dataesr/scientific-paragraphs-categorization huggingface dataset:
Finally, in this study we identify an important number of asthmatic and non-asthmatic subjects who agreed to participate. This database could be the basis of ancillary studies, such as exploring etiological aspects by comparing the occupational settings between the different groups of subjects having similar personal factors. A longitudinal follow-up could assess the effectiveness of an early screening of the disease. Moreover, the possibility of getting genetic material would allow to study gene-environment interactions (GEI). The occupational setting-based recruitment of the epidemiological part of the study will enable us to estimate more accurately the prevalence of WRA in a working population. The diagnosis procedure elaborated for the clinical part of the study could be proposed as a standard objective diagnostic procedure if validated.
In the patients with rapidly increasing weight, we measured the plasma free thyroxin and thyroid stimulating hormone concentrations to exclude hypothyroidism and 24-h urinary cortisol to exclude hypercorticism. If there was any clinical doubt, x-rays of the pelvis and lumbar spine were taken (front and profile) to exclude constitutional bone disease. All of these investigations showed normal results. All of the patients were followed until complete pubertal development. Those with low BMI were encouraged to increase their caloric intake. Karyotype was performed in 6 girls and was denoted 46 XX for all of them and in 4 boys and the only abnormality found was a 9p trisomy, usually associated with growth and puberty retardation  [19]. Results were expressed as the means ± SD. The data are normally distributed and p values are corrected for multiple comparisons.
Data analyses and modelling Author-produced version of the article published in Chemosphere  (2016), vol. 155, p. 380-387  The original publication is available at http://www. sciencedirect. com doi:10. 1016/j. chemosphere. 2016. 04. 006 ©. This manuscript version is made available under the CC-BY-NC-ND 4. 0 license http://creativecommons. org/licenses/by-nc-nd/4. 0/ Data were checked for normality using the Shapiro-Wilk test and were analysed using the Kruskal-Wallis test (a non-parametric test) and the Mann-Whitney test using R language (R Core Team, 2014a, b). Assuming sediment was the main source of PFASs for the gammarids, accumulation data were adjusted to a two-compartment model  (Spacie and Hamelink, 1985; Landrum, 1989). Equation (  1 ) was used to estimate the uptake and elimination coefficients (k u and k e , respectively).
Acknowledgements This study was funded by the  Rhone-Mediterranean and Corsica Water Agency  and the  Rhone-Alps Region  within the Rhone ecological restoration plan. The  Aquitaine Region  and the  European Union  ( CPER A2E  project) are acknowledged for their financial support. This study also benefitted from grants from the  French National Research Agency (ANR)  as part of the  Investments for the Future Program , within the Cluster of  Excellence COTE  ( ANR-10-LABX-45 ). We thank  Linda Northrup  ( English Solutions, Voiron, France ) for copy editing the text.
AERONET data In the present study, ground-based AODs were measured using a CIMEL 318 radiometer. This instrument is available at the AERO-NET stations, which form a global network of sun photometers. AERONET data are used to derive AOD and other aerosol characteristics using an inversion method applied to measurements of downwelling solar radiation  (Holben et al. , 1998; Dubovik and King, 2000). Standard protocol makes that AOD is provided every 15 min for spectral bands centered at 340, 380, 440, 500, 675, 870, and 1020 nm with an accuracy of ±0. 02. For the level 2. 0 products, the AOD uncertainties are of the order of 0. 01 in the visible and nearinfrared channels  (Eck et al. , 1999). In this study, instantaneous AOD data at 440 nm and Ångstr€ om exponent within 440e675 nm are retrieved from the cloud screened and quality-assured Level 2.
Surface albedo data In this paper, we use the daily-averaged surface albedo (a), which is borrowed from the climatology constructed using seven years (2004e2010) of MODIS black-sky visible albedo. In particular, we use the MCD43 product from collection 5. 0, which is obtained by inverting multi-date, multi-angular, cloud-free, atmospherically corrected, surface reflectance observations acquired by the MODIS instruments on board the Terra and the Aqua satellites over a 16day period. The accuracy of the high quality MODIS operational albedo is well less than 5% at the majority of the validation sites studied by  Cescatti et al. (2012). In addition, only albedo values with a high confidence index were used to build the albedo climatology used in the present article. The latter was built at a spatial resolution of 0.
