dataesr/scientific-paragraphs-categorization huggingface dataset:
Marianas TIRF microscopy After transfection, PC12-DKD cells were visualized with a TIRF microscope (Marianas Everest; Intelligent Imaging Innovations, Inc. ) fitted with a 100× oil immersion objective (NA 1. 46; ZEI SS). Images were acquired using an electron-multiplying charge-coupled device camera (QuantEM 512SC; Photometrics) and SlideBook software (version 5. 5. 2 × 64; Intelligent Imaging Innovations, Inc. ) for the acquisition of images. Cells were bathed in Buffer A (145 mM NaCl, 5 mM KCl, 1. 2 mM Na 2 HPO 4 , 10 mM d-glucose, and 20 mM Hepes, pH 7. 4) before the addition of 2 mM Ba 2+ , and a time-lapse simultaneous acquisition was captured at 20 Hz for the indicated period. Green mEos2 Alexa Fluor 488 fluorescence was detected using the 488-nm laser line. mEos2 was photoconverted through illumination with a 405nm laser, and converted molecules were observed with excitation at 561 nm.
Acknowledgments We thank  Jean-Baptiste Sibarita  ( University of Bordeaux ) for providing the single-molecule analysis software PalmTracer and  Kirill Alexandrov  and  Zakir Tnimov  ( Institute for Molecular Bioscience, The University of Queensland ) for providing the GFP nanobody. We thank  Daniel Mathews  ( Queensland Brain Institute, The University of Queensland ) for assistance in superresolution analysis. We also thank members of the  Meunier laboratory ,  Nela Durisic  and  Rowan Tweedale , for critical reading of this article. We thank  Jenny Martin  for her contribution in making Fig. 7 and helpful suggestions. We thank  Shuzo Sugita  for providing DKD-PC12 cells and  Sally Martin  for correlative light and electron microscopy imaging. We acknowledge the contribution of  Pranesh Padmanabhan  ( Queensland Brain Institute ) for his input on statistical analysis.
Imaging in Bordeaux was supported by the  Bordeaux Imaging Center , part of the  national Infrastructure France-BioImaging  ( ANR ANR-10-INBS-04 ). We also acknowledge the facilities of the  Australian Microscopy and Microanalysis Research Facility  and the assistance of  Garry Morgan  with Technai 12 grid mapping at the  Centre for Microscopy and Microanalysis, University of Queensland. This work was supported by a  National Health and Medical Research Council (NHM RC)  project grant ( GNT1044014  to  F. A. Meunier  and  B. M. Collins ) and  Australian Research Council LIEF Grants  ( LE0882864  and  LE130100078  to  F. A. Meunier ). F. A. Meunier is an  National Health and Medical Research Council Senior Research Fellow  ( GNT1060075 ), and  B. M. Collins  is an  National Health and Medical Research Council Career Development Fellow  ( GNT1061574 ).
6) model with sequences available in GenBank, and representing all HBV genotypes. [25, 26]  Phylogenetic analyses was carried out by Bayesian inference using the Bayesian Markov chain Monte Carlo (MCMC) statistical framework implemented in MrBayes version 3. 1. 2 software  [27, 28]  under the model 'general reversible model of evolution' with a gamma distribution of rates across sites, run for 8 x 10 6 generations, with a burn-in of 25%. Calculations of Effective Sample Size (ESS) were examined with the TRACER v1. 5 program (http://evolve. zoo. ox. ac. uk/) and all the ESS were greater than 1200. The tree datasets were computed using strict consensus and majority rules algorithms to evaluate the posterior probabilities of branching pattern (BPP). The final tree was visualized in the FigTree version 1.
4. 2 (program 2006-2014, Andrew Rambaut Institute of Evolutionary Biology, University of Edinburgh. http://tree. bio. ed. ac. uk/). Using the Mutation Reporter Tool software (http://hvdr. bioinf. wits. ac. za/mrt/), HBV resistance-associated mutations (RAMs) in the pol gene represented by V173L, L180M, A181V, A194T, S202G, M204V/I and N236T were assessed  [29] [30, 31]. In addition, we looked for VEMs outside (at amino-acid (aa) position 120) and within the HBsAg immunodominant 'a' determinant (at aa positions 126, 129, 130, 144 and 145)  [32]. Finally, A1762T/G1764A mutations in BCP region and T1858C, G1862C andG1896A mutations in PC region were analyzed  [33].
