dataesr/scientific-paragraphs-categorization huggingface dataset:
No presence of mineral dust was forecast above Évora, as expected from the measurements, except for COSMO-MUSCAT, which predicted fair low values of dust τ 550 nm above the station. BSC-DREAM8b, DREAM8-NMME, and NMMB/BSC-Dust indicated no presence of dust above Barcelona, even though it was located close to the edge according to BSC-DREAM8b. www. atmos-chem-phys. net/16/7043/2016/ Atmos. Chem. Phys. , 16, 7043-7066, 2016  As in the case of Évora, almost negligible values were forecast above the station by COSMO-MUSCAT. This would be in agreement with the previous data except for the possible dust layer observed on 11 July. In the eastern region, the station of Athens was affected by mineral dust during the 3 days according to the DREAM8-NMME model and COSMO-MUSCAT, only on 10 July according to NMMB/BSC-Dust, and on 10 and 11 July according to BSC-DREAM8b.
Due to the relatively coarse horizontal resolution of the model data presented in Fig. 10  compared to the singlesite measurements at the five analyzed stations, it is worth evaluating in more detail the mineral dust mass concentration profiles provided by the models at the specific locations of our interest. To perform this evaluation, mineral dust mass concentration profiles provided by the BSC-DREAM8b, NMMB/BSC-Dust, DREAM8-NMME, and COSMO-MUSCAT models are evaluated against LIRIC results. The main focus is at the Granada station since this site presents a larger number of mineral dust profiles due to the characteristics of the mineral dust event and allows evaluation of the temporal evolution of the dust microphysical properties. Figure  11  shows the dust mass concentration profiles provided by the four models and LIRIC every 3 h from 9 July at 15:00 to 11 July at 18:00.
Therefore, it does not appear to be a systematic delay between the model and LIRIC profiles. However, in the future it will be beneficial for the modeling community to gather a more extended database of continuous lidar measurements with similar characteristics to the one presented  here in order to further explore and improve the possible existence of delays between the model forecast and experimental data. COSMO-MUSCAT shows an increase in the mineral dust load during the analyzed period, with an increasing maximum approximately located between 4 and 5 km. This behavior is totally opposite to the one observed in LIRIC profiles that shows a decrease in the volume concentration with time, as indicated by the negative values of R in Fig. 13a. According to the integrated mass concentration values in Fig.
, as already pointed out in  Binietoglou et al. (2015). Robust conclusions in this respect cannot be drawn from this study and would require wider databases with higher temporal and spatial coverage in order to cover the different aspects of the model calculations, and more dedicated studies. Nonetheless, the comparison presented here provided valuable results since it addresses the points of discrepancy and proves LIRIC's potential as a tool for future model evaluations. Information inferred from the results obtained here could be used for the planning of future validation strategies and campaign management.
In general, the three BSC models showed more similar results compared against COSMO-MUSCAT, based on a different philosophy, but further conclusions regarding the differences between the models cannot be drawn from our study. A more detailed analysis based on a wider and more specific database designed to cover the different aspects of the model calculations would be required. Results presented here are valuable since they prove LIRIC's potential as a tool for model evaluation and provide valuable information for the planning of future validation strategies and campaign management. 6 Data availability BSC-DREAM8b data and additional information about the model are available at http://www. bsc. es/projects/ earthscience/BSC-DREAM/. The Supplement related to this article is available online at doi:10.
Acknowledgements. This work was supported by the  Andalusia Regional Government  through projects  P12-RNM-2409  and  P10-RNM-6299 , by the  Spanish Ministry of Economy and Competitiveness  through projects  TEC2012-34575 ,  TEC2015-63832-P ,  CGL2013-45410-R ,  CGL2011-13580-E/CLI ,  CGL2011-16124-E , and  CGL2013-46736-R ; by the  Spanish Ministry of Science and Innovation  (project  UNPC10-4E-442 ); the  EU  through the  H2020  project  ACTRIS2  (contract number  654109 ); by the  University of Granada  through the contract "Plan Propio. Programa 9. Convocatoria 2013"; and by the  Department of Economy and Knowledge of the Catalan autonomous government  (grant  2014 SGR 583 ). M. J. Granados-Muñoz  was funded under grant  AP2009-0552  from the  Spanish Ministry of Education and Science.