In contrast to the case of DARF at TOA and SUR, due to the AOD cycle, daily averaged DARF in ATM is always underestimated by using constant MODISderived AOD value (Fig. 7 ). The diurnal cycle of AOD cannot be captured by using MODIS/ Terra or MODIS/Aqua data alone, because only one AOD estimate is available per day at most. Therefore, differences in daily averaged DARF in ATM, at TOA and SUR for each season and the year-round are shown in Fig. 8 , when the constant MODIS/Terra or MODIS/ Aqua AOD value is used instead of AERONET-observed AOD variability. If constant MODIS/Terra or MODIS/Aqua AOD is used, the daily averaged DARF at TOA is absolutely overestimated by the range of 0. 2e3. 9 Wm -2 (4. 4e34. 2% in relative ones) with 2. 0 Wm -2 on average for each season and by around 1. 2 Wm -2 for the yearround (Fig.
It can be clearly seen that, for negative DARFs, the higher the surface albedo, the stronger impact on estimates of DARFs at TOA. For dust aerosols, the impact on estimates of DARFs using constant MODIS/Terra (MODIS/ Aqua) data estimates can reach up to 117. 6% (233. 4%). The magnitude of influence becomes 37. 8% (67. 6%) for biomass burning aerosols. However, for continental aerosols, the impact on estimates of DARFs using constant MODIS/Terra (MODIS/Aqua) data estimates is less than 20. 2% (36. 0%). Interestingly, the biases in the calculation of DARF at TOA caused by using MODIS/Aqua AOD seems systematically to be larger compared with using MODIS/ Terra AOD. The underlying factors leading to this difference warrant a further investigation in the future.
Summary In this study the influence of the diurnal variation in AOD on the MODIS (aboard Terra and Aqua) -derived DARFs at the top of the atmosphere, surface and in the atmosphere has been investigated at the AERONET Beijing site, China, during the year of 2010. To perform such an investigation, we calculated seasonal and annual mean of the hourly averaged AERONET observed AOD data as input for the radiative transfer code 6S, and compared them to the daily averaged DARF calculated using single MODIS Terra and Aqua AOD values as inputs. Assuming constant MODIS/Terra (MODIS/Aqua) AOD throughout the daytime instead of the observed diurnal variation of AOD in determining daily averaged DARF, we found that the daily averaged DARF estimates is on average underestimated by around 2. 0 Wm À2 at TOA and around 38.
8 Wm À2 at SUR. By contrast, it is overestimated by around 36. 8 Wm À2 in terms of DARF in ATM. The impact on DARF estimates using MODIS-derived AOD data not only depend on the diurnal variation of AOD during the daytime but also depend on whether the maximum AOD occurred around the Terra or Aqua overpass time. The impact of using MODIS-derived AOD for estimation of DARF at TOA is sensitive to surface albedo and aerosol type. For negative DARFs, the impact on estimates of DARFs at TOA using constant MODIS AOD data grows with the increase in surface albedo. For different aerosol types, DARF estimates at TOA changes from negative to positive, depending on the surface albedo. For dust aerosols, the impact on estimates of DARFs using constant MODIS/ Terra (MODIS/Aqua) data estimates can reach up to as large as 117.
Acknowledgements The work was performed under the auspices of  Ministry of Science and Technology of the People's Republic of China  (Grant  2014BAC16B01 ),  Natural Science Foundation of China  (Grants  41590874 ,  91544217 ,  41471301 ,  41405035 ,) and  Chinese Academy of Meteorological Sciences  (Grant  2014R18 ),  Guangdong Provincial Science and Technology Plan Projects  (Grant  2014A010101151 ). The MODIS AOD data used in this study were also acquired as part of the  NASA's Earth-Sun System Division  and archived and distributed by the  Goddard Earth Sciences (GES) Data and Information Services Center (DISC) Distributed Active Archive Center (DAAC).
Data collection and analysis Two review authors independently selected studies for eligibility using Early Review Organizing Software (EROS). Similarly, two review authors independently used predesigned data collection forms to extract information from the original study reports about the participants, methods of randomisation, blinding, comparisons of interest, number of participants originally randomised by arm, follow-up losses, and outcomes, and they assessed the risk of bias. We resolved any disagreement by consulting a third author and contacted trial investigators of identified trials to obtain additional information. We used standard methodological procedures expected by Cochrane.
