dataesr/scientific-paragraphs-categorization huggingface dataset:
Diet and lifestyle questionnaire Dietary data were obtained using different validated dietary history or food-frequency questionnaires (FFQ), tailored to each country in order to capture local dietary habits and provide high compliance. To calibrate dietary data collected by different instruments, a single 24-h computerized dietary recall (24-HDR) was performed in a random sample (8 %) of the EPIC cohort (36,900 individuals)  [36, 37]. The aim of the calibration study was to adjust for systematic differences in reporting of dietary intakes across countries due to different populations and different instruments  [14]. Fish intake has been evaluated in the EPIC cohort, and strong correlations were found between fish intake and plasma phospholipid fatty acids  [33]. We examined total fish consumption (lean and fatty fish), lean fish consumption, and fatty fish consumption.
Age at recruitment, weight, BMI, and season in which the FFQ data were collected were included as covariates in the calibration model. In addition, centre was included in the calibration model as a main effect to ensure correction for between-centre measurement errors. Estimation of regression coefficients was weighted for season and day (weekday/weekend) of the 24-HDR measurements  [16]. The regression intercepts and slopes that were obtained from the calibration study were then applied to the main The same covariates were included in disease models using calibrated values as for the non-calibrated model. The middle quintile was used as reference since they are considered to be more ''normal consumers'' than the non/very low-consumers, who are often different from the rest in many ways (e.
To account for the distance between the quintiles, a variable with the median within each quintile was included. The same variable was then included as squared, to test for linear and quadratic (U-shaped) trend. Restricted cubic spline regression was used to examine non-linearity of the relative risk function for predicted intake data. Log likelihood ratio statistics was used to evaluate whether the fish variables contribute significantly to the model fit, either for the linear or the restricted cubic spline model, and whether the restricted cubic spline model parameters add significantly to the model fit compared to the linear model. To rule out reverse causation the analyses were repeated excluding participants with \2 years of follow up. All analyses were performed using SAS version 9. 2 (SAS Institute, Cary, North Carolina).
Acknowledgments The coordination of EPIC is financially supported by the  European Commission (DG-SANCO)  and the  International Agency for Research on Cancer. The national cohorts are supported by  Danish Cancer Society (Denmark) ;  Ligue Contre le Cancer ,  Institut Gustave Roussy, Mutuelle Generale de l'Education Nationale, Institut National de la Santeet de la Recherche Medicale (INSERM) (France) ;  Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany) ;  Ministry of Health and Social Solidarity ,  Stavros Niarchos Foundation and Hellenic Health Foundation (Greece) ;  Italian Association for Research on Cancer (AIRC) ,  Compagnia di San Paolo , and  National Research Council (Italy) ;  Dutch Ministry of Public Health, Welfare and Sports (VWS) ,  Netherlands Cancer Registry (NKR) ,  LK Research Funds, Dutch Prevention Funds, Dutch ZON (Zorg Onderzoek Nederland) ,  World Cancer Research Fund (WCRF) ,  Statistics Netherlands  (The Netherlands);  ERC-2009-AdG 232997  and  Nordforsk, Nordic Centre of Excellence programme on Food, Nutrition and Health (Norway) ;  Health Research Fund (FIS) ,  Regional Governments of Andalucı ´a, Asturias, Basque Country, Murcia and Navarra, ISCIII RETIC  ( RD06/0020 ) (Spain);  Swedish Cancer Society, Swedish Scientific Council and Regional Government of Ska ˚ne and Va ¨sterbotten (Sweden) ;  Cancer Research UK, Medical Research Council (United Kingdom ).
Pharmacological actions of ghrelin in animals and humans A significant number of studies suggest that ghrelin acts directly on the VTA to increase preference for and motivation to obtain highly palatable food but no data are available on the actions of the other ghrelin-derived peptides (Figure  1 ). In animal models, motivation to obtain rewarding food (like sucrose) is tested in an operant system by measuring lever pressing or nose poking in a progressive ratio paradigm. In such behavioral tests, either intraperitoneal (i. p), intracerebroventricular (i. c. v) or intra-VTA but not intra-NAcc ghrelin administration induces motivation for palatable food by increasing operant lever pressing for sucrose pellets or for 5% sucrose solution in rats  (18) (19) (20). Similarly, reinforcement in satiated rats and motivation for highly palatable food even in foodrestricted animals are increased after intra-VTA chronic ghrelin infusion  (21).
