dataesr/scientific-paragraphs-categorization huggingface dataset:
Limitations Several limitations have to be acknowledged. First, the monocentric design limits the extrapability of the results. In particular, antibiotics policies largely differ from a hospital to another. However, use of PBLI is in accordance with current guidelines  [7, 8, 20] , even though monotherapy is not recommended in American guidelines  [8]. Second, the case-control methodology is another limitation. The control population was recruited in 2013 whereas the CDI population was recruited over a wider period, from 2007 to 2017. Even though the included population and antibiotic prescriptions were comparable during these two periods, this could have led to a selection bias. Third, adjustment on confounders was limited by the restricted availability of clinical data in this retrospective study (including pre-hospitalization antibiotic treatment) and also by the relatively small number of patients.
Statistical Analysis Since we used different measure, data were standardized (µ = 0 and a σ = 1) so they can have a comparable scale  [26]. Prior to the statistical analyses, data were tested for normality and homogeneity of variance using SPSS software v22. The means were evaluated according to descriptive statistics represented as Mean ± SE. Data analysis was performed using IBM SPSS v22. Analysis of variance (One-way ANOVA) was performed to test significant differences among the samples. The differences among means were estimated with Duncan new multiple range (DMRT) test. Correlation coefficients and their levels of significance were calculated using Pearson correlation. Principal component analysis was carried out using correlation matrix. In addition, a scatter plot was created according to the first three principal components (PC1-PC3).
Principal Components Analysis (PCA) PCA based on correlation coefficients was used to discriminate between variables in the datasets. The aim of this analysis was to determine the main factors to reduce the number of effective parameters to use in classification of the strawberry tree genotypes based on their biochemical parameters. In our study, only a principal component loading of more than |0. 5| was considered as being significant for each factor. Total variance of 93. 19% was explained by seven components (Table  7 ). The first three components consisted of 26 variables, which explained 68. 77% of the total variability observed, which means that these characters had the highest variation between the genotypes and had the highest impact on discrimination of them. The first component accounted for 36.
The IDEA detector response has been simulated by relying on the Delphes 3  [46]  software package, that makes use of the anti-k T algorithm  [47]  as implemented in FastJet 3  [48]  for event reconstruction. Both these last codes are driven through their interface with the MadAnalysis 5 platform  [49, 50] , that we also use to carry on our phenomenological analysis. We begin with a cut-and-count analysis, aiming at unravelling the signal from the overwhelming backgrounds. However, as a consequence of the low statistical significance, we then employ a novel machine learning algorithm based on boosted decision trees in an attempt to improve the significance, using the XGBoost toolkit  [51]. In identifying backgrounds to the signal process, we consider both processes which lead to a true τ τ final state, and those containing fakes.
Acknowledgements We thank  Giacomo Cacciapaglia ,  Gabriele Ferretti ,  Thomas Flacke ,  Michelangelo Mangano  and  Andrea Thamm  for useful discussions. ASC is supported in part by the  National Research Foundation of South Africa (NRF)  and thanks the  University of Lyon 1  and  IP2I  for support during the collaboration visit in Lyon. LM is supported by the  UJ GES 4IR initiative , and thanks  Campus France and SA-CERN  for additional support. The authors would like to acknowledge the  Mainz Institute for Theoretical Physics (MITP) of the Cluster of Excellence PRISMA+  (Project  ID   39083149 ) for its hospitality and support.
Multilevel Models We propose a multilevel modelling approach that allows estimating the impact of market concentration on patient treatment, controlling for other hospital characteristics which may influence process quality. Multilevel models based on patient-level data allow to exploit information about the characteristics of individual patients clustered within each hospital, rather than aggregated characteristics across patients, and to make more robust inferences about the variables of interest because standard errors are more precisely estimated. 35, 36 Following a spatial competition framework, we test if patients treated in hospitals in less concentrated markets have higher propensity to receive interventions considered as good practice using multilevel logistic models. Our quality variables are measured at the patient level and binary (0/1).
