dataesr/scientific-paragraphs-categorization huggingface dataset:
Ethics Statement The Institutional Review Board of Jean Verdier University Hospital approved the protocol and the French Research Delegation Office accepted the creation of a dedicated biocollection for the patients included in the protocol. The CNIL (National Informatics and Liberty Commission) also approved the creation of both bio-collection and database. Depending of the site of hospitalization (ward or ICU) and the mental status of the patient, the written consent was obtained directly from the patient at inclusion or from the Person of Confidence designed by the patient (pre-emptively) or by the family relatives. In case, the patient survived, secondary written consent from the patient was systematically seek.
H-NMR Spectroscopy For NMR analysis, samples were thawed at room temperature. A volume of 0. 6 mL of serum was placed into a 5-mm-diameter tube together with 0. 1 mL of D 2 O, which contained a known amount of fumaric acid. The proton spectra were acquired at 500 MHz on a Varian Unity InovaH spectrometer at 25uC. The signal was acquired after a 90u pulse on 32-K data points for a spectral window of 5000 Hz. The relaxation delay was 4 s. The water signal was suppressed by a pre-saturation sequence using low-power irradiation (0. 03 W for 2 s) on the water-signal frequency during the relaxation delay. The Carr-Purcell-Meiboom-Gill (CPMG) sequence is frequently used by others  [9]  to suppress the broad protein signal according to its short T2 relaxation time. Because lipids content in the sera of patients with cirrhosis  [6]  are an important discriminant variable, a single pulse sequence was preferred to preserve the complete lipid profile of the spectra.
The resulting free induction decays obtained with 128 transients were processed by MestrecH software. A Fourier transformation was applied with an exponential window function to produce a 1-Hz broadening line. The spectra were phased and a spline baseline correction was applied with three points at 0. 5, 4. 6, and 9 ppm. The chemical shifts were referenced using the fumaric-acid signal (6. 53 ppm). The spectral region between 0-9 ppm was divided into 9000 spectral regions of 0. 001-ppm width, called buckets, using a personal program with RH. Water, urea (signal damaged by water-saturation transfer), and fumaric-acid regions were excluded (1810 variables excluded). At admission, ICU patients were not fasted whereas patients from the CLF group were. Moreover, some ICU patients were perfused with hydroxyl-ethyl starch, a volume expender, before admission to the ICU.
Multivariate Analyses A principal component analysis (PCA) was first performed to detect any group separation based on NMR-signal variability. This method also enabled detection and exclusion of any outliers, defined as observations located outside the 95% confidence region of the model. Orthogonal projection to latent-structure (OPLS) analysis was run to identify differences between patients in the CLF and ACLF groups. Compared to the classical projection of latent-structure analysis, this method allowed improved interpretation of the spectroscopic variations between the groups, by removing information that had no impact on the differences. PCA and OPLS analyses were performed using Simca-P12 (Umetrics, Umea) and in-house MatlabH (Mathworks, Natick, MA) code based on the Trygg and Wold method  [19].
Patients' Characteristics and PCA Analysis A total of 130 patients fulfilled the inclusion criteria: there were 35 patients in the ACLF group and 95 patients in the CLF group. A flow chart of the patients' characteristics is presented in Figure  1. The data obtained by bucketing the spectra of 130 sera were first analyzed by PCA (not shown). From this analysis, seven outliers were identified: two in the CLF group and five in the ACLF group. The two outliers in the CLF group exhibited high levels of ethanol: 1. 11 and 3. 66 ppm. Of the five outliers in the ACLF group, three concerned hemorrhagic shock previously transfused. All these outliers were excluded from the OPLS analysis. Demographic, clinical, and biological features of all patients included in the study are displayed in Table  1 , and specific characteristics of the ICU patients included in the OPLS analysis are displayed in Table  2.
X-ray and Optical In the X-rays, we obtained the Swift X-ray Telescope (XRT) data over the energy range, 0. 3-10 keV, from an ongoing monitoring programme of Fermi/LAT monitored sources. The Swift/XRT data reduction method is discussed in  Williamson et al. (2014). At optical wavelengths, we obtained B, V, R and I-band data from an ongoing monitoring programme of blazars at several observatories. The optical facilities include the Catalina Real-time Transient Survey  (Drake et al. 2009)   The optical data analysis procedures except for the Catalina data, were performed as discussed in  Jorstad et al. (2010).