Electronic searches We searched the following databases up to 10 May 2016: • the Cochrane Skin Group Specialised Register using the search strategy in Appendix 1; • the Cochrane Central Register of Controlled Trials (CENTRAL) 2016, Issue 5, in the Cochrane Library using the strategy in Appendix 2; • MEDLINE via Ovid (from 1946) using the strategy in Appendix 3; • Embase via Ovid (from 1974) using the strategy in Appendix 4; and • LILACS (Latin American and Caribbean Health Science Information database, from 1982) using the strategy in Appendix 5.
Trial registries We searched the following trials registries and portals up to 10 May 2016: • the metaRegister of Controlled Trials (www. controlledtrials. com); • the US National Institutes of Health Ongoing Trials Register ClinicalTrials. gov ( www. clinicaltrials. gov); • the Australian New Zealand Clinical Trials Registry ( www. anzctr. org. au); • the World Health Organization International Clinical Trials Registry Platform ( www. who. int/trialsearch); and • the EU Clinical Trials Register ( www. clinicaltrialsregister. eu). We used the terms "non melanoma skin cancer", "basal cell carcinoma", "squamous cell carcinoma", "bowen disease", and "actinic keratosis".
Selection of studies Two review authors (MO and GS) independently selected studies for eligibility with Early Review Organizing Software (EROS) (Ciapponi 2011; Glujovsky 2010; Glujovsky 2011). We checked the titles and abstracts of all of the retrieved studies to determine if they fulfilled the inclusion criteria previously proposed. We assessed the full texts of identified studies to confirm their final inclusion. We resolved all disagreements by involving a third author (IA-R). We were not blinded to characteristics such as the authors' names, institutions, or the journal of publication at any stage of the review. We recorded the reasons for our exclusion of potential studies in the 'Characteristics of excluded studies' tables  (Higgins 2011).
Data extraction and management Two review authors (CS and JG) used predesigned data collection forms to retrieve information, such as randomisation methods, blinding of participants and personnel, comparisons, number of participants by arm, and follow-up losses, from the original study in an independent way  (Higgins 2011). We tested this format prior to extended use. We resolved any disagreement by discussion with a third review author (JN). We entered extracted data into Review Manager 5 for further analyses (Review Manager 2012).
'Summary of findings' tables We used the guidelines of the Grading of Recommendations Assessment, Development and Evaluation (GRADE) Working Group  (Guyatt 2008)  to assess the quality of the evidence related to primary outcomes, and developed a 'Summary of findings' table with the GRADE profiler software. The GRADE system assesses the quality of evidence based on the extent to which users can be confident that an association reflects the item being evaluated  (Guyatt 2008). Assessment of the quality of evidence included consideration of the risk of bias, heterogeneity, directness of the evidence, risk of publication bias, and precision of effect estimates  (Guyatt 2011; Guyatt 2011a; Guyatt 2011b; Guyatt 2011c; Guyatt 2011d; Guyatt 2011e; Guyatt 2011f; Guyatt 2011g).
Incomplete outcome data Regarding attrition bias for our primary outcomes, Green 1999 declared: "At the end of the study in year 5, 238 (15%) participants had withdrawn without a complete skin examination by a dermatologist in the follow-up period. " The percentage of losses to follow-up was similar for all groups, and it was the same for all primary outcomes (group one: 12. 37%; group two: 17. 15%; group three: 14. 1%; group four: 15. 01%). We judged this domain at low risk of bias. By contrast, the number of participants lost to follow up was bigger for actinic keratosis (ranging from 28% to 36%), and the risk was considered high for this outcome. One thousand three hundred and eighty-three participants completed the full followup period of 4. 5 years, but we included the 1621 participants originally randomised under intention-to-treat (ITT) analysis.