Data The analysis relies on patient level data from the French National Hospital Discharge Database (PMSI), a comprehensive database for 2005 and 2012. This contains information on patient's demographics (age, sex, postcode), primary and secondary diagnostics, procedures carried out and DRG for all inpatient treatments delivered in all hospitals in France. Our sample consists of all patients diagnosed with invasive carcinoma of the breast (ICD-10: C50) or ductal carcinoma in situ of the breast (DCIS, ICD-10: D05) who underwent breast cancer surgery in 2005 or 2012. Hospitals with less than five cases of breast cancer surgery were excluded in order to reduce noise in our sample, and avoid over dispersion, since these tend to be outliers. In order to identify hospitals providing breast cancer surgery, their geographic location and characteristics, we used the French Health Facility Statistics (Statistique annuelle des établissements de santé,) complemented with information from the French National Cancer institute (Inca, Institut national du cancer) website.
Data on patient residential income were obtained from the French National Institute of Statistics and Economic Studies (Institut national de la statistique et des études économiques). The distance between patients' residence zip code and the hospital zip code was measured by Odomatrix software using distances by the road. Tables  1  and 2  contain the summary statistics presenting respectively patient and hospital samples. Patient level variables used in the models are presented in Table  S1  (see Supplementary file 1). In 2005, out of the 54 904 women potentially concerned, 16. 3% underwent SLNB. The number of SLNB has tripled in 2012: almost one over two women with breast cancer benefited from this procedure (49%). About 10% of women who underwent mastectomy benefited from an IBR in 2005 against 12% in 2012.
Limitations First, we should note that we use an administrative database which has the advantage of covering all patients and hospitals, but does not give any information on tumor characteristics or staging data which are important determinants of treatment. Therefore, our controls for cancer severity may be inadequate in explaining all of the variations in treatments across patients, but it is unlikely that any omitted variable is correlated with the HHI index. Also, both procedures are likely to be less feasible for patients with complex cancer situations, but our results show that in CLCC and teaching hospitals that usually treat more complex patients, the rates are still higher. Second, the discrete nature of market boundaries assume that hospitals are either in or out of any local market.
This may lead to measurement error in geographic markets, which in turn can bias the estimated effect of competition toward zero. Nevertheless, the results from the sensitivity analysis that tested different indexes based on all cancer surgery and hospital counts are comforting. Finally, we should note that our results cannot be interpreted as the impact of the policy changes (yardstick competition and volume thresholds) on quality. We used cross sectional data for the years before and after the reforms were implemented to have some insight into the change in the relationship between quality and competition, but we do not have a reference (control group) for establishing the effects that are attributable to these reforms. Other exogenous factors are likely to have an impact on hospital practice over this period.
The OpToGait device is validated for research because it has an ICC more significant than 95%  [18] , which allowed us to obtain reliable data for the measurements obtained. During the test, each subject was allowed a 10-minute adaptation time  [3]  on the treadmill. This adaptation time made it possible to reproduce a walking pattern that was as natural as possible. During this adaptation period, the treadmill's speed was set to 4. 0 km / h. Once the elapsed adaptation, the treadmill is stopped, and gait analysis is started on OptoGait software. The OptoGait system already offers the selected test. The test is the following " Treadmill Walking 4km / h or 2. 5mph ». The latter imposes a walking speed of 4 km / h.
Statistical analysis Statistical analyzes were performed using R® software (R Studio, Version 1. 2. 5033 © 2009-2019 RStudio, Inc -250 Northern Ave, Boston, MA 02210) after exporting the data to Excel® as well as to using the software Epi Info TM I CDC. Interference statistics have been carried out. The confidence level is preset such that C = 95% and the significance level is α = 0. 05. The study includes 86 subjects; therefore, it is necessary to perform a normality test to account for the distribution of populations. To compare each patient's demographic data, we, therefore, used the Shapiro test on each of the populations of the sample. The study of the variances is carried out on each of the sample populations thanks to the Chi2 test chosen for the qualitative values and Mann and Whitney (Wilcoxon -Mann-Whitney) for quantitative values.