Pharmacological actions of ghrelin in animals and humans An emerging literature demonstrates that ghrelin modulates the action of psychostimulants such as nicotine, amphetamine, cocaine, and heroin but no data is available on the effects of the other ghrelin-derived peptides (Figure  1 ). In animal models, these rewarding properties of drugs of abuse are often measured by locomotor stimulation, NAcc dopamine release and/or CPP. Indeed, systemic ghrelin infusion increases cocaine-induced hyperlocomotion as well as CPP in rats [reviewed in Ref. (108) ]. In addition, i. c. v infusion of ghrelin induces an increase in heroin reinforcement breakpoint on a progressive ratio schedule when animals have to work to obtain a reward  (109). Bilateral micro-injection of ghrelin into the NAcc, where cocaine induces locomotor activation, increases cocaine-induced hyperactivity and these effects are blocked by a GHS-1a antagonist, [d-Lys3]-GHRP-6, in rats  (110).
Purpose: Patients with atrial fibrillation are at increased risk for stroke and thus require anticoagulant prophylaxis with vitamin K antagonists. However, many such patients fail to achieve target coagulation status. The objective of this study was to evaluate time in the therapeutic range and its relationship to clinical outcomes in patients with nonvalvular atrial fibrillation prescribed a vitamin K antagonist in everyday clinical practice in 4 European countries (France, Germany, Italy and the United Kingdom). Methods: Data were extracted from the European electronic primary care database, the Longitudinal Patient Database. Included in the analysis were 6250 adult patients for whom data on monitoring of coagulation time and international normalized ratio were available. The time within the therapeutic range was estimated by using the Rosendaal method.
31 For this reason, we have performed a large retrospective observational cohort (Real Evidence of AntiCoagulation Treatment in Atrial Fibrillation) of patients with nonvalvular AF in 4 European countries that provides an opportunity to collect data on patients treated with VKAs. The primary objective of the present analysis was to assess TTR in everyday clinical practice in these patients. The relationship between TTR and clinical outcomes was also evaluated, but the analysis was purely exploratory.
Data Source Data were extracted from electronic medical records in the Longitudinal Patient Database (LPD) managed by Cegedim Strategic Data (Boulogne Billancourt, France) and owned by Cegedim Group, a company specialised in developing databases and software in the field of health. LPD is a computerized network of general practitioners (GPs) in different European countries who contribute extensive anonymous data on patient consultations and treatment to a centralized database. The database reflects the regular activity of a national sample of independent practitioners, and it enables collection and longitudinal analysis of data taken from patient records related to prescription practices and health care consumption in everyday clinical practice. Currently, 4500 primary care practitioners contribute to the LPD in the 4 countries of the study, providing data from routinely collected records of 45 million patients.
Study Population All adult patients with a diagnosis of nonvalvular AF in the LPD database who had consulted a physician at least once during the 2-year study period and had been prescribed a VKA at least once were identified. From this group, we selected those patients for whom data on monitoring of coagulation time were available and who had at least 2 documented measures of INR separated by o90 days, at least once each year since the beginning of treatment. These patients constituted the analyzable population. For all patients in the analyzable population, the TTR was estimated by using the Rosendaal method. 37 Patients with a TTR 470% were considered to have wellcontrolled VKA treatment. 38 The analyzable population was thus divided into 2 subgroups of well-controlled (TTR 470%) and poorly controlled (TTR r70%) patients.