Chemical characterization and soil sampling Total concentrations of As, Cu, Zn and Pb were determined in situ using XL3t800 NITON © portable X-ray fluorescence field apparatus (pXRF), in order to defined the metal(loid)s distribution and to targeted the soil sampling. The signals were calibrated with the chemical analyzes of the soil samples and considered the soil moisture. Maps of As, Cu, Zn and Pb concentrations were drawn by interpolating data by kriging (ArcGIS © ). Four soilswere sampled inthe surface, non-saturatedblack layer (0-10 cm) in zones with contrasting vegetation cover. The soils were sieved at 2 mm through sterile sieves, placed in in sterile glass jarsand stored at 5°C. Their water content was determined by drying at 105°C for 24h. In order to study the chemical composition of different particle size fractions, soil sub-samples wereseparated into three fractions by mechanical sieve shaker: coarse sand (>200 µm), fine sand (50-200 µm) and loam and clay (<50 µm).
2). Leached Cu and Pbvalues seemed to reach a threshold independently of the original ground level pollution (Tab. 2). The percentage of leached pollutants were very low (below 2%), however theyrevealed that a higher proportion of pollutants were mobilizedfrom the less contaminated soil. These observations can be explained by the saturation of these elements in the aqueous phase of the leaching batch systems, which would induce their re-precipitation. In order to evaluate the mobility of metals and arsenic in these conditions, the geochemical PHREEQCi3. 0. 6. 7757 code together with the minteq. v4thermodynamic database was used by integrating the complete chemistry of the leached water (SM3). In these acid (5<pH<6) and oxidizing conditions,the PHREEQCi   (Smedley and Kinniburgh, 2002). However, in soil II the proportion of As(III) in the percolating water was not higher than in the solid phase.
Background: Maintenance treatment (MTx) in responders following first-line treatment has been investigated and practiced for many cancers. Modeling and simulation may support interpretation of interim data and development decisions. We aimed to develop a modeling framework to simulate overall survival (OS) for MTx in NSCLC using tumor growth inhibition (TGI) data. Methods: TGI metrics were estimated using longitudinal tumor size data from two Phase III first-line NSCLC studies evaluating bevacizumab and erlotinib as MTx in 1632 patients. Baseline prognostic factors and TGI metric estimates were assessed in multivariate parametric models to predict OS. The OS model was externally validated by simulating a third independent NSCLC study (n = 253) based on interim TGI data (up to progression-free survival database lock).
The AVAPERL trial compared maintenance bevacizumab plus pemetrexed vs. bevacizumab alone and showed a significant prolongation of PFS  [14]  but not of OS  [15]  following bevacizumab plus pemetrexed compared to bevacizumab alone. The objectives of this work were 1) to develop a model for OS after maintenance therapy in NSCLC based on erlotinib data from SATURN and ATLAS, 2) to prospectively predict the probability to success of AVAPERL study and perform an external validation by simulating the OS outcomes of AVAPERL study (pemetrexed data) based on interim tumor size data (up to PFS database lock).
Trials and data Data were collected from all patients enrolled in three studies evaluating maintenance treatment after first-line NSCLC therapy. In all studies, patients whose disease did not progress after four cycles of first-line treatment were randomized to maintenance treatment. Details of the studies can be found in the respective papers, in the introduction section and in Table  1. The studies complied with the Declaration of Helsinki and Good Clinical Practice guidelines, and were approved at all investigating centers by local ethics committees. All patients provided written informed consent for participation and publication of the data  [11] [12] [13] [14] [15]. An ethics statement was not required for this analysis as they have been provided in each of the three individual studies  [11] [12] [13] [14] [15].
Simulations OS of AVAPERL study were simulated based on TGI metrics estimated using interim tumor size data to predict the likelihood of a successful OS outcome for AVA-PERL and further assess performance of the OS model (external validation). In order to calculate the prediction interval and make statistical inferences, the study was simulated multiple times (20,000) by sampling survival model parameters from their estimated uncertainty distribution. Patient survival times were drawn from the appropriate survival distribution defined by model parameters, baseline prognostic factors and TGI metric of AVAPERL patients. Censoring was simulated in sampling patient study duration, assumed to be independent of death. Patient survival times were censored assuming a uniform distribution of patient study duration from 50 to 140 weeks, which was consistent with the minimum and the maximum time period the patient stayed in the SATURN study without a death event.
Data Patients with at least one post-randomization tumor size measurement were included in this analysis. Overall 1534 patients were evaluable to estimate TGI metrics used for building the OS model: 837 (94 %) out of 889 patients from SATURN, and 697 (94 %) out of 743 patients from ATLAS. Interim AVAPERL data used as the external validation dataset were collected by the time of PFS database lock (data cutoff: July 2011) and included 231 evaluable patients out of 245 (94 %) randomized to maintenance treatment.