Radio The 230 GHz (1. 3 mm) light curve was obtained at the Submillimeter Array (SMA). The source is included in an ongoing monitoring programme at the SMA to determine the fluxes of compact extragalactic radio sources that can be used as calibrators at mm wavelengths  (Gurwell et al. 2007). Observations of the source are calibrated against known standards, typically solar system objects (Titan, Uranus, Neptune or Callisto). Data from this programme are updated regularly and are available at the SMA website 4. The 37 GHz single-dish fluxes were obtained from the observations made with the 13. 7-m telescope at Aalto University Metsähovi Radio Observatory, Finland. The flux density scale is based on observations of the calibrator source DR 21, with NGC 7027, 3C 84 and 3C 274 used as secondary calibrators.
A detailed description of the data reduction process and analysis is given in  Teräsranta et al. (1998). To investigate the kinematics of the inner regions of the jet, we used 47 VLBA observations at 43 GHz from the Boston University blazar monitoring programme 5. The data reduction and calibration was performed as discussed in  Jorstad et al. (2005). We then modelled the complex visibility data with multiple components using the task modelfit in the Difmap program  (Shepherd 1997) , with each represented by a simple twodimensional Gaussian brightness distribution. This method can identify components of the source structure that are closer than the resolution of the synthesized beam but are resolved by the longest baselines. Our model consisted of circular Gaussian components to parametrize the data in order to reduce the number of free parameters.
The fit was considered to be good if the residual map rms-noise was low and the reduced χ 2 statistic was ∼ 1. A fit to an additional component was deemed necessary only if it significantly improved the quality of the fit. No starting model was used during the modelfit procedure. The uncertainties of the parameters of individual components were estimated with the Difwrap package (Lovell 2000), following the approach discussed in  Rastorgueva et al. (2011). The model fitting parameters are given in Table  1.
The total intensity images of the blazar at selected epochs with jet component locations marked according to modelling are shown in Figure  3 , while Figure  4  shows the separation of the components from the core as a function of time. The kinematics of the moving components were determined by fitting a polynomial using the method of least squares. We determined the order of the polynomial based on a F -test which tells whether addition of model parameters to fit the data is warranted by the level of misfit improvement. And thus, the first three moving components were fit with a first order polynomial while the best-fitting polynomial for component C4 was of second order based on a F -test with probability 1 × 10 -5. The fit yields the proper motion (µ) and the ejection epoch which is determined by back-extrapolating the fitted linear trajectory of every component (see Table  3 ).
SUMMARY AND CONCLUSION We have investigated the mm-γ-ray connection in the blazar 1156+295 by analysing a multiwavelength dataset (briefly described in Section 2) and 43 GHz VLBA observations over the period of 2007-2012. Our findings are as follows: -From the 43 GHz VLBI maps, we identify 4 moving and 1 stationary component (0. 4 ± 0. 04 mas from the radio core) with apparent speeds in the range 3-12c shortly after ejection and viewing angles between 1 • and 7 •. -In the γ rays, one major flare and three sub-flares were noticed in 2010 and towards the end of 2011. The cross-correlation analysis to study the connection between the γ-ray activity and the radio core, yielded a time lag of ∼ 2 months in the source frame with the γ rays lagging the radio. However, given the caveat mentioned in Section 3.
ACKNOWLEDGEMENTS We thank the anonymous referee for his/her positive and helpful comments. We are grateful for the support from the  Academy of Finland  to our AGN monitoring project (numbers  212656 ,  210338 ,  121148 , and others). VR acknowledges the support from the  Finnish Graduate School in Astronomy and Space Physics. The VLBA is an instrument of the  National Radio Astronomy Observatory. The  National Radio Astronomy Observatory  is a facility of the National Science Foundation operated under cooperative agreement by  Associated Universities, Inc. The PRISM camera at Lowell Observatory was developed by  K. Janes  et al. at  Boston University (BU)  and  Lowell Observatory , with funding from the  National Science Foundation ,  BU , and  Lowell Observatory. The Liverpool Telescope is operated on the island of  La Palma  by  Liverpool John Moores University in the Spanish Observatorio del Roque de los Muchachos of the Instituto de Astrofisica de Canarias , with financial support from the  UK Science and Technology Facilities Council.
