dataesr/scientific-paragraphs-categorization huggingface dataset:
, 2011; Meybeck and Moatar, 2012; Thomas et al. , 2016]. Elements with down behavior have been described as source limited, since delivery to the stream network is determined by their abundance or rate of production, rather than the transport capacity of the catchment  [Basu et al. , 2011]. Because C-Q analysis requires repeated measurements across the hydrograph, understanding the prevalence and meaning of C-Q modalities depends on gathering and analyzing appropriate data for multiple elements from diverse catchments. While high-frequency data are still relatively rare and tend to be short Water Resources Research 10. 1002/2016WR019635 term  [Schwientek et al. , 2013; Strohmeier et al. , 2013; Bieroza and Heathwaite, 2015] , there are many large databases of low-frequency water chemistry and discharge from government agencies and research institutes  [Alewell et al.
Because this analysis required at least 150 C-Q couples for each of the sampling periods, it was only possible for 14 to 38 stations depending on the parameter, and was somewhat biased toward larger catchments since there were no eligible catchments smaller than 1,500 km 2. We quantified relative change of slopes (b 50 ) and intercepts (C Q50 ) to assess whether the C-Q modality and background concentration changed between the two periods. For analyses and data visualization, we used R version 3. 3. 0, Matlab 7. 8, ArcGis 10. 2. 2, and Python for ArcGis.
Discussion The causes and prevalence of C-Q curves for different elements across catchments remains unknown, despite decades of research  [Hem, 1948; Edwards, 1973; Thompson et al. , 2011]. To address this issue, we analyzed chemistry and discharge data collected over 40 years from 293 water-monitoring stations draining half of France. Our analysis of segmented C-Q slopes  [Meybeck and Moatar, 2012]  revealed several surprising patterns, including flat (chemostatic) behavior of TSS for low flows in 66% of catchments, up behavior of NO 2 3 during low flows for 71% of catchments, and up behavior of DOC in 80% of catchments during high flows independent of catchment size. While dilution effects dominated C-Q behavior for nonreactive elements, we found evidence for biological modulation of nitrogen, phosphorus, and TSS, particularly during low flows.
Conclusion We calculated segmented C-Q slopes and intercepts for four decades of water quality data from 293 catchments in France. We found systematic differences in C-Q behavior for carbon, nutrients, major ions, and suspended sediment. Only two or three of the nine possible C-Q modalities were manifest for each parameter, and these modalities were largely stable through time, suggesting that C-Q dynamics were constrained by intrinsic and extrinsic elemental properties (e. g. , solubility, reactivity, and source dynamics). These properties set a basic C-Q template for each parameter, which was secondarily influenced by biological activity during low flows, and the interaction between hydrology and catchment characteristics at high flows. The size of our data set and the segmented C-Q analysis revealed several patterns that challenge current views on C-Q dynamics including predominantly flat (chemostatic) behavior of TSS for the lower half the hydrograph, biological mediation of NO 2 3 concentration increasing with catchment size, and positive C-Q behavior of DOC independent of catchment size.
Study population This is a cross-sectional analysis of a subset of participants from the Coyoacán Cohort, an observational study conducted in Mexico City. Specific details for the design of this study have been published elsewhere  10. Briefly, to be eligible for recruitment, participants had to meet the following criteria: age 70 years or older, established residence in Coyoacán, not being institutionalized, and being registered at the Food Support, Medical Care and Free Drugs Program, which is a government program that includes 95% of the community dwelling elderly (≥ 70 years of age) in Mexico City. The first phase was composed of a face-to-face interview during which a wide range of information was collected, including self-reported data regarding sociodemographic characteristics, general health-related information, medication use, oral health (self-reported and clinically evaluated), and mental health.