The study results show that the walking pattern is altered within two years after reconstruction associated with partial meniscectomy but not in the case of a suture associated with ACL reconstruction. The analysis of this study's results, which takes place on subjects with a post-operative delay of 2 years, seems to highlight the fact that meniscectomy would be more harmful in the long term than meniscal suturing. Indeed, one can suppose that the absence of meniscus, which alters the knee's stability, modifies the application of the constraints and could lead to unwanted cartilaginous lesions that would contribute to the alteration of our walking pattern. From another point of view,  Asaeda et al. (2017)   [14] , who studied walking defaults depending on gender concludes that, according to the current database, the differences between the sexes in the occurrence of post-operatory osteoarthritis probably comes most likely from the occurrence of meniscal and ligament injuries instead of the specific difference in gender in the biomechanics of walking.
The patients were assessed by the investigators through the 14-day observation after receiving the investigational product. The clinical, laboratory, and radiological outcomes were recorded and certified by a trained group of doctors. The detailed record included primary safety data (infusional and allergic reactions, secondary infection and life-threatening adverse events) and the primary efficacy data (the level of the cytokines variation, the level of C-reactive protein in plasma and the oxygen saturation). The secondary efficacy outcomes mainly included the total lymphocyte count and subpopulations, the chest CT, the respiratory rate, and the patient symptoms (especially the fever and shortness of breath). In addition, the therapeutic measures (i. e. antiviral medicine and respiratory support) and outcomes were also examined.
Statistical analysis MIMICS 21. 0 (Interactive medical image control system of Materialise, Belgium) was used to evaluate the chest CT data. The analysis of Mass cytometry of the peripheral blood mononuclear cells is described in Supplementary 1. The analysis of the 10 x RNA-seq survey is described in Supplementary 2. Data were analyzed by SPSS software (SPSS 22. 0). Differences between two groups were assessed using unpaired two-tailed t tests. Data involving more than two groups were assessed by analysis of variance (ANOVA). P values <0. 05 indicated statistical significance.
Acknowledgement This work was supported by the  National Key Research and Development Program of China  ( 2016YFA0101000 ,  2018YFE0114200 ),  CAMS Innovation Fund for Medical Sciences  ( 2017-I2M-3-007 ) and the 111 Project ( B18007 ),  National Natural Science Foundation of China  ( 81971324 ,  81672313 ,  81700782 ,  81972523 ,  81771349 ).
Surveys, interviewing, and analysis The analyses presented in this article are based on data from two population-based trend surveys among Austrian citizens. The first survey was conducted in 1998 (n = 1042), the second in 2018 (n = 1010). In 2018, the sampling frame was based on the address register of the Austrian postal service, targeting individuals aged 16 years and older. A multistage random sampling has been used with a response rate of 48%. In 1998, quota samples have been drawn. The sociodemographic characteristics for both samples and, for comparison, those of the general population in the respective years are reported in Table  1. Since there were no statistically significant differences, the samples can be considered representative of the Austrian population in the corresponding years.
A combination of design and poststratification weights was applied for descriptive purposes. The poststratification weights were calculated based on the population level distributions of educational achievements, age, and sex. The reference for each year was the Austrian micro census, a mandatory large-scale population survey tracking the national social structure. Fieldwork was carried out in 1998 by the Institut für Markt-und Sozialanalysen Ges. m. b. H. (IMAS; Linz) and in 2018 by the Institut für empirische Sozialforschung GmbH (IFES; Vienna). Both companies are specialized in market and social research. Informed consent was considered to have been given when individuals accepted to complete the interview. Data collection for both presented surveys was conducted via face-to-face interviews, administrated by trained interviewers.