Distribution of HBV genotypes HBV genotype was successfully analyzed in 28 samples (on 114 specimens with HBV DNA levels 50 IU/mL, 26%) from patients with HBeAg-positive CHB (n = 3), negative-HBeAg CHB (n = 6), inactive HBV carriage state (n = 9) and reactivated OBI (n = 10). PCR failed from 22 (19. 3%) samples whereas 64 (56. 1%) had insufficient volumes of plasma. All sequences described herein are available in the GenBank (accession numbers KM983561 to KM983588). As shown in Fig  5 , we revealed the presence of two HBV genotypes, including 19 sequences (67. 9%) clustering with genotype HBV/A and nine (32. 1%) with genotype HBV/E. For genotype A, most (n = 16) strains clustered with sub-genotype QS-A3, and were closely related to isolates from Cameroon and Gabon. Two additional strains were classified as sub-genotype A4 and one clustered with sub-genotype A2.
One HBV/A strain (1/8, 12. 5%) had BCP mutations whereas   2 ). The classical PC G1896A mutation was found in two (2/8, 14%) and four (4/6, 66. 7%) HBV/A and HBV/E isolates, respectively (P =. 31). All 14 sequences are available in the GenBank (accession numbers KT724763 to KT724776).
16 This rule however directly follows from the oscillator picture of the free scalar Hilbert space in the canonical quantization, where each factor ∂ l φ corresponds to adding an oscillator of angular momentum l; see  [1] , Eq. (3. 13). The above discussion was for general d. Let us now describe how the Gram matrix can be used to enumerate the null states in 4d, which correspond to evanescent operators when we move slightly away from 4d. This will be also relevant for the subsequent discussion of the WF fixed point. Using our Mathematica code we evaluated all matrix elements for scalar parity-even operators up to ∆ = 23 in d = 4. This will be enough for the applications we have in mind. The code becomes slower for longer operators but with additional optimization one could go higher up in dimension if needed.
Search strategy English-language databases with online search tools were searched for to offer maximum coverage of the relevant literature: Medline (via the Ovid interface 1946 to July 2015); EMBASE (via the Ovid interface 1980 to July 2015); The Centre for Review and Dissemination Library (incorporating the DARE, NHS EED, and NHS HTA databases); The Cochrane Library (via the Wiley Online Library) and Health Technology Assessment Database (1989 to July 2015). We supplemented our data by searching relevant published reports from: National epidemiological agencies, Google search for grey literature and hand searched the reference lists of the included studies. The general search headings identified were: Clostridium difficile, economic, costs, cost analysis, health care costs, length of stay, hospitalization.
Data extraction Two reviewers (SP, SZ) independently selected the included articles and extracted data. After combining their results, any discrepancies were solved by discussion with HN and MK. The primary outcomes were CDI-related costs (total costs of those with CDI and other comorbidities) and CDI-attributable costs (total costs of CDI management only, after controlling for the confounders). For studies with control groups (e. g. matched patients without CDI), the CDI-attributable cost extracted was either the cost provided by the articles or calculated by reviewers using the CDI-related cost minus the treatment cost of control groups. The secondary outcome was resource utilization associated with CDI, i. e. CDI-related length of stay (LOS) in hospital and CDI-attributable LOS. The study characteristics of each article were extracted.
These included basic publication information, study design, statistical methods, economic data reporting characteristics and population information. When multiple cost data were presented in a study, we included only one cost estimate for each population subgroup as per the priority below: All costs/charges data were inflated to 2015 US$ equivalent prices adjusted for the Consumer Price Index. If the price year was not reported, it was assumed to be the last year of the data collection period. In cases where charges were reported without cost-to-charge given, costs were estimated using a cost-to-charge ratio of 0. 60, which is commonly used value in US health economic studies  [22].
At each iteration, we calculated the weighted average of all included studies. Finally, we reported the mean and 90 % CI from the resulting distribution of the 100,000 weighted average of CDI. This approach facilitated the combination of cost data and eliminated the limitation of combining non-normally distributed data. Monte Carlo simulations were conducted using the Monte Carlo simulation software @RISK, version 7. 0 (Palisade Corp). We estimated the national financial impact of CDI on the US healthcare system, by determining the potential boundaries. The higher boundary was the total number of CDI cases in the US in 2011 extracted from Lessa et al  [24] , while the lower boundary was the result from a meta-analysis to estimate the total burden of CDI cases in the US  [25]  (For detailed results see Additional file 1).