S. N. Pereira  was funded under fellowship  SFRH/BPD/81132/2011  and projects  FCOMP-01-0124-FEDER-029212 (PTDC/GEO-MET/4222/2012  from the  Portuguese Government). S. Basart  and  J. M. Baldasano  acknowledge the  CICYT  project ( CGL2010-19652  and  CGL2013-46736 ) and  Severo Ochoa Programme  ( SEV-2011-00067 ) of the  Spanish Government. BSC-DREAM8b  and  NMMB/BSC -Dust simulations were performed on the Mare Nostrum supercomputer hosted by  Barcelona Supercomputing Center-Centro Nacional de Supercomputación (BSC-CNS ). This paper was realized also as a part of the project  III43007  financed by the  Ministry of Education and Science of the Republic of Serbia  within the framework of integrated and interdisciplinary research for the period 2011-2015. It has also received funding from the  European Union 's  Seventh Framework Programme  for research, technological development, and demonstration under grant agreement no.
289923 -ITaRS. The CIMEL calibration was performed at the  AERONET-EUROPE calibration center , supported by  ACTRIS-2  ( EUH2020  grant agreement no. 654109. The authors express gratitude to the  NOAA Air Resources Laboratory  for the HYSPLIT transport and dispersion model; the ICARE Data and Services Center the MODIS team; and the  ChArMEx  project of the  MISTRALS (Mediterranean Integrated Studies at Regional And Local Scales ; http://www. mistrals-home. org)multidisciplinary research programme. Edited by:  X. Querol.
5 μm. The output from the fiber was collected by either a similar molded lens for insertion loss measurements, or a reflective objective lens for spectroscopic measurements that were performed using a Newport Cornerstone 1/4 m monochromator. Either a PbSe or a MCT detector connected to a lock-in amplifier was used to record the spectra in the range 1. 5-4. 0 μm and 4. 0-8. 5 μm, respectively. Filters were used to prevent higher order grating reflections from reaching the detectors. At wavelengths > 4. 0 μm the data was corrected for the detector responsivity, which was provided by the manufacturer and shown in Fig. 1(b). The total insertion loss of the fiber plus the coupling lenses was measured to be 7. 5 dB and thus the power delivered to the fiber was estimated to be 3. 75 dB lower than the measured average power after the chopper.
Acknowledgments This work has been supported by the  European Commission  through the  Framework Seven  ( FP7)  project  MINERVA: MId-to NEaR infrared spectro-scopy for improVed medical di-Agnostics  ( 317803 ; www. minerva-project. eu);by  Innovation Fund Denmark  ( Light & Food , j. no. 132-2012-3 ); and by the  Australian Research Council Centre of Excellence  grant  CE110001018. Yi Yu  acknowledges the financial support of the  China Scholarship Council  for PhD Scholarship No. 201206110048.
Study Subjects The ANRS PRETHEVIC study is a multicenter prospective cohort of HIV/HCV-coinfected patients, which was set up in 2009. A total of 32 centers in France, comprising 17 departments of infectious disease or internal medicine and 15 hepatology departments, were involved in this study. A common database was created to collect the data. HIV/HCV-coinfected patients were included between 2009 and 2012 if they had experienced an initial episode of decompensation of cirrhosis and/or a diagnosis of hepatocellular carcinoma (HCC) during the 12 months preceding their enrollment. HCV infection was identified by the simultaneous presence of serum HCV antibodies and HCV ribonucleic acid. Cirrhosis was diagnosed by histological examination and or noninvasive tests (transient elastography and/or biological markers of fibrosis).
When the polynuclear count of ascitic fluid was higher than 250 cells/mL or bacteria grew in a culture of ascitic fluid, and no clinical or image data indicative of secondary peritonitis were present, a diagnosis of spontaneous bacterial peritonitis was made. If HCC was suspected during follow-up in light of the results of a previous ultrasound examination or because of elevated afoetoprotein levels (>250 ng/mL), the diagnosis was confirmed by computed tomography or magnetic resonance imaging according to American Association for the Study of Liver Disease criteria (nodule size greater than 1 cm with arterial phase hyperenhancement and portal venous or delayed phase washout at either computed tomography or magnetic resonance imaging). 17 Hepatorenal syndrome was defined as proposed by Arroyo et al.