Data Collection At the index consultation, data were extracted from the electronic medical records on age, sex, body mass index, date of AF diagnosis, comorbidities, history of complications, treatments, and smoking status. Timedependent variables such as age were also documented at the date of treatment initiation and treatment discontinuation for treated patients, at the end of the follow-up period, and at the visit before occurrence of stroke (for patients who experienced strokes). For patients receiving VKA medication, coagulation status (determined by using the INR) was documented. Stroke risk was calculated with the CHA 2 DS 2 -VASc score. 20 This is calculated by assigning 1 point each for the presence of congestive heart failure, hypertension, diabetes mellitus or vascular disorder, for female sex, and for age 65 to 74 years; 2 points were assigned each for age Z75 years and for a history of stroke or transient ischemic attack.
Statistical Analysis The population evaluated in this article corresponded to all patients for whom data were available for least 2 measures of INR separated by o90 days. The analysis of the data is principally descriptive. Data are described by using mean (SD) and median (range) values for continuous variables and frequency counts and percentages for categorical variables. For selected variables, 95% CIs were calculated. Risk of stroke and hemorrhage in patients with suboptimal TTR was evaluated as odds ratios with their 95% CIs. A multivariate logistic regression was performed to adjust for potential confounding factors, namely age, sex, CHA 2 DS 2 -Vasc score, and VKA treatment duration. Patient follow-up data were censored after discontinuation of VKA.
TTR and Stroke and Bleeding Risk The association between the time spent in the therapeutic range and the incidence of stroke and hemorrhage was investigated (Figure  2 ). To optimize the power of the analysis, data were pooled across the 4 participating countries. Overall, 105 ischemic strokes were documented for 14,836 patient-years of VKA prescription. These strokes were more frequent in patients who had a TTR r70% (1. 0 stroke/100 person-years) than in patients whose coagulation status was well controlled (0. 5 stroke/100 personyears). The unadjusted odds ratio for stroke was 1. 60 (95% CI, 1. 08-2. 36; P ¼ 0. 019). After adjustment for age, sex, treatment duration, and CHA 2 DS 2 -Vasc score, the adjusted odds ratio was 1. 38 (95% CI, 0. 93 -2. 06), which was no longer significant (P ¼ 0.
Comparative data on the effectiveness of these VKAs in everyday practice are extremely limited, and it is possible that differences in the VKAs prescribed may contribute to some of the between-country variation in TTR observed. The relation between TTR and clinical outcome was also evaluated, although it should be noted that this analysis was purely exploratory, and the study was neither designed nor powered to address this factor. When the data were pooled across countries, we observed a higher frequency of stroke in these poorly controlled patients (1. 0 stroke/100 person-years) compared with those with a TTR 470% (0. 5 stroke/100 person-years). However, when adjusted on stroke risk factors, the odds ratio did not reach statistical significance. Sample attrition may have provided less power to our analysis, and the observed trend should be evaluated in a larger population.
The absence of a similar trend for hemorrhage is surprising but could be explained by the fact that patients with uncontrolled VKA treatment may be more often below the intended INR therapeutic range than above. This phenomenon has been observed previously  39  and may reflect a tendency of GPs to underdose VKAs because of possible adverse effects related to concerns about VKA-associated hemorrhage. This study has several limitations, principally linked to the use of electronic general practice databases. These databases provide information on a large number of patients gathered in real-life conditions by a representative national sample of GPs. However, because the data are collected in everyday clinical practice, their availability depends on the assiduity of the GP in using the medical software.
Whereas basic data on patient characteristics, diseases, and drug prescriptions are generally collected comprehensively by all GPs, information on paraclinical tests such as INR assays may only be documented in the electronic database by some GPs some of the time. Calculation of the TTR requires regular assay and documentation of the INR, which could explain why only 20% of the patients in our survey were considered analyzable in the 4 countries considered. The low proportion of analyzable patients may have introduced some inclusion bias with respect to all patients with AF who consult their GP. Indeed, in all 4 countries, the analyzable populations tended to be younger and more frequently women, although their stroke risks were similar to those of the entire study sample. Generally speaking, the characteristics of the AF patients evaluated were consistent with previous findings from real-life cohorts in France,  40  Germany,  41  and Italy.