The research by the  BU group  was partly supported by  NNX08AV65G ,  NNX11AQ03G , and  NNX12AO90G. M. A. was supported in part by  NASA   Fermi GI  grants  NNX11AO13G  and  NNX13AP18G. This paper is partly based on observations carried out at the  German-Spanish Calar Alto Observatory , which is jointly operated by the  MPIA  and the  IAA-CSIC. Acquisition of the MAPCAT data is supported by  MINECO (Spain)  grant and  AYA2010-14844 , and by  CEIC (Andalucía)  grant  P09-FQM-4784. St. Petersburg University team was supported by  Russian RFBR  grant  12-02-00452  and  St. Petersburg University  research grants 6. 0. 163. 2010,6. 38. 71. 2012. The Swift effort at  PSU  is supported by  NASA  contract  NAS5-00136. This research has made use of  NASA 's Astrophysics Data System.
With FITC-RGD peptides grafted onto HA25, the amide component increased in comparison with HA25 -FITC-RGD ads (Fig. 4B ). The N1s peak of HA25 FITC-RGD ads was well defined considering that the peptide had an additional nitrogen percentage. For the RGD and FITC-RGD data set, although the existence of the amide peak (at 402 eV) cannot conclude conjugation, we still observed that the amide peak increased in the grafted sample (Fig. 4A, 4B ), which is also complementary supporting evidence of conjugation as we have discussed in C1s fits. By combining C1s and N1s fitting results, we conclude that the RGD grafting process was effective to make the RGD surface grafted, rather than adsorbed, onto the HA25 polymer.
Crystallographic data for 4 Intensity data were collected at 100 K on a Gemini Agilent diffractometer using Cu Kα radiation source and equipped with an Oxford Cryosystems Cryostream Cooler Device. The structure was solved by SUPERFLIP,  [4]  and refined by full-matrix least-squares procedures on F, using the programs of the PC version of CRYSTALS. [5] Atomic scattering factors were taken from the International Tables for X-Ray Crystallography. [6] All non-hydrogen atoms were refined anisotropically. Hydrogen atoms were refined using a riding model. Absorption corrections were introduced using the MULTISCAN program. [7] ystal data for   O15 0. 0303(9) 0. 0344(9) 0. 0240(9) -0. 0036(7) 0. 0013(7) 0. 0015 (7) Δ/σ max 0. 0004 Refinement on F w = w′ × [1 -(ΔF obs / 6 × ΔF est )²]² w′ = [P 0 T 0 ′(x) + 1 T 1 ′(x) +.
Excess dye terminators were removed with a BigDye XTerminator® Purification Kit (Life Technologies, CA, USA). The samples were loaded on an ABI Prism 3100 analyzer (Applied Biosystems) according to the manufacturers' instructions. The sequences were analysed using Vector NTI advance (TM) software (version 11, Invitrogen, Cergy Pontoise, France) to identify specific single nucleotide polymorphism (SNP) combinations.
Acknowledgements The authors thank the patients and the staff of the  Hôpital Principal de Dakar  and  Ndeye Fatou Diop  and  Maurice Gomis  from the  Hôpital Principal de Dakar  for technical support. This study was supported by the  Schéma directeur Paludisme, Etat Major des Armées Françaises  (grant  LR 607a ) and by the  Ministère des Affaires Etrangères.
Numerical simulations are performed with ANSYS Fluent s CFD commercial software. The mesh inside electrical heater was made fine and structured. The mesh (89,000 cells) is structured and refined near the wall to satisfy the turbulence model condition y þ ¼ 1 ð Þ. This mesh becomes coarser away from the wall in order to optimize the computational time. The convergence criteria were based on the residuals resulting from the integration of the conservation equations over finite control volumes. During the iterative calculation process, these residuals were constantly monitored and carefully scrutinized. For all simulations performed in this study, converged solutions were achieved with residuals as low as 10 À 5 (or less) for all the governing equations.