Access to Liver Transplantation and Causes of Death At each visit, the decision (Yes/No) to refer a patient to an LT unit was recorded. The dates of registration on the waiting list and of transplantation were recorded. Mortality data were obtained through direct patient follow-up; an expert group classified the causes of death into liver-or HIV-related causes of death. Deaths were considered to be liver-related when attributable to hepatic failure or HCC.
The occurrence of a new decompensation episode after the initial episode was also included in models, as a time-dependent variable. Survival since the diagnosis of the initial hepatic decompensation to death or censoring was studied using Kaplan-Meier curves. Censoring was defined at the last visit during the follow-up or the date of LT, whichever came first. Univariate and multivariate Cox models with late entry were used to assess prognostic factors of survival. Late entry was used in models with time zero as the date of first decompensation; subjects entered the dataset at risk (ie, they were considered as exposed to death) only at the date of inclusion. Since the start of followup for some individuals could be different from the specified time origin, correcting for late entry avoided to underestimate mortality rate and produced less biased estimates of hazard ratios.
ACKNOWLEDGMENTS The authors thank sponsored and funded by the  French National Institute for Health and Medical Research -French National Agency for Research on Aids and viral hepatitis (Inserm-ANRS ). The authors also thank all members of the  ANRS HC EP 25 PRETHEVIC Study Group  (listed in the Appendix); especially thank  Laura IORDACHE  (previously Project Manager), Geraldine BOY (previously Project Manager),  Messad Ould-Rabah  (Clinical Research Assistant),  Laetitia JOHNSON  (previously Clinical Research Assistant), and Geraldine ROBERT (previously Clinical Research Assistant),  Anne PERSOZ  (senior data manager); thank  Prof. Daniel Vittecoq ,  Prof. Jean-Francois Delfraissy  and  Prof. Didier Samuel  who supported the project; and thanks also go to all the patients who took part in this study.
Small Angle X-ray Scattering (SAXS) SAXS measurements were made at the SAXS-WAXS beamline of the Australian Synchrotron, Melbourne, Australia. For each SAXS measurement, 10x1 second exposures were measured and averaged together after verifying that there was no evidence of radiation damage (systematic change in the shape of the scattering curves as a function of exposure time). During collection the sample was flowed through a 1. 5 mm quartz capillary at a rate of 4 μl/sec to further control for radiation damage. Measurements were performed on a dilution series of PlyC alone from 3. 3 to 0. 14 mg/ml in a buffer consisting of 1 x phosphate-buffered saline with 10% (v/v) glycerol. Some concentration dependent oligomerisation was observed at protein concentrations above 1 mg/ml as evidenced by increases in Rg and disproportionate increases in I(0) (data not shown).
The SAXS data used in this study were from protein at 0. 9 mg/ml. Dilution of the protein below these concentrations did not result in changes to the shape of the scattering curve and calculated molecular weights at these concentrations were consistent with monomeric protein. The molecular weights of the scattering species were estimated from the total forward scatter of the SAXS measurements that were normalised by comparison to water scatter and with reference to the measured protein concentrations. Partial specific volume and scattering length density were calculated using the program MULCh  [17]. The monomeric state of the protein was inferred by comparison of the theoretical molecular weight of the protein sequence with the calculated molecular weight from the SAXS experiment. A 1. 6 m camera was used with an X-ray energy of 11 KeV giving a Q range from 0.
01 to 0. 5 Å-1. Data was collected on a Pilatus 1M detector (Dektris) and averaging of images, subtraction of blanks and radial integration was performed using the beamline control software ScatterBrain (Australian Synchrotron). Measurements were made at 25°C. Calculation of scattering intensities from molecular models was done using CRYSOL  [18] [19].