42 ith such databases, it is impossible to ensure that all medical events are exhaustively documented, particularly if they are not the specific objective of the GP consultation. Thus, it is useful to compare the event rates documented in our study with those reported in dedicated patient cohorts in real-life conditions of care. For example, in a cohort of 3302 patients with AF in Italy,  39  the rates of stroke (0. 76 event/100 patient-years) and bleeding events (1. 24 events/100 patient-years) were similar to those reported in the present study. Conversely, in the United Kingdom and France, although the incidence of stroke is consistent with what has been observed previously in real-life studies (0. 9 and 1. 2 events/100 patient-years, respectively), the incidence of hemorrhage in the present study appeared to be lower than expected from these earlier studies (3.
This VOC diversity is already sufficient for PCA to discriminate between the cultivars studied (Fig. 3 ). It can be concluded that the PDMS sorbent is capable of sampling a diverse range of VOCs that are suitable for the assessment of aroma qualities in fruit related foodstuffs. The newly developed method represents an alternative simplified means of accurate and robust high-throughput VOC sampling from very limited volumes of material. Multivariate and univariate analyses suggest substantial compositional VOC differences between the melon cultivars Initially a multivariate statistical method, PCA  (Jolliffe, 1986) , was applied to the PDMS dataset in order to summarise graphically the abundance changes in VOCs between the different melon cultivars and to also assess the technical, analytical, and biological reproducibility.
Solid phase micro extraction-GC-MS analyses of melon VOCs For comparison of the newly devised TD-GC-MS analytical method, the two Israeli and two of the three French cultivars were also subjected to an established SPME-GC-MS method. The extraction and analytical procedures used, as well as the data processing workflow within the MetAlign software package, were as described previously by  Moing et al. (2011)  and  Verhoeven et al. (2011). The netCDF files have been deposited, with associated metadata, into the Metabolomics Repository of Bordeaux MeRy-B (http://services. cbib. u-bordeaux2. fr/MERYB/public/PublicREF. php? REF=M08004).
TD-GC-MS data processing, deconvolution and identification of VOCs All TD-GC-MS data were recorded as Varian sms files, the files were converted to netCDF using the Palisade MASSTransit program (Scientific Instrument Services Inc, NJ, USA). The netCDF files have been deposited, with associated metadata, into the Metabolomics Repository of Bordeaux MeRy-B (http://services. cbib. u-bor-deaux2. fr/MERYB/public/PublicREF. php?REF=M08004). The net-CDF files were then loaded into Matlab R2006 (The MathWorks Inc, Natick, MA, USA) where the mexnc toolbox was utilised for data processing. Alignment of the data was performed by correlation optimised warping (COW)  (Nielsen et al. , 1998). The two parameters of COW, number of segments and slacking size, were optimised by using a simplex optimisation procedure as described by  Skov et al.
, 2006. A detailed description of the COW based alignment and deconvolution procedure is presented in  Xu et al. (2010). The signal derived from the 0. 0001% 1-pentanol internal standard (based upon the peak area in single ion monitoring mode) was stable throughout the experiment for all technical and biological replicate samples regardless of differences between sample matrices or cultivars (Fig. 2 ). For this reason, it was not necessary to normalise to the internal standards response. The deconvoluted mass spectra were exported as NIST compliant text files into the NIST MS search 2. 0 software, putative compound identification was based upon qualitative similarity matching against the NIST 02 mass spectral library. An overall match score of greater than 70% based upon the combined average from forward and backward library searches was used as an initial match criteria.