Study characteristics The characteristics of the 42 included studies  are summarized in Table  1. Cost data collection periods ranged from 1997 to 2012. Most studies (n = 27) used national level databases, with 17 used National Independent Sample (NIS) database and the remaining 10 studies extracted data from various national databases. Fifteen studies were conducted at state level, of which 6 studies only collected data in single hospital. All studies reported cost in hospital level of care, no articles identified in LTCF and community. Nearly all identified references were retrospective hospital database studies (n = 40) and only 1 study was a prospective observational study  [29]  and another study was a decision tree model  [48]. Most studies (n = 15) investigated economic outcomes in all age inpatients.
Our review combined 100-point estimates and ranges from 42 individual studies, which provided more accurate and      comprehensive data of the cost result. Despite the methodological heterogeneity in perspectives, treatment procedure and statistical analysis, each included study met our inclusion criteria, which were defined to identify studies that provided real world estimates of costs, therefore the combination of these data with uncertainty range represented a valuable and reliable summary of CDI-related cost. Furthermore, we evaluated hospital onset CDI and community onset CDI separately. We found that CDI complicating hospitalization cost more than CDI requiring hospitalization and the former had longer attributable hospital stay. Therefore, other factors, such as comorbidity, may contribute to infections and increase the difficulty of CDI treatment.
Cost and LOS estimation of healthcare-associated infections has the potential to be misleading if the confounders such as patients' comorbidities or daily severity of illness were not properly controlled for. Using either the matching design or multivariable regression analysis allows to control known confounders and may, in part, address selection bias  [70]. We found that whether advanced statistical methods were used and described was crucial for the assessment of data quality, which has not be fully captured by the existing quality assessment tool. Therefore in this study we assessed quality of included studies using this new method. Moreover, Nanwa et al  [26]  has evaluated the methodological completeness of most included studies (34 out of 42); we agree with their recommendations regarding possible improvement of future cost-of-illness study.
This is likely because we restricted our search to English language databases. Therefore the cost of CDI management in Canada remains unknown. However, we did not apply any language restrictions to the current review. Effective prevention can reduce the burden of diseases. Strategies have been promoted such as appropriate use  of antimicrobials, use of contact precautions and protective personal equipment to care for infected patients, effective cleaning and disinfection of equipment and the environment, and early recognition of disease as primary prophylaxis  [71]. As CDI is an infectious disease, the population at risk would benefit from an effective vaccine, which is currently under development  [72, 73]. More cost of illness studies for recurrent CDI, or in LTCF, and indirect cost from a societal perspective are needed in the future.
CDI cases were identified either with laboratory test, i. e. positive C. diffcile toxin assay, or hospital discharge diagnosis of C. difficile (primary and/secondary) from administrative datasets using the International Classifications of diseases, Ninth, Clinical Modification, ICD-9-CM 008. 45. Clinical diagnosis was also used in two studies. CDI was classified in three types: Community-onset CDI (CO-CDI) requiring hospitalization, Hospital-onset CDI (HO-CDI) complicating other diseases, or both CDI (Table2). Most of included studies considered HO-CDI (n = 23) or both CDI types (n = 17). Only four studies investigated CO-CDI only. However, subgroup data of CO-CDI is also available in studies that reported both CDI types.
45 Medium No matching; NIS 2005 Cirrhosis-CDI: 61 ± 15 (PD, SD) regression Tertiary: 2002-2006 5 Campbell 2013 National 2005-2011 Comp. General NR 4521 Renal impairment 72. 9 ± 13. 4; Toxin assay High Matching; Health Facts electronic health [31] Advanced Age: 78. 7 ± 7. 4; regression record (HER) database Cancer/BMT 69. 2 ± 14. 0; IBD 61. 2 ± 18. 3; Cabx exposure 61. 2 ± 14. 8 6 Damle 2014 [14] National 2008-2012 Comp. Colorectal 84,648 1266 >18 ICD-9; 008. 45 Medium No matching; University Health System surgery 63 ± 17 (PD, SD) regression Consortium database 7 Dubberke 2008 Missouri 2003-2003 Both Non- 24,691 439 67(18-101) a Toxin assay High Matching; Barnes-Jewish Hospital [33] Surgical regression Electronic record 8 Dubberke 2014 Missouri 2003-2009 Both Recurrent 3958 421 >18 Toxin assay or High Matching; Barnes-Jewish Hospital [2, 34, 71] CDI clinical regression Electronic record diagnosis for recurrent CDI 9 Egorova 2015 National 2000-2011 Comp.