All-atom Molecular Dynamic Simulations MD simulations of three systems (PlyC complex, CHAP domain, and GyH domain) were performed using NAMD v2. 8  [20]  in conjunction with the CHARM22 forcefield with CMAP correction  [21]. Chains 1 and A-H from the 3. 30 Å structure of the PlyC complex (PDB accession ID 4F88) were used as a template for all simulations and missing residues were modelled using multiple rounds of Modeller 9v10. PlyC complex: The protein complex was solvated with 35055 TIP3P water molecules in a rhombododecahedral box with a minimum padding of 10 Å and neutralised by the addition of 10 Na + ions. The final system contained 121038 atoms. GyH domain: The PlyCA GyH domain (Chain 1; Residues 1-209) was solvated with 12617 TIP3P water molecules in a cubic box with a padding of 10 Å and neutralised by the addition of 11 Na + ions.
Subsequently, the system was further energy minimized with 1000 CG steps and then applied the ABNR algorithm without positional restraints using a convergence criterion of 10 -5 kcal mol -1 Å -1 RMS energy gradient. The normal modes and the atomic fluctuations were computed with the VIBRAN module of CHARMM. The degree of collectivity of a protein motion can be understood as a fraction of atoms participating to a given displacement  [24]. For a mode vector of length 3N with elements α i , the degree of collectivity κ is defined as: k ¼ 1 N expðÀ X 3N i¼1 a 2 i loga 2 i Þ If the conformational change involves only a few atoms, κ is minimal. On the other hand, if κ = 1 the given motion is maximally collective. Free energy landscape (FEL) analysis. The free energy difference (ΔG α ) of a particular state α with respect to the most populated one, was calculated according to the probability of finding these states as given by: G a ¼ Àk B T ln Pðq a Þ P max ðqÞ where k B is the Boltzmann constant, T the temperature of the simulations, P(q α ) an estimate of the probability density function obtained from bi-dimensional histograms of the RMSD and radius of gyration values (here assigned as the reaction coordinates q i and q j )from each snapshot; P max (q) is the probability of the most visited state.
To investigate the effect of crystal packing on the structure of PlyC, we analysed the crystal contacts in the structure (PDB ID: 4F88). We found 12 hydrogen bonds between the two PlyC structures present in the asymmetric unit and their symmetry partners (S1 Fig). Three of these hydrogen bonds were between the CHAP domain in PlyCA and the PlyCB ring in a symmetry partner. These lattice contacts, together with the cryo temperatures used, may affect the position of the CHAP domain and hence also the overall conformation of PlyC, effectively "locking down" the CHAP domain, offering one explanation for why PlyC appears more compact in the crystal structure than in solution.
Acknowledgments MGSC was financially supported by a  French-Brazilian CAPES/COFECUB  collaboration project. AMB is a National Health and Medical  Research Senior Research Fellow  ( 1022688 ). SM acknowledges fellowship support from the  Australian Research Council  ( FT100100690 ). This work was supported by the  Victorian Life Sciences Computation Initiative (VLSCI) , an initiative of the  Victorian Government, Australia. We thank the  Australian Synchrotron  for beamtime.
Data variables Data variables included numbers enrolled in each quarter, socio-demographic and reproductive characteristics, the use by each woman of the different components of the ANC package, and maternal and perinatal outcomes at delivery. These data were routinely captured in patient file records, Ministry of Health antenatal enrolment records, maternal log booklets and delivery registers, which were extracted into an Excel (Microsoft, Redmond, WA, USA) electronic database maintained by the RMC between August 2013 and April 2014. Data in the electronic database were validated for consistency and checked for missing variables using pre-developed Stata scripts (StataCorp, College Station, TX, USA). A complete ANC package was defined as blood tests for haemoglobin, blood group, screening for syphilis and human immunodeficiency virus (HIV), tetanus toxoid immunisation, and four or more antenatal visits.
Analysis and statistics Data were transferred and analysed using EpiData Analysis software (version 2. 2. 2. 182, EpiData Association, Odense, Denmark), and a descriptive analysis was performed. Maternal and neonatal outcomes in women who received a complete ANC package and those who received an incomplete package were compared using the χ 2 test, with relative risks (RRs) and 95% confidence intervals (CIs). Levels of significance were set at 0. 05. For morbidity statistics, we report disease episodes instead of patient-specific morbidity due to the multiplicity of diagnoses per patient.