It is important to clarify the impact of alcohol intake on prognosis in women with breast cancer because alcohol is a well-established risk factor for breast cancer  (25) , and public health advice to women is to limit alcohol consumption. It is not clear, however, whether it is safe to continue with moderate alcohol consumption after breast cancer diagnosis. Thus, the key question relates to the influence of postdiagnosis alcohol consumption on outcome. The aim of this study was to carry out a systematic review and meta-analysis of published data to provide more precise estimates of mortality risk after breast cancer diagnosis and, if possible, to identify the causes of heterogeneity across published studies. In addition, we evaluated the association between alcohol consumption and prognosis in large case cohorts: the Studies of Epidemiology and Risk Factors in Cancer Heredity breast cancer cohort (SEARCH), the European Prospective Investigation into Cancer and Nutrition (EPIC), and nine studies from the Breast Cancer Association Consortium (BCAC).
SEARCH, EPIC, and BCAC breast cancer cohorts SEARCH cohort-SEARCH is an ongoing, population-based study of breast cancer in the region covered by the Eastern Cancer Registration and Information Centre (ECRIC). A detailed description of the study has previously been published  (11). The study was set up to investigate genetic susceptibility to breast cancer. All patients diagnosed with invasive breast cancer before the age of 55 years since 1991 and still alive at the start of the study in 1996 (prevalent cases; median age, 48 years) together with all those diagnosed under 70 years of age between 1996 and the present (incident cases; median age, 54 years), are eligible to take part. The study was approved by the Cambridgeshire Research Ethics Committee. The present analysis is based on data from 8,446 participants (98% of whom were White British) with a diagnosis of invasive breast cancer.
A self-administered questionnaire was used to collect information on lifestyle factors, including height, weight, smoking history, and current (postdiagnosis) alcohol intake. Reported weekly alcohol intake was converted into standard units. The local area Index of Multiple Deprivation was used as a proxy for socioeconomic status (SES; ref. 28 ). Age at diagnosis, vital status, and data on tumor characteristics were obtained through the ECRIC. The registry actively follows up individuals at 3 and 5 years after diagnosis and every 5 years afterwards with continuous passive follow-up through notifications of death received from the Office for National Statistics. EPIC cohort-Data from incident breast cancer cases from EPIC were included in the analysis. EPIC is an ongoing multicenter prospective cohort study designed to investigate the associations between diet, lifestyle, genetic and environmental factors, and various types of cancer.
Vital status was ascertained through linkages with regional and national mortality registries and data collected by active follow-up (Germany and Greece). For the present study, the latest dates of complete followup for cancer incidence and vital status in the EPIC centers ranged from 2002 to 2006. BCAC studies-BCAC comprises multiple studies investigating inherited susceptibility to breast cancer susceptibility  (30). Many of these have detailed pathologic data on the breast cancer cases linked to follow-up data. All BCAC studies that had collected data on alcohol intake, and had data available on survival time were eligible for inclusion in this analysis. In all the studies, the prediagnosis alcohol intake was estimated using a self-reported questionnaire that was filled in after diagnosis.
Statistical analysis Cox regression was used to assess the association of alcohol consumption and survival for the SEARCH, EPIC, and BCAC breast cancer case cohorts. Because cases were enrolled at variable times after diagnosis, analyses were conducted allowing for left-truncated data. Time to failure was considered from the date of diagnosis. Time at risk began on the date of receipt of the completed questionnaire and ended at the date of death from any cause or, if death did not occur, at date of last follow-up. Follow-up of all breast cancer cases was censored at 15 years. EPIC data were stratified by country and BCAC data were stratified by study. We modeled alcohol consumption both as a categorical and as an ordinal variable in four categories: nondrinkers, up to 7, >7 to 14, and >14 U per week.
A similar procedure was used for a trend test for ordinal and continuous variables. All tests were two sided. The assumption of proportional hazards was assessed using standard loglog plots and tested using Schoenfeld residuals. ER status and stage were time dependent (in addition to grade in SEARCH) and were therefore treated as time-dependent variables in an extended Cox model. Intercooled Stata version 12 (STATA statistical software, release 12; Stata Corporation) was used for all analyses.
Published data meta-analysis We identified 22 studies that investigated the relationship between alcohol consumption and survival (Table  1 ). Half of these studies had been conducted in the United States. Sample size varied considerably (range: 125-9,325 cases), with 12 studies including more than 1,000 cases. Median follow-up ranged from 3 to 13 years. Ten studies reported findings based on alcohol intake before breast cancer diagnosis and 12 studies measured alcohol intake after diagnosis. Seventeen studies reported the relationship between alcohol consumption and OS. Most studies reported no significant association between pre-or postdiagnosis alcohol consumption and OS (1-10). However, some studies found a protective effect of alcohol  (11) (12) (13) (14) (15) (16)  and one showed an adverse effect  (28).
