were also used in the official emission accounts in China  (45). Moreover, our city-level emission inventories were constructed as 17 fossil fuels and 46 socioeconomic sectors. The 17 fossil fuels were consistent with China's latest energy statistical system, and the 46 socioeconomic sectors corresponded with the System of National Accounts in China.
The samples within the groups will be close together geometrically, while the statistical distance between groups will be farther. The statistical distance is measured by distance metrics, such as Euclidean distance, Manhattan distance, and Minkowski distance. K-means algorithm is one of the cluster algorithms in which the desired number of clusters could be specified in advance before choosing the "best" solution  (64). According to a previous study on comparison of different distance metrics used in K-means algorithm, the Euclidian distance metric's performance is better than others  (65). Therefore, we used K-means algorithm implemented using the Euclidean distance metric to group cities in this study. The SPSS software was used. As this study focused on the city-level industrialization process, we used the cities' manufacturing structure as indicators to conduct the cluster analysis to expose the cities' pillar industries as well as their position in the industrialization process.
Acknowledgments Funding: This work was supported by the  National Key R&D Program of China  ( 2016YFA0602604  and  2016YFA0602500 ), the  Natural Science Foundation of China  ( 71533005 ,  51502103 ,  71503156 ,  41629501 , and  41501605 ), the  Social Science Foundation of China  ( 15CJY058 ), the  UK Economic and Social Research Council  ( ES/L016028/1 ), the  Natural Environment Research Council  ( NE/N00714X/1 ), a  British Academy Grant  ( AF150310 ), the  Philip Leverhulme Prize , N. F. and Z. L. thank the  Resnick Sustainability Institute at Caltech.
Data Collection Patients demographic (sex, age), socioeconomic profile (housing situation, socioeconomic status, employment status, third party payer [TPP], education level), risk factors, medical history including medical treatments, laboratory and imaging data, complications, and rehabilitation therapy (physiotherapy and speech therapy) were collected at baseline and/or during hospitalization period. Current smokers were defined as persons who reported smoking at least 100 cigarettes during their lifetime and who, at the time they participated in the study, reported smoking every day or some days. Former smokers were defined as those who have smoked at least 100 cigarettes in their lifetime but who have quit smoking since a minimum of 28 days. A researcher pharmacist did the data collection.
Billing data were collected using a bottom-up approach. Costs of hospitalization of patients admitted to another hospital before being transferred to a participating hospital were also included. Costs were calculated according to the quantity of resources consumed by each patient from admission till discharge from hospital. The total direct medical cost per patient for each resource item was calculated as follows: total direct cost = ∑unit cost × resource use. The bills for each patient were provided by the hospitals' administration including information related to cost of hospitalization, laboratory, radiology and cardiologyrelated investigations, medication, nursing charges, physicians fees, and rehabilitation services. Costs calculated in Lebanese Pound (LBP) were converted to US$ (exchange rate: US$1 = LBP 1508).
Statistical Analysis Data were entered and analyzed using Statistical Package for the Social Sciences (SPSS), version 20. 0 (IBM Corporation, Armonk, New York). Cost data entry was doubled checked. Two researchers audited 5% randomly selected questionnaires. Data entry showed high reliability (error rate <1%). Data were presented as means ± SDs, except financial data presented also as medians and ranges. In bivariate analyses, Pearson correlation coefficients (or Spearman) were used for 2 continuous quantitative variables, Student test (or Mann-Whitney) for means comparison between 2 groups (for quantitative variables), and chi-square test (or Fisher exact test) for comparing percentages (for nominal, ordinal, and categorical variables) were used. ANOVA (analysis of variance) test (or Kruskal-Wallis) was used to compare between-group differences, followed by Bonferroni post hoc test when a significant difference was obtained.
P ⩽. 05 indicated statistical significance. Bivariate analysis was done for the following dependent variables: length of stay (LOS), intensive care unit (ICU) LOS, and cost. Predictors of total hospital cost (all stroke type and ischemic stroke [IS] only) and LOS were determined through multivariable stepwise linear regressions controlling for potential confounders, after ensuring sample and conditions adequacy. Logistic transformation ln(cost of stroke) and ln(LOS) were performed as their distributions were skewed. Transformed data were normally distributed and were entered in each model as dependent variable. Independent variables with P < 0. 2 in the bivariate analysis were entered into the models. Regression was checked for collinearity (variance inflation factors [VIF] < 10 indicated noncollinearity).
Strengths and Limitations of This Study The first strength of this study was related to the prospective data collection using validated tools used in previous similar research for data collection. It pioneered in assessing cost of stroke predictors through multivariable analysis. In addition, we estimated costs, including physicians' fees, based on actual bills vs using proxy methods, rather than predetermined charges. We conducted a multicenter incidence-base study including a diversified population, thus increasing the generalizability of our results. This study does, however, have limitations. Even though patients came from all governorates, hospitals were limited to Beirut region. In this study, we could not exclude some unintentional bias in patient care, due to its observational nature.
The Universal Distribution of Stock Volatility In this section, we use daily returns of common stocks observed from international exchanges to show how well the universal distribution fits volatility data. Table  1  gives a presentation of our sample. To show the wide presence of the universal distribution, we sampled a price-driven market (NYSE) as well as order-driven ones. Our sample covers more than 20 years of transactions and have been observed from 3 variant continents. For each exchange, we investigate the components of its market index. The composition of these indexes is fixed on 31 March 2014. All data has been furnished by the commercialized platform IDOS. For each individual stock, daily returns are calculated by the difference of logarithmic Closes, and volatility is the absolute value of successive returns.
The Universal Distribution and Volatility Autocorrelation If GARCH simulated data cannot be transformed into universally distributed trading weeks, can our reported algorithmic structure result in volatility autocorrelation? A negative answer to this question would indicate a total irrelevance of the universal distribution to VC's explanation. In this section, we show that algorithmic structure reported in our study could be responsible for volatility autocorrelation, and volatility autocorrelation could be solely another way to present the universal distribution of binary weeks. To do this, we start by simulating 20,000 successive binary weeks. As each week consists of 5 trading days, this will lead to 100,000 daily returns. The binary weeks' generation depends on the following distribution law: Like what we exposed in Table  2 , we distinguish three classes of binary weeks: (I) the most frequent class consisting of 2 possible binary strings, (II) the less frequent one consisting of 10 binary weeks, (III) and the least frequent group with the others.
This result indicates that financial volatilities behave as if, at each week, someone picks out a program randomly, and the program's output is the binary trading week to appear. From a theoretical point of view, our result relieves VC from all micro-structural or behavioral assumptions, in the way that this well referenced stylized fact could be due to the complexity difference between repetitive strings such as "00000" or "11111" and alternative ones such as "01101" or "10010". As VC is usually highlighted by autocorrelation functions, we find it necessary to compare the universal distribution to these econometric measures. Empirical tests show that once transformed into binary sets, GARCH-simulated data do not follow the universal distribution, but universally distributed binary weeks do lead to volatility autocorrelation.
The families were mostly in precarious situation (63. 3%), and most of parents were unemployed (71. 3%). Almost the two third of children and 79. 4% of parents were small consumers of FV (less than 3. 5 servings per day). Several reasons for nonparticipation were reported including time constraints, understanding and mistrust issues. Conclusions: Though using facilitating strategies, we recruited fewer participants than expected. The population finally included was mainly made of precarious families with a low consumption of FV. These results highlight the importance of identifying effective facilitating strategies to improve recruitment in disadvantaged populations. Trial registration: ClinicalTrial. gov no. NCT02461238, on June 3, 2015, retrospectively registered.