Asthma control, quality of life questionnaire and Spirometry With the exception of lung function parameters (maximum expiratory volume per second (FEV 1 ) and Tiffeneau coefficient), which were measured by the investigator, all data were collected using a patient's questionnaire based on a 1-year recall. This questionnaire was developed from both asthma control and quality of life questionnaires used to date in the evaluation of asthmatic disease  [18, 19]. It was made up of 4 parts: the first part established the initial clinical and socio-demographic characteristics of the patient. The second part focused on the levels of asthma control according to GINA criteria, use of oral corticoids and use of peak flow monitoring. The third section dealt with limitations of activities and with health-related QoL, and the fourth part with the way the patients used the self-management program, their overall satisfaction with regard to the action plan and their general opinion on the use of such educative programs for asthmatics.
Statistics Statistical analysis was performed with SAS 8 software. Quantitative parameters were expressed by numbers of patients, mean ± standard deviation, and qualitative parameters by number and percentage of patients. Univariate analysis of QoL scores comparing self-management and no self-management groups was performed by Kruskal-Wallis test. Statistical tests were performed using a statistical significance threshold of 0. 05. Changes in quantitative parameters from D0 until the end of the study were assessed with matched t-tests.
Global asthma evolution for the study population For the 112 patients who completed the questionnaire, clinical and paraclinical data recorded both at the time of the original action plan (start of study) and at the date of completing the questionnaire (end of study) were compared. GINA severity score was significantly improved between the start and end of study (Figure  1 ). GINA severity scores were improved in 39. 13% of patients and remained stable for 40. 5%. FEV 1 was significantly improved at the end of study compared to the beginning (79. 3 ± 22. 9 vs. 74. 3 ± 22. 9). Among the 85 patients for whom FEV 1 values were available at the beginning and end of the study, a significant improvement was observed (p=0. 037). At the end of the study compared with D0, the percentage of patients with FEV 1 <60% was lower while the percentage of patients with a FEV 1 >80% was higher (Table  3 ).
Patient Population and Data Collection Patients who received VA-ECMO due to cardiac or respiratory failure after isolated CABG at 12 centers from September 2005 to June 2016 were included in the present analysis. Preoperative and intraoperative characteristics were retrospectively collected in a dedicated datasheet. Follow-up information was collected by direct contact with the patients or their general practitioners, or by using medical records or national registry data. This study was approved by the Institutional Review Board (IRB) of the participating centers or a regional ethics committee, and it was not financially supported. Informed consent was collected in institutions where it was required by the internal IRB, otherwise it was waived. Adult patients who required VA-ECMO treatment for acute cardiac or respiratory failure occurring within seven days after isolated CABG procedure were included in this analysis.
Statistical Analysis 4 Statistical analysis was performed using the SPSS v. 23. 0 statistical software (IBM Corporation, 1 New Orchard Road Armonk, New York, USA) and the freely available software Meta-Analyst (http://www. cebm. brown. edu/openmeta/, accessed on  October 20, 2016). No attempt to replace missing values was made. Continuous variables are reported as the median and mean with standard deviation (SD). Nominal variables are reported as counts and percentages. In view of the uneven proportion of treated patients in the participating centers, in-hospital mortality was pooled using a random-effects method and adjusted for the effect of proportion of VA-ECMO of each participating institution in meta-regression. Fisher exact test, Chi-square test, Mann-Whitney test and Kruskal-Wallis test were used for univariate analysis.
First, the retrospective nature is a major limitation of this analysis. However, data on large number of baseline, perioperative and ECMO-related parameters were retrieved from these patients and allowed a reliable assessment of the baseline risk and outcomes of these patients. Second, this study collected data from twelve centers of cardiac surgery with possibly significant differences in the revascularization strategy and perioperative treatment. Furthermore, differences in the availability of, criteria for, and experience in mechanical circulatory support in cardiac surgery patients might exist between the centers. However, pooled analysis suggested that in-hospital mortality was not heterogeneously distributed between the.
[4]. Moreover, turbine efficiency provided by manufacturers, eq. (  2 ), merges the turbine isentropic efficiency, eq. (  3 ), heat transfers and the mechanical efficiency. The maps are measured for high turbine inlet temperature, which prevents from dissociating the two effects (thermal transfers, mechanical losses) only using the manufacturer data. Some models of thermal transfers in a turbocharger have been developed  [5]  as methods to extrapolate the manufacturer maps  [6]. A mechanical efficiency model will then enable to use and model the manufacturer turbine efficiency in the whole TC operating range. This article will present experimental techniques and results to characterize the friction losses in a turbocharger. This will generate a database and an influence parameter list to develop a friction losses model.
