dataesr/scientific-paragraphs-categorization huggingface dataset:
Discussion Analyses based on a representative sample of 4-to 13-year-old children in France, using the most recent INCA 2 database  (17, 18, 20)  , estimated total water consumption at 1324 ml/d and plain water consumption at 453 ml/d. Beverage consumption was 348 ml/d. About 90 % of boys and girls had a total water intake below EFSA recommendations. For boys and girls aged 9-13 years, total water intake was about 0•6 litres short of the DRI. The second criterion of adequate hydration was in the range of 0•76 litres water/1000 kcal (4184 kJ) as opposed to the desirable 1•0 litres/1000 kcal. In studies based on data from the US National Health and Nutrition Examination Survey (NHANES)  (5)  , total water intake for the same 4-13 years age group was estimated at 1580 ml/d and plain water consumption at 431 ml/d.
Beverage consumption was estimated at 630 ml/d, with different proportions from milk, agua fresca (fruit water) and soda depending on age. A majority of Mexican children failed to meet the DRI for water. The INCA 2 national dietary survey, conducted in 2006-2007 by ANSES, continues to inform the national food and nutrition policy. The present findings, based on the most recent INCA database, show that French children consumed mostly milk at breakfast and plain water at lunch and at dinner meals. The beverage consumed in school canteens was almost exclusively plain water. Promoting water consumption in schools is the goal of French national policies and guidelines  (19). Sodas were more likely to be consumed in full-service and fast-food restaurants. On the average, about 10 % of daily energy in the French children's diets came from caloric beverages, including milk.
That per capita consumption of bottled water is higher in France than in the USA is well established  (37). The consumption of sodas by children has been reported to be lower in France than in the USA  (5, 13). The present analyses of the INCA 2 database  (38)  show that compared with US children, the consumption of plain water by French children was much higher and the consumption of both milk and soda was considerably lower. A much higher proportion of dietary water came from moisture in foods, suggesting a higher consumption of low-energy-density foods such as vegetables and fruits. Nevertheless, in both studies children are not meeting the daily needs for water as recommended respectively by EFSA and the Institute of Medicine. The present analyses had limitations. First, the INCA 2 data, based on self-report, are subject to random inaccuracies and systematic reporting biases.
The proxy recall for younger children may be an additional source of bias. The 7 d food records impose a burden on respondents and data quality can be variable. Validation data have been published before  (39). Most important, the INCA 2 data were collected in 2006-2007. Given rapid shifts in beverage consumption patterns, especially by children and adolescents, the data are in danger of becoming obsolete. For example, recent data from the USA showed a plunge in added sugars consumption among children and adolescents aged 2-19 years  (32). While the INCA 3 study is already underway, the INCA 2 database remains the standard source of information about dietary intakes in France and is comparable in scope and importance to NHANES in the USA. The present analyses represent one of the first explorations of the consumption of drinking-water by children in France.
Intakes (ml/d) of total water, plain water and water from beverages and foods* by age group, sex and sociodemographic group; French children aged 4-13 years (n 835), INCA 2 (Étude Individuelle Nationale des Consommations Alimentaires 2006-2007) Water content of all beverages and foods was calculated using the CIQUAL (Centre d'Information sur la Qualité des Aliments) 2013 database. †' <1' means below the poverty threshold; ' ≥2' means more than two times higher than the poverty threshold.
Two data sets at a time are matched, using the dynamic criteria, with the satellite averaging four observations for SCIAMACHY and two for GOSAT, which reduces the fit errors. Since different data sets will have different data gaps and time ranges, the TC-CON results will be somewhat different for each comparison. Plots are individually examined to ensure that there are adequate data. Stations are removed when there are large errors for any of the above errors, and locally influenced stations are also removed (see Sect. 2. 1). The stations that were excluded based on variability of results for changes in the time range on the order of a few months are Eureka for the models and Sodankylä, Bialystok, Saga, and Lauder_125HR for the satellites. At Bialystok, for example, the GOSAT-TCCON seasonal cycle was 0.