Simulation The final OS model was applied to prospectively predict the expected OS outcome of AVAPERL study (external validation). The goal was to predict the likelihood of a successful OS outcome using interim tumor size data collected by the time of PFS database lock (data cutoff: July 2011). This dataset was not used for model-building (Table  1 ). Median OS was not yet reached at the time of data cutoff, and the immature OS data that were observed by the time of data cutoff were not used. Patients in AVAPERL study had more favorable prognostic factors than those from SATURN and ATLAS with a smaller proportion of ECOG score >0 (52 % vs. 66-69 %) and smaller baseline tumor size (5. 2 cm vs. >6 cm) (Table  1 ). Simulations indicated that pemetrexed plus bevacizumab as maintenance treatment in AVAPERL was unlikely to demonstrate a significant OS prolongation vs.
These excluded patients who died or dropped out of the study early before the first tumor size measurement may have rapidly growing tumors. However, this may not have a significant impact on this analysis because 94 % of the patients were evaluable. The model successfully simulated the OS outcomes of the pemetrexed maintenance study AVAPERL based on interim tumor size data collected by the time of PFS database lock before median OS was even reached. This is the first modeling framework for maintenance treatment and one of the few such frameworks validated in simulating an independent study with a drug with a different mechanism of action (pemetrexed) compared to the one used to develop the model (erlotinib), providing support to the hypothesis that TGI metrics capture drug effect independent of treatment  [5].
Conclusion In conclusion, a robust TGI-OS model linking OS with TGI metrics and prognostic factors was developed for maintenance therapy following first-line NSCLC treatment. The model successfully predicted the OS outcomes of an independent study (AVAPERL) based on interim tumor size data (up to PFS database lock), indicating that the model may be used for trial simulation and facilitate interpretation of interim data and development decisions. The model was built based on erlotinib data and externally validated using pemetrexed data, suggesting that TGI-OS models may be treatment-independent. The results also supported the use of longitudinal tumor size and TTG as endpoints in early clinical oncology studies.
driven by time-dependent tumor size. In a typical clinical trial setting, tumor size is only observed until disease progression when treatment is stopped. Median time of last tumor size observation was 11-18 weeks while median OS was four times as long (45-63 weeks) in our model-building dataset. As a result TTG is much shorter than time to death as shown with the median estimates of TTG and OS in Fig. 2. Accounting for tumor size-dependent hazard would have implied an extrapolation substantially beyond last tumor size observation, leading to unrealistically large tumor sizes as the model assumes exponential growth after end of treatment.
To this end, we used annotated genomes from P. putida strains available in the NCBI data base (http://www. ncbi. nlm. nih. gov/genome/?term = putida, May 2016), that includes six clinical P. putida isolates, to carry out in silico studies of the pyoverdine locus and the TonB-dependent transporters. To support the bioinformatic studies we have used four available P. putida clinical strains. The four strains were isolated at the Hospital of Besançon (France). P. putida HB13667 and H8234 were isolated from the blood of two in-patients presenting general bacteremia  (Molina et al. , 2013 (Molina et al. , , 2016)) ; P. putida HB4184 was isolated from sputum of a cystic-fibrosis patient  (Molina et al. , 2016) ; and P. putida HB3267 which exhibits an antibiotic multi-resistant phenotype and was isolated from the blood of a deceased in-patient  (Molina et al.
Bioinformatic Analysis All the strains used in this study are listed in Table  1. Their sequences are available in the GenBank data base. Genome comparisons to determine protein homology, common genes, and genomic organization were performed using the RAST software  (Aziz et al. , 2008)  and BLASTp. Phylogenetic studies of individual proteins were performed using the platform Phylogeny. fr (http://www. phylogeny. fr/) (Dereeper et al. , 2008). Phylogenetic studies of complete genomes were performed using Composition Vector Tree (http://tlife. fudan. edu. cn/cvtree/). These phylogenetic studies were carried out as recommended by the user manual (http://tlife. fudan. edu. cn/cvtree/help/index. html) with a Kpeptide length of 6, as recommended by the authors for prokaryote organisms  (Xu and Hao, 2009).