dataesr/scientific-paragraphs-categorization huggingface dataset:
5 (blue), 1:1. 4 (black) and 1:3 (magenta). The excess of PGC-1a RID2 in the 1:3 data is seen as a shoulder extending to values close to that of free PGC-1a RID2. In the experiments with ERRc LBD, the concentration of PGC-1a RID2 was overestimated, as can be seen by the faint shoulder of the 1:3 titration data. Thus the 1:1. 4 molar ratio is overestimated and as a consequence the corresponding data approach a limiting value of saturation given by the 1:3 titration data. C and D. ESI mass spectra recorded under non-denaturing conditions in 200 mM ammonium acetate at pH = 7. 4 for (C) ERRa LBD (top) and PGC-1a RID2/ERRa (bottom) and for (D) ERRc LBD (top) and PGC-1a RID2/ERRc complex (bottom). The different charge states of the proteins are indicated above the peaks and depicted for ERRa and ERRc with green and red dots, respectively and for PGC-1a RID2/ERRa and PGC-1a RID2/ERRc with magenta and blue triangles, respectively.
SAXS Provides Evidence for Similar Induced Partial Compaction of PGC-1a on Interaction with ERRa and ERRc As references for PGC-1a/ERR complexes, scattering data were first obtained for the isolated ERRa and ERRc LBDs. Guinier analysis showed a linear behavior for both LBDs with no sign of unspecific aggregation and allowed the determination of R g (Table  2 ). The experimental SAXS data for ERRc is neatly fitted by the scattering profile calculated from the crystal structure (PDB entry 1KV6) using CRYSOL  [29]  (Fig. 5A ) and yields a symmetrical distance distribution function (Fig. 5B ). As expected, the Kratky plot corresponds to a folded globular protein (Fig. S3F ). For ERRa LBD, we have detected a minor fraction of specific multimerization of the ERR dimers (trimers of dimers). These hexamers were found to occur in the crystal packing of ERRa LBD (PDB code: 2PJL) and exist as a minor fraction of the molecules in solution, as estimated by the program OLIGOMER  [30].
The latter gives an estimate of 0. 260. 02 volume fraction of hexamers compared to dimers (PDB code: 3D24), resulting in a reasonable fit with a chi value of 1. 07. The scattering intensities were then recorded for several PGC-1a RID/ERR complexes (Fig. 5A ) and the structural parameters were derived from the data (Fig. 5B  and Table  2 ). The R g and D max values of the PGC-1a RID complexes are significantly smaller than the corresponding value of the isolated PGC-1a RID molecules (Table  2  and Table  S3 ). This suggests that PGC-1a RID undergoes a partial compaction upon binding to its ERR partner and the similarity in the values of the structural parameters indicates a similar type of compaction upon complex formation. The partial induced folding is dependent on the presence of the two interaction motifs, as suggested by the much larger parameters observed for the PGC-1a RID1 L2m complex, which are comparable to the values measured for PGC-1a RID1/PPAR-RXR (Table  2 ).
This can be explained by the fact that PGC-1a is bound close to the hexamerization interface seen in the crystal packing of isolated ERRa LBD (PDB code: 2PJL). The molecular envelopes of the ERRa and ERRc in complex with PGC-1a RID1 exhibit highly similar shape and size with a marked asymmetry as compared to the crystal structure of ERRc LBD dimer (Figs. 5C  and 6C ). From the shape of the molecular envelopes, it is tempting to position the LBDs in the globular region and the PGC-1a RID1 in the asymmetric tail of the envelope. The envelope of PGC-1a RID2/ERRc shows an even more pronounced asymmetry as compared to that of PGC-1a RID1/ERRc (Fig. 5C ). This supports the positioning of PGC-1a in the asymmetric tail of the envelope where the extra portion arises from the 50 additional disordered residues at the C-ter of PGC-1a RID2.