CO 2 yearly growth rate The same fitting program in the above section, CCGCRV, also calculates a yearly increase. In Table  4  we compare the fitted yearly increase for TCCON, which ranges from 1. 92 to 2. 55 ppm yr -1 for the different stations and time ranges, to each of the data sets. None of the TCCON/satellite or TCCON/model differences reach significance for the t test, which is done when there is more than one result per bin. However, the GOSAT Lamont station is low at -0. 3 ppm yr -1 , with -0. 1 of this estimated to result from co-location error. creases 2006-2014, and 1. 76, 2. 22, 1. 60, 1. 89, 2. 42, 1. 86, 2. 63, 2. 06, 2. 17 ppm yr -1 for Mauna Loa yearly increases 2006-2014, with error bars 0. 05-0. 09 ppm yr -1 for global and 0. 11 ppm yr -1 for Mauna Loa. The average global yearly increase predicted from the above using the time periods in Table  4  is shown in the last column of Table  4.
The correlation r value between the yearly increase in TCCON and the above ESRL global yearly increase is 0. 61, and the correlation with the Mauna Loa yearly increase is 0. 67, whereas the correlation r value between the yearly increase in TC-CON and the yearly increase in columns is 0. 82. Therefore, the variability seen in Table  4  is partly from the time range used but also partly from variations due to geographic location (and sampling). Reuter et al. (2011, JGR, Table 2)  found agreement within the calculated errors at Park Falls and Darwin for BESD-SCIAMACHY and CT2009 vs. TCCON. However, older data sets were used for this result. Looking specifically at Park Falls, we see 1. 80 ± 0. 14 and 2. 10 ± 0. 22 for SCIAMACHY and TCCON, respectively and at Darwin 1. 67 ± 0. 08 and 2. 16 ± 0.
Seasonal cycle phase This section looks at the time offset correlation and standard deviation between the test data sets and TCCON. This checks whether, for example, a seasonal cycle is delayed or ahead of the TCCON seasonal cycle, which has important implications for flux estimates  (Keppel-Aleks et al. , 2012) , whether there are seasonally dependent biases that are affecting the seasonal cycle, and whether the data sets are seeing the same seasonal cycle. To compare seasonal cycle amplitudes, all data sets have 2 ppm yr -1 subtracted to approximately remove secular increases (over the ±60 days' offset this has a very small effect). For a 0 day offset, the data sets are matched as usual. For a 1 day offset, TCCON is moved forward by 1 day and compared to the data set. This is repeated for all offset times.
Correlations are fit to a second-order polynomial to determine the phase minimum difference. As TCCON is moved forward or backward in time, different points will match up, particularly when there are data gaps in either data set. This can cause difficulties in interpretation. The maximum correlation is limited by the ratio of the error to the variability. It follows from the definition of correlation that corr_max = corr o 1 1 + ε x σ x 2 1 + ε y σ y 2 , (3) where corr o is the noise-free correlation, ε x is the error on x and σ x is the true variability for x, ε y is the error on y and σ y is the true variability for y. Because we are estimating σ and ε, there is uncertainty on the correlation maximum. In our case σ y is taken to be the TCCON variability and ε y is esti-   mated using Table  2  with 10 SCIAMACHY and 4 GOSAT averages.
Results. We obtained medium-resolution (R ∼500) spectra in the 3-4 µm range for nine high-mass YSOs in the LMC. Additionally, we analyzed archival ISAAC data of two LMC YSOs. We detected absorption bands due to solid H 2 O and CH 3 OH as well as the 3. 47 µm absorption band. The properties of these bands are investigated based on comparisons with Galactic embedded sources. The 3. 53 µm CH 3 OH ice absorption band for the LMC YSOs is found to be absent or very weak compared to that seen toward Galactic sources. The absorption band is weakly detected for two out of eleven objects. We estimate the abundance of the CH 3 OH ice, which suggests that solid CH 3 OH is less abundant for high-mass YSOs in the LMC than those in our Galaxy. The 3. 47 µm absorption band is detected toward six out of eleven LMC YSOs.
2002; Boogert et al. 2015). However, the exact identification of the 3. 47 µm band carrier is still under debate. Furthermore, properties of the 3. 47 µm band in low metallicity environments are poorly understood. In this paper, we present the results of L-band spectroscopic observations toward embedded high-mass YSOs in the LMC with the Infrared Spectrometer And Array Camera (ISAAC) at the Very Large Telescope (VLT) of the European Southern Observatory (ESO). Details of target selection, observations and data reduction are summarized in §2. Results of observations and spectral analysis are described in §3. Properties of solid methanol and the 3. 47 µm absorption band in the LMC are discussed in §4. Implications for the formation of organic molecules in low metallicity galaxies are also discussed in this section.