I do not tend to this opinion' and 'I do not know'. To assess respondents' attitude towards integrating people with schizophrenia into society, they were questioned: 'Do you think that someone with such an illness should live in our midst?)'. Respondents could answer with 'Yes', 'No' or 'I do not know'. Statistical analyses presented in this article were done with IBM SPSS V26. As stated, the data was weighted for representation purposes; with the lowest weight applied being 0. 3 multiplier, and the highest a 3. 6 multiplier. This helped to establish representation concerning the descriptive statistics. There were no indications that the applied weights influenced the multivariate analysis substantially. The analytical procedures done for the study were based on logistic regression modeling to identify significant influences on the public attitudes concerning schizophrenia, while controlling for age, sex and formal education.
Senescence The senescence of the different tree and understorey parts is modelled according to the organ-specific turnover time and to the mortality induced by low temperature, soil moisture deficit or date, respectively (Tables  S4-S5 ). The timing of senescence is fixed for the cohort of coniferous needles. For broadleaf species it is a linear function of the sum of the mean daily shortwave radiation( = 1 24 24 0 SW ↓, in W m -2 ) accumulated from the date of bud burst until DOY = 258 for European beech (Table  S4 ), as fitted on data provided by the French ICP forest network (http://icp-forests. net/, last access: 10 June 2015) from 14 beech stands where meteorological data were recorded  (Lebourgeois, 2008). This accumulated radiation model explains 60 % of the variance of the leaf senescence date across the dataset explored; it compares well with other modelling attempts requiring more parameters and variables  (Delpierre et al.
g. after clear-cut, before every specified spacing, thinning and clear-cutting operation or before regeneration. Immediately after any operation affecting the soil, the mineralization and decomposition rates of the soil carbon fraction affected are enhanced; this enhancement then decreases exponentially with time. Figure  4  shows the dataset taken from  Jolivet (2000)  which is used for calibrating the enhancement factor and its half-life. Table  S1  provides the default values of the parameters. This approach is simple but easier to implement at multiple sites and spatial scales than the more mechanistic  Gottschalk et al. (2010) , which differentiates the ploughing effect according to the carbon pools described in Roth-C and to their linkage with the mineral fraction. We also evaluated the model on soil carbon data collected by  Arrouays and Pelissier (1994).
Testing conservation principles The verification tests consisted of checking the conservation of energy and mass of carbon and water for a long time series of model simulation. The period covered a typical forest stand rotation from the seedling stage to the final clear-cut; thinning and the impact of extreme natural events were included. We selected the Le Bray site to provide the benchmark data for the sensitivity analysis and evaluation of the model. The tree stand demography at this site was monitored from 1987 to 2008, with measurements of sensible heat, CO 2 and H 2 O fluxes and meteorological variables starting in 1996. The period starts in 1984 and ends in 2010. It includes a series of dry years  (1989-1991, 2002-2003, 2005-2007)  and the December 1999 "Klaus" storm that felled or broke 22 % of the trees.
Sensitivity and uncertainty assessments We focused the sensitivity analysis presented below on the Le Bray site that was monitored from 1987 to 2010. It is a well-documented site and the data meet our objective, We used the time series of meteorological data interpolated across the SAFRAN grid from 1970 (planting) to 2010 (final cut) as well as the parameters related to soil characteristics and the forest tree stand (stocking density, soil preparation, understorey removal, thinning and harvest). The data used are available at the ISI-MIP project web site and the Fluxnet database (https://doi. org/10. 18140/FLX/1440163). We analysed the sensitivity at three temporal resolutions: hourly, annual and full rotation (40 years). The parameters' mean values, the meteorological and soil datasets, and initial stand conditions were all taken from the European data cluster database (http://gaia.