SEARCH, EPIC, and BCAC Postdiagnosis alcohol intake data were available for 8,446 patients with breast cancer in SEARCH with 55,684 person-years of follow-up (median 6 years). Age at diagnosis varied from 23 to 73 years (mean 54 years). There were 1,506 deaths of which 1,213 were due to breast cancer. The annual mortality rate was 2 per 100 and the 5-, 10-, and 15-year breast cancer survival rates were 88% (95% CI, 87%-89%), 75% (95% CI, 74%-77%), and 43% (95% CI, 36%-50%), respectively. Supplementary Table  S2  shows the characteristics of the SEARCH study by post-diagnosis alcohol consumption. Alcohol intake tended to be higher for women with low-grade and early-stage tumors and varied by ER status, BMI, SES, smoking, and menopausal status. Prediagnosis alcohol intake data were available for 10,561 patients in EPIC with 69,383 person-years of follow-up (median 7 years).
Age at diagnosis varied from 18 to 95 years (mean 54 years). There were 1,911 deaths of which 860 were due to breast cancer. The 5-, 10-, and 15-year breast cancer survival rates were 88% (95% CI, 87%-88%), 77% (95% CI, 76%-78%), and 66% (95% CI, 64%-67%), respectively. Supplementary Table  S5  shows the characteristics of the BCAC data by study. The associations between self-reported alcohol intake and prognosis in women with ERpositive breast cancer are shown in Table  2. There was a weak inverse association between postdiagnosis alcohol intake and all-cause mortality for women with ER-positive tumors (SEARCH data) with an apparent dose-response effect in univariate analysis. However, the association was substantially attenuated after adjustment for stage, grade, BMI, smoking status, SES, and menopausal status.
There was also an inverse association between postdiagnosis alcohol intake and BCSS in ER-positive disease, but the effect was weaker than for OS and no association remained in the multivariate analysis. There was a very similar pattern for the inverse association of prediagnosis alcohol intake and both OS and BCSS in women with ER-positive breast cancer (EPIC and BCAC datasets). The associations between self-reported alcohol intake and prognosis in women with ERnegative breast cancer are shown in Table  3. In unadjusted analyses, there was a strong inverse relationship in SEARCH between post-diagnosis alcohol intake and all-cause mortality (P = 0. 0003) with an apparent dose-response effect (per-category HR, 0. 74; 95% CI, 0. 65-0. 87; P trend = 0. 0001). A slightly weaker inverse association was observed for breast cancer-specific mortality (P = 0.
006) with a similar dose-response effect (percategory HR, 0. 78; 95% CI, 0. 67-0. 91; P trend = 0. 001). This association was attenuated slightly in a multivariable adjusted model (per-category HR, 0. 81; 95% CI, 0. 69-0. 96; P trend = 0. 013). However, no association between prediagnosis alcohol intake and mortality was observed in EPIC or the BCAC dataset. Supplementary Table  S6  shows the association of alcohol consumption and mortality without stratification by ER status. We also carried out analyses stratified by BMI and menopausal status but no significant differences were found (data not shown). We also restricted the analysis stage I and II disease for SEARCH and again there was no significant difference in the findings (Supplementary Table  S7 ).
Discussion We have evaluated the association between alcohol consumption and mortality after a diagnosis of breast cancer using a combination of meta-analysis of previously published studies and an analysis of individual participant data from 11 breast cancer case cohorts (SEARCH, EPIC, and nine BCAC studies). The exposure of primary interest was postdiagnosis alcohol consumption, but many studies only obtained data on prediagnosis alcohol consumption. It is likely that pre-and postdiagnosis consumption are correlated, and therefore prediagnosis intake may be a proxy for postdiagnosis intake  (14). However, prediagnosis alcohol consumption could be associated with prognosis through complex mechanisms in addition to any correlation with postdiagnosis consumption. For example, alcohol intake is a well-established risk factor for breast cancer, but risk is higher for hormone receptor-positive disease than for hormone receptor-negative disease  (25, (32) (33) (34).