Study population The European Prospective Investigation into Cancer and Nutrition (EPIC) study includes 518,408 participants (366,040 women) aged 25-70 years at enrolment  (1992) (1993) (1994) (1995) (1996) (1997) (1998) (1999) (2000). The cohort and data collection have been previously described  [28, 29]. Briefly, study participants were recruited from the general population if they resided in a particular town or province in 23 centres in 10 European countries (Denmark, France, Germany, Greece, Italy, the Netherlands, Norway, Spain, Sweden, and the United Kingdom). Exceptions to this were the French cohort, which includes female members of the health insurance for teachers; components of the Italian (Ragusa and Turin) and Spanish cohorts that included members of local blood donor associations; Utrecht (the Netherlands) and Florence (Italy) cohorts, from where women attending population-based mammographic screening programs were recruited; Oxford (United Kingdom), where half of the cohort included "health conscious" participants from England, Wales, Scotland, and Northern Ireland who did not eat meat; and the cohorts of France, Norway, Utrecht (the Netherlands), and Naples (Italy) which included women only.
Data on diet, lifestyle characteristics, and medical history were collected using baseline questionnaire and interview data. Informed consent was provided by all participants and ethical approval was obtained from the internal review board of the International Agency for Research on Cancer and from local ethics committees in the participating countries. The full list of all local ethics committees is provided in Additional file 1. Exclusions at the study baseline were men; participants reporting prevalent diseases that could influence important confounders, diabetes (n = 8,298), myocardial infarction/heart attack (n = 2,063), angina (n = 3,275), stroke (n = 1,920), or cancer (except non-melanoma skin cancer; n = 18,649); participants who were missing a lifestyle questionnaire (n = 536), vital status (n = 743), or their date of death (n = 216); those who reported having never menstruated (n = 37) or were missing all of the following: age at menarche or menopause (postmenopausal women only), number of full term pregnancies (FTPs), age at first and last FTP, OC use, and duration of breastfeeding (n = 7,331).
Age at menopause was categorized consistently with previous EPIC study reports  [31, 32]  and the largest category (46-50 years) was set as the referent group. Ovulatory years were calculated as the difference between a participant's age at menopause (postmenopausal) or their age at recruitment (premenopausal or perimenopausal/unknown menopause), and their age at menarche, minus the length of time that a woman was pregnant or using OCs. Anthropometric data  [33] , physical activity incorporating occupational and recreational activities  [34] , smoking status/intensity and duration, marital status, and education level at the study baseline also were assessed.
Additional sensitivity analyses were conducted after restricting to women who were postmenopausal at recruitment, or without excluding participants who reported prevalent conditions. In analyses of age at menopause, we conducted further sensitivity analysis of breast cancer mortality without excluding women who had a surgically-induced menopause. The proportional hazards assumption was verified using the Grambsch and Therneau  [40]  method. A two tailed P <0. 05 was considered statistically significant. Analyses were performed using the survival package  [41]  in R (version 3. 0. 2)  [42].
Acknowledgements We would like to thank the EPIC study participants and staff for their valuable contribution to this research. We thank  Yvonne van der Schouw  for her critical review of a draft of this manuscript. The EPIC study is 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 Générale de l'Education Nationale ,  Institut National de la Santé et de la Recherche Médicale (INSERM) (France) ;  German Cancer Aid ,  German Cancer Research Center (DKFZ) ,  Federal Ministry of Education and Research (BMBF) ,  Deutsche Krebshilfe, Deutsches Krebsforschungszentrum and Federal Ministry of Education and Research (Germany) ; the  Hellenic Health Foundation (Greece) ;  Associazione Italiana per la Ricerca sul Cancro-AIRC-Italy 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) ;  PI13/00061  to Granada,  PI13/01162  to  EPIC-Murcia, ISCIII RETIC  ( RD06/0020 ),  Health Research Fund (FIS) ,  Regional Governments of Andalucía, Asturias, Basque Country, Murcia and Navarra , and the  ISCIII RETIC  ( RD06/0020 ) (Spain);  Swedish Cancer Society, Swedish Scientific Council and Regional Government of Skåne and Västerbotten (Sweden) ;  Cancer Research UK  ( 14136  to  EPIC-Norfolk ,  C570/A16491  and  C8221/A19170  to  EPIC-Oxford ),  Medical Research Council  ( 1000143  to  EPIC-Norfolk ,  MR/M012190/1  to  EPIC-Oxford) (United Kingdom ).