Top panels: raw thermophoresis data recorded at 20uC using the LED at 50% and IR-laser at 80%. Bottom panels: isotherms averaged over three consecutive measurements and fitted according to the law of mass action to yield the apparent K D,1. For the determination of K D,1 , the concentration of unlabeled PGC-1a RID1 was varied between 30 mM and 3 nM, while the concentration of ERR LBD was kept fixed (50 nM). Insets: isotherms for titration series comprising higher unlabeled PGC-1a RID1 concentrations (300 mM to 10 nM) with a fixed ERR LBD concentration (20 nM), showing the two binding events of binding affinities K D,1 and K D,2. doi:10. 1371/journal. pone. 0067810. g004 curves that deviate from the experimental curve. This result underlines the existence, in solution, of a well-defined compact conformation for PGC-1a RID1 bound to ERRc.
Nuclear Magnetic Resonance Experiments 1 H-15 N HSQC spectra were recorded at 600 MHz or 700 MHz and at 10uC on a Bruker DRX600 or Avance III 700 spectrometer equipped with a z-gradient triple-resonance cryoprobe. 15 N-labelled protein samples were at a concentration of 100 mM. The molar excess of non-labelled ERR LBD partner was about 1. 2 per PGC-1a RID molecule. The water signal was suppressed using the WATERGATE sequence  [42]. Data were processed using NMRPipe  [43]  and analyzed with NEASY  [44].
Small Angle X-ray Scattering Synchrotron X-ray solution scattering data were collected at the X33 beamline (DESY, Hamburg) and at the SWING beamline at SOLEIL Synchrotron (Gif-sur-Yvette, France). The data were processed and analyzed with the ATSAS suite of SAXS programs as described in SI Text. The figures of the envelopes and the solution structures were made using Pymol 1. 4. 1, Chimera 1. 5. 3 and the plots were drawn by using Sigmaplot 11.
Analytical Ultracentrifigation Experiments were performed using a Beckman Proteomelab XL-I ultracentrifuge (Beckman Instruments, CA, USA) with an An50Ti 8-hole rotor. Data were fit using the UltraScan software version 9. 9 as described in SI Text. Size-Exclusion Chromatography Coupled to Multi-Angle and Quasi-Elastic Light Scattering SEC-MALS/QELS experiments were performed on a multiangle light scattering detector (miniDAWN TREOS, Wyatt Technologies) coupled in-line with SEC and an interferometric refractometer (Optilab T-rEX, Wyatt Technologies). The analysis of the data was performed using the ASTRA 5. 3. 4 software (Wyatt Technologies).
Figure5. SAXS of ERR and ERR in complex with PGC-1a RID. A. Scattering profiles of (from top to bottom) ERRc LBD, PGC-1a RID1/ERRa, PGC-1a RID1/ERRc, PGC-1a RID2/ERRc and PGC-1a NTD/ERRc. Experimental data are denoted by black dots, the corresponding fits are given as solid lines. Red fit is computed from the crystal structure of ERRc LBD (PDB entry 1KV6). Blue fits are yielded by the best MD based model of PGC-1a RID1/ERRc. Green lines represent the fits to the experimental data by the appropriate ab initio bead models. The profiles are arbitrary displaced in logarithmic scale for better visualization. B. Distance distribution functions of ERRc LBD (red), PGC-1a RID1/ERRa (blue) PGC-1a RID1/ERRc (green), PGC-1a RID2/ ERRc (magenta) and PGC-1a NTD/ERRc (cyan) computed from the X-ray scattering patterns using the program GNOM.
C. Ab initio molecular envelopes from SAXS measurements. The ab initio molecular envelope of ERRc LBD alone is shown in semi-transparent red color together with the crystal structure of ERRc LBD bound to short peptides derived from the coactivator SCR-1 (PDB code 1KV6). The ab initio molecular envelopes of the complexes between ERRc LBD and PGC-1a RID1 (green), PGC-1a RID2 (magenta) and PGC-1a NTD (cyan) are depicted highlighting the location of the additional 50 amino acids at the C-terminal of the PGC-1a RID2 and of the N-terminal extension of PGC-1a NTD. The Ca-trace of ERRc LBD homodimer and of the coactivator peptides are shown in grey and yellow ribbons, respectively. doi:10. 1371/journal. pone. 0067810. g005.