Cardiac 512,217 421,294 >40 ICD-9; 008. 45 Medium No matching; NIS surgery CABG 65. 4 ± 10. 5 (PD, SD) regression VS 66. 1 ± 12. 3 Lawrence 2007 Missouri 1997-1999 Both ICU 1872 76 Primary 68. 9 (34-93) Toxin assay Medium No matching; A 19-bed medical ICU in a [44] Secondary 58. 7 (16-91) regression Midwestern tertiary care re- ferral center. Lesperance 2011 National 2004-2006 Comp. Elective 695,010 10,077 >18 ICD-9; 008. 45 Medium No matching; NIS [45] colonic All 69. 8; Surgery-CDI 68. 7 (SD) regression resections Lipp 2012 [46] New York 2007-2008 Comp. General 4,853,800 3883 >17 ICD-9; 008. 45 Medium No matching; -The SPARCS database- (SD) regression acute care non-federal hospi- tals in New York State Maltenfort 2013 National 2002-2010 Both Arthroplasty NR NR All age band ICD-9; 008. 45 Low No matching; NIS [47] (PD, SD) no regression McGlone 2012 National 2008 Comp.
General NR NR >65 ICD-9; 008. 45 Low No matching; Decision tree model [48] (SD) no regression Nguyen 2008 National 1998-2004 Comp. IBD 527,187 2372 47. 4 ± 0. 2 ICD-9; 008. 45 Medium No matching; NIS [49] (secondary regression diagnosis) Nylund 2011 [50] National 1997,2000, Both Children 10,495,728 21,274 CDI 9. 5 ± 0. 07(SEM) ICD-9; 008. 45 High Matching: Healthcare Cost and 2003,2006 (PD, SD) regression Utilization Project Kids'Inpatient Database O'Brien 2007 [51] Massachusetts 1999-2003 Req. General 3692 1036 Primary 70 ± 17. 6; Secondary ICD-9; 008. 45 Low No matching; Massachusetts hospital 70 ± 17. 2 (PD, SD) no regression discharge data Pakyz 2011 [52] National 2002-2007 Comp. General 30,071 10,857 CDI 61 ± 17 ICD-9; 008. 45 High Matching; University Health System (SD) regression Consorsoum (UHC) Pant 2012 [53] National 2009 Both Organ 244,955 6451 >18, ICD-9; 008.
CDI cases were identified either with laboratory test, i. e. positive C. diffcile toxin assay, or hospital discharge diagnosis of C. difficile (primary and/secondary) from administrative datasets using the International Classifications of diseases, Ninth, Clinical Modification, ICD-9-CM 008. 45. Clinical diagnosis was also used in two studies. CDI was classified in three types: Community-onset CDI (CO-CDI) requiring hospitalization, Hospital-onset CDI (HO-CDI) complicating other diseases, or both CDI (Table2). Most of included studies considered HO-CDI (n = 23) or both CDI types (n = 17). Only four studies investigated CO-CDI only. However, subgroup data of CO-CDI is also available in studies that reported both CDI types. Inpatient Database (HCUP- KID).
Design and patients This is a retrospective, observational, longitudinal analysis from "The Dat'AIDS Cohort", a multicenter cohort of 26,959 HIV-infected patients from fifteen major French HIV centers using the NADIS 1 electronic medical record (Fedialis Medica, Marly le Roi, France) for HIV, HBV-or HCV-infected adults  [7, 8]. Patient data are recorded during medical visits in a structured database, and data quality is controlled systematically during capture, annual assessments and ad hoc processes prior to analyses  [7, 8]. Selected subjects were HIV1-infected patients initiating a first-line cART between Jan-2002 and Dec-2009, achieving an undetectable plasma HIV load (pVL) for at least 12 months, and with available CMV serostatus (anti-CMV IgG positive or negative). Only patients with unchanged cART regimen through the study period were analyzed.
We finally built multivariable models (Table  4 ) for the tree outcomes that were significantly associated with CMV serology in Table  2  (CD8/mm3, CD8%, CD4:CD8 ratio). Final multivariable models included correlates who had a p-value <0. 20 in the univariable models (data not shown). Correlates tested in univariable models were 1) socio-demographic characteristics (sex and age); 2) HIV-related characteristics (Transmission risk group, CDC staging, pre-cART HIV viral load (pVL), nadir CD4 count), CMV serology, time from D0 and the interaction between CMV serology and the time from D0. The interaction term between time and CMV+ serology was forced in the final multivariable models. Analyses used SPSS Advanced Statistics 20 (IBM Corp) and SAS version 9. 4 for Windows.
Ethics statement All subjects participating to this study provided written informed consent for the use of their medical records on the NADIS database, which was approved by the French data protection agency "Commission Nationale Informatique et Liberté" (Registration number: 2001/762876/ nadiscnil. doc). All patients' records were anonymized and de-identified prior to the analysis. This study was carried out in compliance with the International guidelines for human research protection as per the Declaration of Helsinki and ICH-GCP.