Oil grade Three different oil grades were tested during the campaign. The grades were chosen to ensure different behaviors. The SAE grades of the oils are: • 0W30 • 10W40 • 10W60 NB: the different used oils are commercial ones. Hence datasheet from manufacturers are available. We based our dynamic viscosity curve calculation on manufacturer data (oil viscosity for T=40°C and T = 100°C). As Deligant  [15] , we determine the viscosity using the Vogel equation (  11 ).
Firstly, the results on oil grade tests show that the classical available oil temperatures cannot be used to estimate the viscosity of the oil in the bearings. Frequently used models are based on proportional relation between friction losses and viscosity. The latter should be calculated from the in-bearing oil temperature, which has then to be modeled. An example of such a temperature calculation is presented in  [11]. Secondly, the oil inlet temperature is an important influence factor, along with TC speed and should be taken into account. At last, the oil inlet pressure has a role only from a turbocharger speed threshold. This one has to be determined. This study has enabled to define reliable measurement procedures and to generate a database. This will be the basis data to develop and validate a turbocharger friction model designed for integration in 0D/1D engine model.
Powder characterization The chemical composition of the kaolin powder was determined by spectroscopic techniques, as Xray fluorescence for metals and by atomic absorption for alkaline earth metals. Phase identification was performed by XRD analysis (Philips X'Pert X-ray) diffractometer) with Cu Ka radiation (l= 1. 5406 A°), and the crystalline phases were identified by reference to the International Center for Diffraction Data cards. Thermogravimetric analysis (TG) and differential thermal analysis (DTA) were carried out from ambient temperature to 1300°C at a rate of 10°C min -1 under air, using a setaram SETSYS Evolution 1750. The particle size distributions of kaolin were determined by the Dynamic Laser Scattering (DLS) technique using water as dispersing medium (Mastersizer 2000, Malvern Instruments).
However, as discussed later in the chapter, this new process is often opaque and assumes a level of impartiality that is not always accurate. It also creates information asymmetry and lack of transparency between actors using these algorithms and the intended beneficiaries whose data is being used. In the following sub-sections, we assess the nature, function and impact of the use of social good algorithms in three key areas: criminal behavior dynamics and predictive policing; socio-economic deprivation and financial inclusion; and public health.
Tizzoni et al. [82]  and Wesolowski et al. [95]  have compared traditional mobility surveys with the information provided by mobile phone data (Call Detail Records or CDRs), specifically to model the spread of diseases. The findings of these works recommend the use of mobile phone data, by themselves or in combination with traditional sources, in particular in low-income economies where the availability of surveys is highly limited. Another important area of opportunity within public health is mental health. Mental health problems are recognized to be a major public health issue  6. However, the traditional model of episodic care is suboptimal to prevent mental health outcomes and improve chronic disease outcomes. In order to assess human behavior in the context of mental wellbeing, the standard clinical practice relies on periodic self-reports that suffer from subjectivity and memory biases, and are likely influenced by the current mood state.
Moreover, Twitter data has recently been used to identify people with a high likelihood of falling into depression before the onset of the clinical symptoms  [20]. It has also been shown that, despite the algorithmic advancements in anonymizing data, it is feasible to infer identities from anonymized human behavioral data, particularly when combined with information derived from additional sources. For example, Zang et al. [98]  have reported that if home and work addresses were available for some users, up to 35% of users of the mobile network could be de-identified just using the two most visited towers, likely to be related to their home and work location. More recently, de Montjoye et al. [22, 23]  have demonstrated how unique mobility and shopping behaviors are for each individual. Specifically, they have shown that four spatio-temporal points are enough to uniquely identify 95% of people in a mobile phone database of 1.