Acknowledgments We thank the  Structural Biology and Genomics platform (IGBMC) ; the IGBMC facilities; V. Chavant and  C. Ling  for technical assistance;  N. Rochel ,  J. Osz  for data on PPAR/RXR. We thank the staff of the  EMBL X33 (DESY, Hamburg, Germany)  and  SOLEIL SWING (Gif-sur-Yvette, France ) beamlines for assistance. We thank  S. Duhr ,  J. Pere  `z,  M. Rocco  for discussion and  B. Klaholz  for critical reading of the manuscript. Some of the NMR experiments were produced using the facilities of the  Centre for Biomolecular Spectroscopy, King's College London , established with a  Capital Award  from the  Wellcome Trust. AUC calculations were.
This work was supported by  CNRS ,  INSERM , the  Association pour la Recherche sur le Cancer (ARC) , the  Karo Bio Research Foundation , the  French Infrastructure for Integrated Structural Biology (FRISBI)   ANR-10-INSB-05-01 , Instruct as part of the  European Strategy Forum on Research Infrastructures (ESFRI) , the  WeNMR  project (European FP7 e- Infrastructure grant , contract no. 261572 , www. wenmr. eu)and the  BMBF  research grant  SYNC-LIFE  (Contract no:  05K10YEA ). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
ACKNOWLEDGMENTS M. Teresa Teixeira  apologize the original authors of the numerous important articles, which were cited collectively in reviews. The author thanks team members for fruitful discussions and  E. Fallet ,  Z. Xu , and  A. Londono  for critical reading of the manuscript. Research in Teixeira's laboratory is supported by the  European Research Council  ( ERC-2010-StG 260906-D-END ), the  Mairie de Paris  ( Programme Emergences ), and  ITMO Cancer.
Objectives and design The Optiflow for prevention of post-extubation hypoxemia after abdominal surgery (OPERA) study is an investigatorinitiated multicenter randomized controlled two-arm trial. The Institutional Review Board of the University Hospital of Clermont-Ferrand (France) approved the trial. By April 25 2013, the study had been approved by all centers by a central ethics committee (Comité de Protection des Personnes Sud-Est VI, Clermont-Ferrand, France) with the registration number IDRCB 2013-A00030-45. The OPERA study is conducted in accordance with the declaration of Helsinki and was registered on June 24 2013 at http://www. clinicaltrials. gov with trial identification number NCT01887015.
Blinding A computer program will generate the coding list and will allocate coding numbers to patients from the specific trial site. It is not feasible to mask the assigned oxygen therapy from the attending anesthesiologists, since they have an ethical responsibility to ensure patient safety until discharge from the post-anesthesia care unit. However, in each participating center, postoperative data will be collected and recorded onto case report forms (CRFs) by local research coordinators blinded to the randomized intervention. A trained research coordinator, blinded to the randomized intervention, will centralize data from all sites and will record them onto the electronic database.
The time-to-event curves should be calculated with the use of the Kaplan-Meier method. If the frequency of missing data is >5%, an additional analysis will be performed using the multiple imputation method (STATA command mi)  [21]. Longitudinal analysis using mixed models will be used to take into account between and within subject variability. All analyses will be conducted with Stata statistical software, version 12 (StataCorp LP, College Station, TX, USA). A two-sided P value of less than 0. 05 will be considered to indicate statistical significance.