Acknowledgments The authors would like to thank the members of the Dat'Aids group:  S. Brégigeon ,  O. Faucher ,  V. Obry-Roguet ,  M. Orticoni ,  M. J. Soavi ,  I. Luquet-Besson ,  E. Ressiot ,  I. Pinot ,  M. J. Ducassou ,.
, 2012) , CH 4  (Frankenberg et al. , 2008; Schneising et al. , 2009) , H 2 O  (Schrijver et al. , 2009)  and the HDO / H 2 O ratio  (Scheepmaker et al. , 2013). As a test of the possible impact of uncertainties in the water line parameters, we have repeated the retrievals of the simulated clear-sky scenario after replacing the line parameters of the water isotopologues. For the simulated spectra the parameters from the high-resolution transmission database were used (HITRAN,  Rothman et al. , 2009). We then performed the retrievals using the water line parameters from  Scheepmaker et al. (2013). Table  4  shows the induced systematic errors for replacing a single isotopologue at a time, and for replacing all modelled water isotopologues simultaneously. It shows that the retrieval of HDO and H 2 O can be very sensitive to spectroscopic uncertainties, especially for the ratio HDO / H 2 O, since HDO and H 16  2 O can show sensitivities with opposite sign, which strengthen each other when taking the ratio (as can be seen from replacing only the H 16 2 O parameters).
Performance analysis for a realistic scenario To show the capabilities of the TROPOMI H 2 O and HDO total column retrievals, we have simulated an ensemble of measurements that reflect a realistic scenario as accurately as possible. In Sect. 5. 1 we describe the input data and measurement simulations in more detail. In Sect. 5. 2 we discuss the results of retrieving the simulated measurements in terms of retrieval bias, precision, and effectiveness of the cloud filtering.
Measurement simulations The simulated measurement ensemble covers a region over the south-western US and north-eastern Mexico as observed by TROPOMI on 4 August 2009. Figures  8  and 9  show the region in terms of various input fields. This region comprises a clear gradient in the relative abundance of HDO with respect to H 2 O due to the transport of humid air from coastal regions inland. We have combined data from the MODIS Aqua satellite (clouds, land/water coverage, surface albedo, aerosol) with data from ETOPO5 (elevation), ECMWF (surface pressure, temperature profiles, specific humidity), TM5 (CO and CH 4 profiles) and LMDZiso (HDO and H 18  2 O profiles) to simulate 27405 TROPOMI measurements on a grid of 135 by 203 ground pixels. This ensemble represents roughly what TROPOMI will observe in 5 min with a daily revisiting cycle.
Discussion and conclusions We have presented an algorithm and performance analysis for the retrieval of total column H 2 O and HDO from TROPOMI measurements onboard the Sentinel-5 Precursor mission. By adapting ESA's operational CO algorithm  (Landgraf et al. , 2016) , we developed a relatively simple approach that is fast but relies on strict filtering for clouds, cirrus and aerosol using a 2-band methane or water retrieval. The ratio HDO / H 2 O will be a useful scientific product in the fields of hydrology and climate research, with the potential to improve our understanding of the processes controlling atmospheric humidity and transport. The first studies in this direction which used a similar type of column-averaged satellite product were using SCIA-MACHY data  (Frankenberg et al. , 2009; Yoshimura et al.
Acknowledgements. H. Hu  and  J. aan de Brugh  are in part financed by the TROPOMI national programme from the  Netherlands Space Office (NSO). LMDZiso simulations used the computing resources of IDRIS under the allocation 0292 made by  GENCI. We thank the two anonymous referees for their useful comments that improved this paper. Edited by: B. Veihelmann Reviewed by: two anonymous referees.
Catalyst characterisation Au, Mo, Cl and Al contents were measured by inductively coupled plasma (ICP) atomic emission spectroscopy (CNRS Centre of Chemical Analysis, Vernaison). Carbon content was determined using an Exeter CE-440 elemental analyser after sample combustion at 1873 K. Temperature-programmed reduction (TPR), BET surface area and H 2 chemisorption were measured using a commercial CHEM-BET 3000 (Quantachrome) unit. The passivated samples were loaded into a U-shaped Quartz cell (10 cm × 3. 76 mm i. d. ) and heated in 17 cm 3 min -1 (Brooks mass flow controller) 5% v/v H 2 /N 2 at 2 K min -1 to 523 ± 1 K, which was held for 1 h. The effluent gas passed through a liquid N 2 trap and H 2 consumption was monitored by TCD with data acquisition/manipulation using the TPR Win™ software.