Data from multiple sources, including smartphones, questionnaires, experience sampling probes, etc. has been collected and used to create a multi-layered view of the lives of the study participants. In particular, social interactions (e. g. call and SMS communications), mobility routines and spending patterns, etc. have been captured. One of the MTL goals is to devise new ways of sharing personal data by means of Personal Data Store (PDS) technologies, in order to promote greater civic engagement. An example of an application enabled by PDS technologies is the sharing of best practices among families with young children. How do other families spend their money? How much do they get out and socialize? Once the individual gives permission, MyDataStore  [89] , the PDS system used by MTL participants, allows such personal data to be collected, anonymized, and shared with other young families safely and automatically.
Indeed, a smaller proportion of SCD observed in the ICD group was offset by an increase in the rate of nonarrhythmic deaths among these patients. These results are consistent with findings from the IRIS study  [21]. The United States' experience with the WCD was derived from a national database and included 8453 patients with ejection fraction < 0. 35 early after acute MI  [10]. One point four percent of patients were correctly treated by WCD, whose 75% in the first month of use. The median time to first WCD therapy was 9 d. The resuscitation Barraud J et al. Wearable cardioverter defibrillator review survival rate was of 91%. The VEST Prevention of Early Sudden Death Trial and VEST Registry (VEST) is a randomized simple blind trial currently enrolling patients with LVEF < 0. 35 following AMI.
Literature search Since we anticipated a paucity of reported definitions and/or classification of ulcers in SSc, we included other related autoimmune diseases (systemic lupus erythematosus (SLE), rheumatoid arthritis (RA) and vasculitis) in our search. Diabetic ulcers are also of interest because there has been much effort regarding defining skin ulcers in diabetes. Pressure ulcers were excluded because their pathogenesis is varied and often unrelated to the pathogenesis of SSc-related ulcers. We conducted a systematic literature review (SLR) examining the clinical studies reporting a definition of skin ulcers in SSc, in autoimmune diseases (SLE, RA, vasculitis), and in diabetes mellitus. Data Sources: Database searches were carried out by two investigators (DEF, YS) and a library information specialist(B.
M. ). PubMed, Web of Science, and Cochrane database were searched for articles published from inception to January 1st, 2016. The search was limited to the English language. Search terms: Keywords and MeSH terms for the following concepts were used in the search: skin ulcer, nonhealing wound, or chronic wound; scleroderma, systemic sclerosis, systemic lupus erythematosus, rheumatoid arthritis, vasculitis, diabetes; classification or definition; and clinical trials.
Systematic literature review driven definitions Database searches yielded 3464 publications. After removing duplicates (n= 251), 3213 titles and abstracts were screened, resulting in 446 articles with data referring to skin ulcers as an outcome for full text review (Fig. 1 ). Sixty-three abstracts were case reports, 1373 abstracts reported diseases not of interest, 38 studies were in those less than 18 years of age, and 675 abstracts did not report skin ulcer as an outcome. Ultimately, skin ulcer definitions were extracted from 66 studies, after consensus by the 2 investigators in each group. Definitions were extracted from 34 SSc studies, 28 diabetes and 4 rheumatoid arthritis articles. Supplementary Table  1  shows the extracted definitions (supplementary).
Indeed, the di usion of innovations is a more complicated process than the di usion of information or viruses and its models generally include attitude or opinion dynamics such as the ones described below (e. g. ). Figure illustrates three distinct ideal-typical opinion dynamics for continuous opinions that received much attention in the modeling literature. Some other patterns will be discussed elsewhere in this paper. To generate the figure we used the illustrative models that we formally define in the remainder of this section (Equations -).
A promising development for empirically testing micro-assumptions of social influence models in field settings is the increasing availability of longitudinal datasets that combine the dynamics of social networks as well as of opinions in complete social networks, like in school classes  (Stark & Flache )  or workplaces (Ellwardt et al. ). Rather than drawing random samples in big populations, these studies assess complete social networks in relatively small populations. Recent developments in statistical methodologies have made it possible to utilize such data. Stochastic actor oriented models developed by Snijders and others (Snijders ; Snijders & Steglich ; Steglich et al. ) combine agent-based modeling of such longitudinal data with statistical estimation and testing of the mechanisms assumed by the modeler.
g. structures of social networks). As we argued above, micro level evidence that can narrow down the set of models suitable to match opinion distributions observed in surveys can be obtained from experimental studies or studies of the co-evolution of networks and opinions in longitudinal data. Further empirical sources can be used to calibrate assumptions models make about meso-level structures. For example, structural features of social networks in a population at large can be assessed from increasingly available data sets of online networks (Eagle et al. ) or global network indicators measured in surveys (DiPrete et al. ).