Registration Data will be collected and recorded onto CRFs in each center by trained local research coordinators blinded to the randomized intervention. A trained research coordinator will centralize data from all sites and will record them onto the electronic database. The following data will be collected and registered Prerandomization and baseline characteristics: gender, age, height, weight, ideal body weight, physical status (according to the American Society of Anesthesiology status score), cardiac status (hypertension, cardiac failure, ischemic heart disease), respiratory status (history of chronic obstructive pulmonary disease, asthma status, respiratory infection in the last month, long-term home oxygen therapy), smoking status, alcohol status, history of diabetes mellitus, recent denutrition (defined as weight loss >10% in the last six months and/or plasma albumin <30 g/l), history of renal insufficiency, ARISCAT score, preoperative SpO 2 , and blood samples (WBC count, hematocrit, hemoglobin, platelet count, prothrombin time, fibrinogen, urea, creatinine, C reactive protein and albumin).
Patients We performed a retrospective analysis of all consecutive patients with hematologic malignancies who underwent a first PB-HSCT from either MRD or MUD at Saint-Louis Hospital (Paris, France) between January 2000 and December 2010. According to institutional guidelines, MRD was considered as the first donor choice and search for MUD was only undertaken if no suitable MRD was identified. For MUD PB-HSCT, only donor/recipient pairs matched at the allelic level at HLA-A, -B, -C, -DRB1 and -DQB1 loci (10/10 HLA-matched) were included. Data concerning pre-transplant characteristics and transplant outcomes were extracted from our transplantation database. To assess for pre-transplant comorbidities, the hematopoietic cell transplantation-specific comorbidity index (HCT-CI)  15  was retrospectively calculated for patients transplanted between 2006 and 2010 (Online Supplementary Methods).
Some authors have, however, suggested inferior outcome after transplant with very high CD34 + cell dose (>8-10x10 6 cells/kg). 44 We did not observe such a negative impact of very high dose (data not shown). As previously reported,  45  we also observed a significant advantage of high graft CD3 + cell dose with respect to OS, PFS and NRM, even after adjustment for pre-transplant ATG use. Interestingly, patients transplanted with high graft CD34 + or CD3 + cell doses did not experience higher risks for chronic GVHD. We failed to fully validate DRI  14  for survival prediction as only patients with very high DRI (representing only 3% of patients) had significant worse OS and PFS. Our cohort differed from the training and validation cohorts originally reported by Armand et al. First, children were included in our study.
The atmospheric wind field information is a key issue to numerical weather prediction (NWP) and climate studies. The Atmospheric Dynamic Mission-Aeolus is currently developed by the European Space Agency (ESA) to launch a wind sensing Doppler lidar in mid-2015. The high spectral resolution lidar concept is using backscattered laser signals from molecules and particles to provide accurate horizontal wind velocity measurements in the depth of atmosphere. The Aeolus lidar, so-called ALADIN, will operate in UV at 0. 355 µm. The combination of air molecules and UV laser light is intended to provide wind data evenly distributed everywhere in the lower atmosphere (below 30 km altitude). The goal of the ESA's Aeolus mission is to enhance the present meteorological observations system over sparse wind data regions, and more importantly to provide direct wind information in the tropics where no geostrophic wind can be derived from mass fields obtained from passive radiometer satellite.
Atmospheric measurements The lidar LoS measurements were fixed at a 40 • zenithal angle and alternately switched from east to north directions. The measurements were taken during 1 h. The 72 MHz radar was operated continuously to retrieve two horizontal wind components and the vertical velocity  (Delaval et al. , 2000a,b). Radio soundings with GPS tracking system were launched from OHP during every set of measurements. Twelve data sets are chosen for the comparison, as reported in Table  3. The measurements were taken in different atmospheric conditions: clear sky, strong winds, and high aerosol loadings. As a general policy during the field campaign, the results were provided only a few hours after the measurement periods to conduct blind comparisons.