Chemometric analyses of melon TD-GC-MS VOC data Within MatLab R2006, classical PCA was performed according to the NIPALS algorithm  (Jolliffe, 1986)  as previously described in  Allwood et al. (2006)  and  Biais et al. (2009). Scores plots and loadings plots were generated for the principal components (PCs), as were text files that were exported for all of the PC loadings. The results of PCA were visually interpreted. In addition to PCA, to aid with biological interpretation of the TD-GC-MS data, a heat map was generated for the VOC data averaged for each melon cultivar using Multi Experiment Viewer 4. 41  (Saeed et al. , 2003) , a heat map was also generated following the same method for the SPME-GC-MS data. A non-parametric univariate significance test, the Kruskal-Wallis test, was also performed within MatLab R2006 as described within the MatLab handbook.
fr/MERYB) and spectra of reference compounds acquired under the same conditions (local database), and by standard spiking. For absolute quantification three calibration curves (glucose: 2. 5 to 100 mM, glutamate and glutamine: 0 to 30 mM) were prepared and analysed under the same conditions. The glucose calibration was used for the quantification of all amino-acids other than glutamate and glutamine that were quantified using their own calibration curve. The metabolite concentrations were calculated using AMIX  (version 3. 7. 3, Bruker, Karlsruhe, DE) and Excel (Microsoft, WA, USA) software. A Tukey's test was performed at the 95% confidence limit within XLSTAT software (Addinsoft, FR). The raw 1 H NMR spectral profiles have been deposited, with associated metadata, into the Metabolomics Repository of Bordeaux MeRy-B (http://services.
Background-The fifth Millennium Development Goal (MDG 5) established the goal of a 75% reduction in the maternal mortality ratio (MMR; number of maternal deaths per 100 000 livebirths) between 1990 and 2015. We aimed to measure levels and track trends in maternal mortality, the key causes contributing to maternal death, and timing of maternal death with respect to delivery. Methods-We used robust statistical methods including the Cause of Death Ensemble model (CODEm) to analyse a database of data for 7065 site-years and estimate the number of maternal deaths from all causes in 188 countries between 1990 and 2013. We estimated the number of pregnancy-related deaths caused by HIV on the basis of a systematic review of the relative risk of dying during pregnancy for HIV-positive women compared with HIV-negative women.
Maternal mortality 1990-2013 Data-We used the GBD 2013 cause of death database, which extends from 1980 to 2013, to estimate maternal mortality. Although we report estimates for the MDG period 1990-2013, data for 1980-90 are included in the analysis to improve the robustness of the time trend estimation. Naghavi and colleagues  15  provide substantial detail about the inclusion criteria and data processing of studies across all causes. Briefly, building on previous analyses, we identified data from 180 of 188 GBD countries, including 4877 site-years of vital registration data, 1213 site-years of sibling histories from Demographic and Health Surveys (DHS) and Reproductive Health Surveys (RHS) providing information about the pregnancy-related fraction of reproductive-age deaths, 73 site-years of censuses, 626 siteyears of maternal mortality surveillance, and 267 site-years of verbal autopsy analyses covering women of reproductive age.
Late maternal deaths were not coded in ICD-9 so data are only available for ICD-10 (ie, from 1994). Additionally, because maternal deaths in the age group 10-14 years have been consistently reported in our data sources, we have estimated the number of maternal deaths in this age group but have not included them in the computation of the MMR because no standard estimates of birth rates are available for this group. In vital registration and verbal autopsy data, maternal deaths are often misclassified as deaths attributable to other underlying causes. We reassigned deaths assigned to causes that are unlikely to be underlying causes of death with standardised algorithms. 14 The causes of death that are partly reassigned to maternal causes are shown in the appendix. 15 We reanalysed DHS and RHS microdata for sibling deaths that were related to pregnancy by year using Gakidou-King weights to deal with potential survivor bias.
Causes of maternal death We disaggregated maternal deaths into nine causes: maternal haemorrhage, maternal sepsis and other pregnancy-related infections, hypertensive disorders of pregnancy, obstructed labour, abortion, other direct maternal disorders, indirect maternal disorders, HIV, and late maternal deaths. To estimate the different causes of maternal death, we completed a systematic review (appendix) to identify data to inform which proportion of total maternal deaths is due to each cause. Additionally, we incorporated all vital registration and sample registration data that provided ICD-coded detail for maternal causes (appendix). We identified 61 studies and, after processing, included 943 site-years of vital registration, sample registration, and maternal mortality surveillance data.