Acknowledgements AF, MM and TF acknowledge that their contribution to this work has benefited from stimulating discussions with the members of the " Norms and Networks" research group  at the  Department of Sociology / ICS, University of Groningen. JL acknowledges funding from the  German Research Foundation (DFG LO / -"Opinion Dynamics and Collective Decision"). SH and GD acknowledge funding from the  Auvergne Region  (Emergent themes , project  Associatione ). All authors wish to thank  Flaminio Squazzoni  for his great patience in waiting for us to finish the manuscript.
AEL Treated with ICT (n = 122) In the group of 122 patients receiving front-line ICT treatment, response data were available for 119 patients. The most frequently used (n = 81; 66%) induction regimen was Daunorubicin (45 or 60 mg/m 2 × 3 days) with Cytarabine (100 mg/m 2 bid × 7 days). Similar 3 + 7 regimens using Idarubicin 12 mg/m 2 or Mitoxantrone 12 mg/m 2 for three days, instead of Daunorubicin, were used in 25 (20%) and eight (7%) patients, respectively. Information regarding induction regimen was not available in eight (7%) patients. ORR according to the ELN criteria was 72%; CR in 79 patients was 66%; PR in seven patients was 6%; SD in 16 patients was 13%; PPD in 17 patients was 14% (see Table  2 ). Data on HI was not assessed in this subgroup of patients, as this response form is considered irrelevant for AML-patients treated with ICT.
Patient Population Patient data were collected retrospectively and pooled from registries or patient files from 28 different Institutions representing eight different countries (Austria, France, Germany, Italy, Netherlands, Portugal, Spain, and USA). All subjects gave their informed consent for inclusion before they participated in the study. The study was conducted in accordance with the Declaration of Helsinki, and the protocol was approved by the Medical Ethics Committee of each individual centre. AEL diagnosis by WHO 2008 criteria was the only entry criterion and this was confirmed by local diagnostic laboratories. MRC cytogenetic risk stratification was applied to all patients. Patients were included in the HMA group if they had received HMA at any stage of their treatment, whether first, or subsequent, lines.
Acknowledgments. This work has been supported by the  Agence Nationale pour la Recherche (ANR)  under grant ' MOTIMO ' ( ANR-11-MONU-009-01 ), by the  Engineering and Physical Sciences Research Council (EPSRC)  under grant ref. EP/M006883/1 , and by the  National Science Foundation (NSF)  under grant  RNMS 11-07444  ( KI-Net ). P. D. is on leave from  CNRS ,  Institut de Mathématiques , Toulouse,  France. He  acknowledges support from the  Royal Society  and the  Wolfson foundation through a Royal Society Wolfson Research Merit Award. H. Y. wishes to acknowledge the hospitality of the  Department of Mathematics, Imperial College London , where this research was conducted. P. D. and H. Y. wish to thank  F. Plouraboué  ( IMFT, Toulouse, France ) for enlighting discussions. Data statement. No new data has been generated in the course of this research.
Participants Participants were recruited among community-dwelling women in the Auvergne-Rhône-Alpes region in France. Only study-specific recruitment tools approved by the European Community were used. These recruitment tools included a volunteer database from the General Clinical Research Center (GCRC), Eurofins-Optimed (Gi eres, France); regional newspaper advertisements with specific press inserts; radio spots and broadcast messages; posters; mailings; and the GCRC recruitment website. Two hundred eighty-eight volunteers expressed interest in participating. The screening procedure occurred within 3 weeks before the intervention; 140 participants met the study design criteria and were enrolled between January 7 and April 22, 2015.