Conclusions A 0. 355 µm wind lidar was deployed at the Haute-Provence Observatory in July 1999, to be compared with other wind lidar, a 72 MHz radar and GPS radio soundings. Twelve data sets were acquired in different atmospheric conditions (clear sky, cloudy, strong wind, high aerosol loading, etc. ). The radio soundings launched at OHP site were used as a reference. The comparison provided evidence of a good agreement between 0. 355 µm lidar and radio sounding wind profiles, both in cross-correlation coefficient (with a CC average value of 0. 78) and RMSE (average value of 3. 67 m s -1 ). The cross-correlation coefficients for all instruments show a better agreement to the east direction than to the north direction, especially in the lower troposphere. This can be explained by the local topography around the Haute-Provence Observatory site.
Acknowledgements. Special thanks to  C. Hirt  ( Université de Genève ),  R. Wilson  ( ST-Radar ) and OHP personnel for hosting the campaign and for the technical support. The data sets for the 10. 6 µm lidar and 0. 532 µm are taken from the database built under ESA support. Edited by:  V. Rizi The publication of this article is financed by  CNRS-INSU.
Patients The adult patients evaluated in this study had been enrolled in a prospective, randomized, open-label, non-inferiority trial conducted from April 2003 to February 2006 in 13 French teaching hospitals (ClinicalTrials. gov Identifier: NCT001190463). The primary objective was to compare overall survival following either empirical or preemptive antifungal therapy in patients treated for hematological malignancies. In the empirical therapy arm, persistent or recurrent fever after day 4 of broad spectrum antibacterials led to the administration of antifungal therapy. In the preemptive therapy arm, the initiation of antifungal therapy was guided by clinical and radiological predefined criteria, and biweekly blood screening with galactomannan antigenemia. Patients received early antifungal therapy according to the trial protocol, either amphotericin B deoxycholate (1 mg/kg/d) or liposomal amphotericin B (3 mg/kg/d) depending on the level of creatinine clearance and concomitant nephrotoxic drugs.
Acknowledgments We thank the investigators of the PREVERT trial: Cecile  Pautas MD ,  Sebastien Maury MD PhD ,  Yosr Hicheri MD ,  Francoise Botterel MD PhD ,  Francoise Foulet MD  ( Henri Mondor Hospital, Creteil );  Anne Vekhoff MD ,  Driss Chaoui MD ,  Muriel Cornet MD ,  Patrice Agnamey MD  ( Hotel Dieu Hospital, Paris );  Hassan Farhat MD ,  Sylvie Castaigne MD ,  Odile Eloy MD  ( Andre Mignot Hospital, Versailles );  Felipe Suarez MD PhD ,  Agnes Buzyn MD PhD ,  Richard Delarue MD ,  Svetlana Challier MD  ( Necker Hospital, Paris );  Nathalie Dhedin MD ,  Ahmad Aljijakli MD ,  Emmanuelle Delabesse PD ,  Annick Datry MD  ( Pitie-Salpetriere Hospital );  Francoise Isnard MD ,  Loic Fouillard MD ,  Jean-Yves Poirot MD, Leila Meliani MD  ( Saint-Antoine Hospital, Paris );  Lionel Ades MD ,  Claire Bouges-Michel MD ,  Michele Deniau MD PhD  ( Avicennes Hospital, Bobigny ); Frederique  Kuhnowski MD, Franc  ¸ois  Dreyfus MD PhD ,  Andre Ṕaugam MD ,  Marie-The  ´rese  Baixench MD  ( Cochin Hospital, Paris );  Roland Leclercq MD PhD ,  Oumady Reman MD ,  Chantal Duhamel MD  ( University Hospital, Caen );  Jean-Henri Bourrhis MD PhD ,  Sami Chehata MD ,  Isabelle Chachati MD  ( Gustave Roussy Institute, Villejuif );  Vincent Foissaud MD ,  Christine Macnab MD  ( Hopital des Armees, Clamart );  HerveTilly MD, Stephane Lepretre MD ,  Christian Gray MD  ( Centre Henri Becquerel, Rouen );  Emmanuel Raffoux MD PhD ,  Claire Lacroix PD (Saint-Louis Hospital, Paris ).