Data The time series of daily values of energy, water and carbon dioxide fluxes, i. e. net radiation, R n , latent heat flux, λE, and net CO 2 fluxes, NEE, used in experiments (1) and (3) were obtained from tower stations and taken from the European fluxes database cluster and the Fluxnet Database (URL: http://fluxnet. fluxdata. org, last access: 14 August 2017) for   3 ). The red curve is the normal distribution fitted and the numbers inset is the standard deviation. The variable abbreviations are explained in Table  A1. Douglas fir sites. The variables used are determined from site measurements of SW ↑, SW ↓ and LW ↑, LW ↓, vertical fluctuations of wind speed, U , and fluctuations of CO 2 and water vapour concentrations. The values are further processed for quality checking, filtering and gap filling.
Unless mentioned, they are all level-3-type for R n and level-4-type for λE and NEE. The level-3 data are standard files provided by stations. The level-4 data are filtered, gap-filled using the marginal distribution sampling method  (Papale et al. , 2006; Moffat et al. , 2007)  and aggregated at different time resolutions, from half-hourly to yearly. Other variables commonly used for model testing such as ecosystem GPP, RE or NPP are derived indirectly from primary measurements. They were, therefore, not used in the model evaluation because that would have introduced redundancy with the test on NEE. Table  4  presents the datasets used and their origin. Seven stations were selected because they cover a large part of the geographical range of three important European commercial tree species and also embrace a wide range of tree stand age.
The data used in experiment (2) are a set of 11 longterm records of stand growth that were mostly taken from the Profound project database  (Reyer et al. , 2019). The site characteristics and data sources are detailed in Table  S6. In this evaluation, the model performance was assessed using the annual series of stem diameter at 1. 3 m height (D 130 ) and basal area (BA) of the tree stands. Three common commercial species are represented: maritime pine, European beech and Douglas fir at different locations across Europe and British Columbia. Various tree ages and thinning regimes are used. We compared the annual change of the stem mean diameter, D 130 (cm yr -1 ), and basal area, BA (m 2 ha -1 yr -1 ), that is the cross-sectional area of tree stems at 1. 3 m height over 1 ha. The later can be taken as a proxy for the carbon storage in biomass for which no direct mea-surement method exists.
(2006). (3) European database,  Granier et al. (2000a). (  4 ) European database,  Pilegaard et al. (2011). (  5 ) European database,  Scartazza et al. (2013). (  6 ) European database,  Berbigier et al. (2001). increased with the time span until the (90 d) season length and then dropped at longer time spans (Table  S7 ). The sources of error are multiple and it should be noted that the data themselves are subject to measurement and calculation errors currently assumed to lie within 10 %-15 % of the daily values used. The meteorological data used may also be a source of errors, i. e. at Le Bray where they were interpolated from the main French national meteorological network. Second, the fact that long time series of variables were used for this evaluation exercise makes the model results affected not only by possible errors and approximations in processes directly involved in the energy balance and carbon cycle, but also by possible faults affecting the processes describing the vegetation dynamics, i.
Its performance was similar across sites despite the range of species, age, management and location covered by the dataset. An interesting product of the Go+ model is its evaluation based upon simultaneous values observed and predicted of several variables over decades-long time series. To our knowledge few models have been tested using long-term sets of multiple variables. Figure  14  shows an example for the Collelongo broadleaf forest, Vancouver Island Douglas Fir stand and the Le Bray coniferous forest. In Collelongo and Le Bray, 20-and 25-year-long inventories of tree diameters were available, respectively. A series of soil water stock -or groundwater level -and flux measurements was also available at the three sites. The comparison shows that the longterm trajectory of energy, water and carbon fluxes as well as soil water, tree growth and LAI were captured without significant bias by the model for a period marked by severe droughts  (2002 and 2005), a heatwave (2003) , several  thinnings (1992, 1997, 2005  at Le Bray) and storm damage (at Le Bray 20 % of trees suffered windthrow in December 1999).