Selection of targets Nine high-mass YSOs are selected based on our previous nearinfrared spectroscopic observations with AKARI presented in  Shimonishi et al. (2010)  and  Shimonishi (2012). In addition, we obtained ISAAC spectroscopic data from the ESO archive for two high-mass YSOs (089. C-0882(C); PI: J. M. Oliveira) whose AKARI spectra are also presented in  Shimonishi (2012). All of these YSOs show H 2 O and CO 2 ice absorption bands in their near-infrared spectra, suggesting that they are appropriate targets to investigate minor ice species. Tentative detections of solid CH 3 OH, CO, and XCN bands were reported for several AKARI samples in our previous study, but these were less conclusive due to the low spectral resolution (R ∼ 80) and low spatial resolution. We selected sources with relatively large ice column densities and high L-band fluxes for the present observations.
Details of the targets and observations are summarized in Table  1. Notes. ( a) Spitzer/IRAC band 1 (3. 55 µm) magnitude after color correction. ( b) Total on-source integration time. ( c) H16, H29 and H37 refer to HIP16368, HIP29134 and HIP37623, respectively. Notes. ( a) Data taken from the SIMBAD database. ( b) Estimated assuming [K] -[L'] = -0. 05 (ref. ISAAC web page for spectroscopic standards).
Data reduction The raw data are reduced using our own IDL-based programs developed for VLT/ISAAC slit spectroscopy data. Data of two archival sources are reduced in the same manner as our target sources. First, individual exposure data are coadded to cancel bad pixels and to increase the signal-to-noise ratio. Since the present spectroscopic observations were performed with the ABBA chopping sequences, spectra of objects are detected at three positions along the chopping direction on coadded data (one positive spectrum at the ON position and two negative spectra at the OFF positions). To extract each spectrum, we measure the central position and FWHM of signals along the spatial direction at multiple wavelength points. We then extract the three spectra using the extraction width and position determined above and combine them into one spectrum.
Uncertainties of data are estimated based on the signals at sky positions, which are typically located at 15 pixels apart from the source position. We extract spectra of targets and standard stars in the same manner. The extracted spectra are smoothed by a Savitzky-Golay filter with an appropriate polynomial degree and filter width. To obtain flux calibrated spectra, the target spectra are divided by the spectrum of the associated standard star. The wavelength calibration is carried out using telluric absorption lines before this division, and slight wavelength shifts between target's and standard star's spectra are corrected. We also correct airmass differences between targets and standard stars. A theoretical spectrum of the standard stars is applied to the spectra for flux calibration. We here assume blackbody spectra for the  The reduced spectra are fine-tuned with further wavelength calibration, absolute flux calibration, and appropriate smoothing as necessary.
Calibrated VLT/ISAAC spectra are shown in Figs. 1-2. Since broad wavelength coverage is crucial for reliable continuum determination in the subsequent spectral analysis, AKARI/IRC grism spectra (λ/∆λ ∼100, data taken from  Shimonishi et al. (2010)  and  Shimonishi (2012) ) are concatenated to the short and long wavelength sides of the ISAAC spectra. The IRC spectra are scaled to match the ISAAC spectra around 3 µm and 4 µm (see also.
Spectral fitting: 3. 2-3. 7 µm region We fit a laboratory ice spectrum to the observed spectra to derive ice column densities. Since the 3. 53 µm CH 3 OH band partially overlaps with the 3. 47 µm absorption band, these two absorption components are simultaneously fitted with a χ 2  minimization method. Wavelength regions between 3. 3 µm and 3. 7 µm are used for the fit. For the CH 3 OH band, we fit a laboratory profile of the pure CH 3 OH ice at 10 K, which is taken from the Leiden Molecular Astrophysics database 2  (Gerakines et al. 1995 (Gerakines et al. , 1996)). We tried to fit various CH 3 OH ice mixtures in this analysis and we confirm through visual inspection that the pure CH 3 OH ice results in the best fit among the investigated ice mixtures. It is reported that the profile of the pure CH 3 OH ice also fits well to the 3.
The   (Gerakines et al. 1995). The result of the fitting is shown in Fig. 5  and derived column densities are summarized in Tables  3  and 4. The resultant H 2 O ice column densities are somewhat larger than those derived by AKARI data in  Shimonishi et al. (2010) , particularly for ST2, ST5, ST7, and ST10. This is because ISAAC spectra are less contaminated by surrounding emissions by PAH and warm dust. For CO and CO 2 ices, we use the same method as presented in  Shimonishi et al. (2010)  for spectral fitting and for calculation of ice column densities. The results of spectral fitting are shown in Fig. B. 1. The derived column densities are summarized in Tables 4. The resultant ice column densities are generally larger than the previous estimates due to a decrease in the overall flux level of AKARI spectra.