Financial support  French Ministry of Health  ( PHRC 2002 AOR02028 ). The study sponsor had no involvement in design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, or approval of the manuscript. This work was supported in part by the  French Ministry of Health  (research grant  PHRC 2002 AOR02028 ) and  Translational Health Economics Network , a for-profit organization led by  Michae ¨l Schwarzinger. Stephane Bretagne  is consultant for  Gilead Sciences , and has received speaking honoraria from  Pfizer  and  Gilead Sciences  and travel grants from  Astellas ,  Pfizer  and  Schering-Plough. Catherine Cordonnier  has received travel grants and research supports from  Pfizer ,  Gilead , and  Merck Sharp & Dohme-Chibret , and has been a consultant for  Pfizer ,  Schering-Plough ,  Gilead ,  Merck Sharp & Dohme-Chibret ,  Astellas Pharma , and  Zeneus Pharma.
In contrast, there is very poor agreement among these estimates in cases where farmers do not (or cannot) use best management practices and thus achieve low yields (i. e. , Kenya rainfed maize). Likewise, estimates of Yp or Yw based on maximum yield or upper percentiles can be heavily biased if there are atypical years or farms amongst the observations, and there is no way of knowing if this is the case without a more detailed analysis using simulation models. This problem plays a role in the dataset for rainfed wheat in Victoria in which the average maximum yield and the average 95 and 99 percentiles of farmer's yields across three years is well above the average simulated water-limited yield over the same period (Fig. 3 ). If we had taken the maximum yield and 95 and 99 percentiles of farmer's yields while lumping the three years, this difference would be substantially higher as the best year is now used as the benchmark (data not shown).
Global studies Global studies generally use empirical, statistical approaches or generic crop growth models and a grid-based approach using global datasets on climate, soils and sometimes agricultural land use and general crop calendars (Appendix A). The statistical methods take current highest yields within a defined climatic zone (based on e. g. , FAO statistics and  Monfreda et al. , 2008; Licker et al. , 2010; Foley et al. , 2011; Mueller et al. , 2012)  or use a stochastic frontier production function  (Neumann et al. , 2010). They do not verify whether highest yields accurately represent the biophysical Yp or Yw limit as confirmed by either a robust simulation model or field studies. The major limitation of this method is that it does not distinguish between irrigated and rainfed crops; thus, many Yg estimates for a given climatic zone are based on irrigated crop yields-even in regions where the crop in question is grown almost entirely under rainfed conditions.
g. , crop rotation, planting date, cultivar maturity), which limits agronomic relevance (Appendix A). For example, to estimate Yp and Yw of maize for each major maizeproducing country,  Nelson et al. (2010)  assumed that cultivars had the same maturity in all countries. Actual yields used to estimate Yg are generally based on yields reported in FAOSTAT (FAO, 2012a) and the Agro-MAPS project, a collaboration between FAO, IFPRI (International Food Policy Research Institute), SAGE (Centre for Sustainability and the Global Environment) and CIAT (The International Centre for Tropical Agriculture)  (FAO, 2012b). These same actual yield datasets also served as the basis for the crop area distribution maps of  Monfreda et al. (2008)  that utilized data from subnational levels, where available, and otherwise used national level data from FAOSTAT.
Need for a bottom up approach to be locally relevant As demonstrated in Section 3, existing methods lead to different estimates of Yp and Yw, and therefore to differences in conclusions about magnitude and spatial distribution of Yg. We argue for a transparent, robust and reproducible protocol to estimate yield gaps with local to global relevance. The protocol should be applied consistently across locations and crops in a "bottom-up" approach that optimally exploits local knowledge and data. Global datasets on agricultural management (e. g. ,  Waha et al. , 2012)  and actual yields  (Monfreda et al. , 2008)  are generally too coarse for local relevance. To allow for regional and global coverage of yield gap assessments there are basically two methods. First, a representative point-or polygon-based approach estimates Yp, Yw, Ya and Yg for selected points or polygons using observed input data and then scales up to higher geographical units.