ST6: The source shows the deepest absorptions of the H 2 O, CH 3 OH, and 3. 47 µm band among the sources examined in this study. This suggests the deeply-embedded and chemically-rich nature of the source. We additionally analyzed Spitzer/IRS 5-33 µm spectrum of the source to investigate properties of ice absorption bands in the mid-infrared region. The IRS spectral data were taken from the SAGE-Spec database  (Kemper et al. 2010; Woods et al. 2011). The result of the analysis is presented in § 3. 3.
Ice absorption bands in the mid-infrared spectrum of ST6 Mid-infrared spectral regions provide us with another piece of important information about ice mantle compositions that is complementary to the near-infrared spectral data. Figure  6a  shows 2. 5-33 µm spectrum of ST6, which shows the richest ice absorption bands in the infrared region among the sources we investigated. We examined IRS data of the other sources, but those are severely contaminated by surrounding emissions by PAH and warm dust. Detailed analysis of minor ice absorption bands is thus difficult except for those of 15. 2 µm CO 2 ice band, which were already presented in previous studies  (Oliveira et al. 2009; Seale et al. 2011). The IRS spectrum of ST6 is presented in  Matsuura et al. (2014) , but we carried out the analysis of ice absorption bands.
Acknowledgements. This research is based on observations with the  Very Large Telescope  at the  European Southern Observatory, Paranal, Chile  ( programme ID 090. C-0497 ). We thank the VLT and ISAAC operation team members. We are especially grateful to the support astronomers for many helpful comments on site during our observations. This work uses data obtained by the  AKARI satellite , a  JAXA  project with the participation of  ESA , and those obtained by  NASA's Spitzer Space Telescope. We are grateful to all the members who contributed to these projects. This work has made extensive use of the Leiden Ice Database. This work is supported by the  Japan Society for the Promotion of Science (JSPS)  Research Fellow (24006664) and a  Grant-in-Aid  from the  JSPS  ( 15K17612 ). Finally, we would like to thank our referee,  Jacco Th.
Data availability The following satellite data used in this study are publicly available: CALIPSO, https://eosweb. larc. nasa. gov/project/calipso/calipso_table; MLS, http://mls. jpl. nasa. gov/products/h2o_product. php; COS-MIC, http://cdaac-www. cosmic. ucar. edu/cdaac/. The data from the balloon experiments can be obtained by contacting the principal author.
Acknowledgements. This work and the TRO-pico project (http://www. univ-reims. eu/minisite_134/tro-pico/homepage-presentation-of-the-project, 12171 , 21885. html) were supported by the  French Agence Nationale de la Recherche (ANR)  under contract  ANR-2010-BLAN-609-01  and  H2020 ARISE2  project (ref. 653980, http://arise-project. eu/). We express our sincere gratitude to the personnel of IPMet for providing an infrastructure and manpower, especially for their invaluable help with the balloon operation during the campaign. We also thank three anonymous reviewers for useful remarks that helped to improve the article. Edited by:  E. Jensen Reviewed  by: three anonymous referees.
Approximately 74% of eGFP-LacI-CENP-C transfected cells recruited CENP-T to the LacO locus, as described previously (Fig. 6A ; Supplemental Fig. S7 ;  Tachiwana et al. 2015). siRNA targeting Spt16 reduced the accumulation of CENP-T to the locus tethered with eGFP-LacI-CENP-C by ∼25% within 24 h (Fig. 6A ). These data indicate that Spt16 is required for the efficient de novo accumulation of CENP-T to a locus tethered with eGFP-LacI-CENP-C. Since FACT is a pleiotropic factor with many roles in cellular metabolism, its disruption may indirectly impact centromere function. We thus tested whether a direct artificial tethering of Spt16 to DNA using the LacI-LacO system could stimulate de novo local CENP-T accumulation. We transfected eGFP-LacI-Spt16 into cells that stably harbor the LacO array. After 24 h, we performed immunofluorescence for endogenous CENP-T.
The CCAN component CENP-I was used as an endogenous kinetochore marker as a control to ensure that we could efficiently identify the LacO locus. We found that CENP-T accumulates at the LacO locus in ∼10% of cells transfected with eGFP-LacI-Spt16 (Fig. 6B ). We performed the same experiment using a truncated Spt16 lacking the region of the CTD identified to be important for CENP-T/-W binding (amino acids 940-1047 [LacI-eGFP-Spt16ΔCTD]). The truncated LacI-eGFP-Spt16 fusion was stable and expressed at a level to similar that of the full-length construct (Fig. 6B ). However, CENP-T was not recruited to the locus. Taken together, these data show that tethering Spt16 to the LacO locus is sufficient to stimulate CENP-T accumulation in vivo and that CENP-T accumulation in vivo depends on the CTD of Spt16.
Following 24 h, cells were fixed, and immunofluorescence for CENP-T and CENP-I was performed. The percentage of eGFP-LacI-Spt16-positive cells in which CENP-T colocalized with either eGFP-LacI-Spt16 (n = 390) or eGFP-LacI-Spt16ΔCTD (n = 209) was counted. CENP-T was not recruited by eGFP-LacI-Spt16ΔCTD. FACT chaperones the CENP-T/-W complex Discussion CENP-T/-W deposition at centromeres occurs de novo during S phase  (Prendergast et al. 2011) , and our data confirm this finding. However, we found that CENP-T deposition at centromeres is uncoupled from DNA synthesis (Fig. 1 ). This suggests that events occurring in S phase, other than DNA synthesis, may promote CENP-T/-W deposition. RNA Pol II transcription at centromeres is promoted by modification of centromeric H2B  (Sadeghi et al. 2014 ) and has been suggested to promote centromere organization  (Bergmann et al.
4B ). The ITC data indicate that the binding of the Spt16-CTD to the only site on CENP-T/-W has characteristics similar to the binding to the secondary site on H2A-H2B, as both are endothermic. Alternatively, the location of the binding site on CENP-T/-W could be similar to the primary binding site on H2A-H2B, but different amino acids on the binding interfaces of CENP-T/-W could contribute to the different thermodynamic properties of interactions. Given the higher affinity of Spt16 for H2A-H2B (∼15-fold higher), we speculate that free H2A-H2B may be a negative regulator of the FACT-CENP-T/-W complex. Therefore, the interactions between FACT and CENP-T/-W, which we detected by immunoprecipitation (Fig. 2 ; Supplemental Fig. S4 ), may occur in the presence of a limited amount of free H2A-H2B; for example, when soluble H2A-H2B is sequestered by a dedicated chaperone such as Nap1  (Mosammaparast et al.
5A ). Furthermore, we show that depletion of Spt16 impacts the de novo recruitment of CENP-T by LacO-tethered CENP-C (Fig. 6A ). Together, these data indicate that FACT is involved in regulating CENP-T/-W accumulation at centromeres downstream from CENP-C. Strikingly, we demonstrate that Spt16 alone tethered at a LacO locus promotes local de novo accumulation of CENP-T, supporting our conclusion that Spt16 and CENP-T can interact in vivo (Fig. 6B ). Furthermore, our structure-function analyses demonstrate that the region of the Spt16 CTD (amino acids 940-stop) that binds CENP-T/-W in vitro (Fig. 3B ) is necessary for CENP-T accumulation at GFP-LacI-Spt16 tethered at LacO (Fig. 6B ). This underscores the functional significance of the interaction between CENP-T/-W and Spt16 and further supports a role for Spt16 as a binding partner of CENP-T/-W.
Liquid chromatography-tandem MS (LC-MS/MS) analyses Samples were analyzed by nano-LC-MS/MS using an RSLCnano system (Ultimate 3000, Thermo Scientific) coupled to an Orbitrap Fusion mass spectrometer (Q-OT-qIT, Thermo Fisher Scientific). For details of the separation, mass spectrometer parameters, and data analysis, see the Supplemental Material. The MS proteomics data have been deposited to the ProteomeXchange Consortium via the Proteomics Identifications (PRIDE)  (Vizcaíno et al. 2016 ) partner repository with the data set identifier PXD004099.
ITC experiments All of the ITC titrations were performed on a Microcal ITC 200 calorimeter at 20°C. The exothermic heat of the reaction was measured by 17 sequential 2. 2-L injections of the Spt16-CTD (amino acids 926-965; 2. 2 mM in buffer, 20 mM Tris at pH 7. 5, 0. 2 M NaCl) into 200 µL of the CENP-T/-W, human H2A/H2B, and CENP S/X solution, respectively (150 µM in the same buffer), spaced at intervals of 150 sec. The data were processed with Microcal Origin software, the curve of CENP-T/-W was fit to a single-site binding model, and the curve of H2A/H2B was fitted to a two-site binding model.