dataesr/scientific-paragraphs-categorization huggingface dataset:
AtSRP1 Overexpression in Leaves Induces the Formation of New LDs Organized in Clusters in Contact or in Continuity with the ER To gain insight into the LD ultrastructure, we performed transmission electron microscopy (TEM) after cryo-fixation of AtSRP1 overexpressing Arabidopsis leaves. This analysis showed an accumulation of round-shaped cytosolic aggregates, which were never observed in wild-type leaves (Figure  5A2 ). To confirm the nature of these clusters, immunogold labeling with an antibody specific to the GFP/YFP protein was performed on cryo-fixed roots of a YFP-AtSRP1 overexpressing line. Labeling was restricted to these cytosolic aggregates (Figure  S7 ), while no such specific labeling was observed in control experiments where anti-GFP/YFP antibody was omitted. Therefore, our data indicate that the YFP-AtSRP1 protein is specifically associated with these aggregates.
ACKNOWLEDGMENTS Authors would like to acknowledge  L. Combes-Soia  and  A-M. Lomenech  for technical help in lipidomics and proteomics respectively,  Dr. C. Garcion  for VBA programming skills,  Dr. P. Legros  for lending his confocal microscope,  Dr. M. Bonneu  for his expertise in proteomic data analysis,  Dr. M Blatt  and  Dr. T. Nakagawa  for sharing publish material, and members of the laboratory of membrane biogenesis for their supportive encouragements, especially  Drs. A. Bernard ,  J. Joubes ,  S. Mongrand  and  E. M. Bayer  for critical reading of the manuscript, and  A. Chevin  for fund management. The lipid analyses were performed at the  Metabolome Facility of Bordeaux-MetaboHUB  ( ANR-11-INBS-0010 ). The imagery experiments were achieved on the Bordeaux Imaging center, a member of the  France Bioimaging Infrastructure.
, 2012). Additionally, we calculated end-member age estimates by assuming 1) instantaneous deposition of the entire loess layer immediately after terrace abandonment (maximum age), and 2) very recent deposition of the entire loess layer, meaning that this did not affect the cosmogenic depth profile in the underlying alluvium (minimum age; attenuation in the loess is ignored in such a case). To derive the exposure ages from the depth profiles, we followed a Monte Carlo inversion procedure that tests thousands of parameter combinations to find the best fitting solution by minimizing the difference between the model and the data (e. g. Braucher et al. , 2009; Saint-Carlier et al. , 2016). The local 10 Be production rates for neutrons, fast muons and slow muons were scaled for local latitude and altitude according to  Stone (2000) , and the local atmospheric pressures were extracted from the ERA40 dataset  (Uppala et al.
Thus, in our model, the evolution of a given reverse fault scarp depends on five parameters, including 1) the coseismic slip increment, 2) the fault dip, 3) the initial uniform slope of the terrace surface, 4) the time of its abandonment, and 5) the diffusion coefficient. The initial slope of the faulted surface can be estimated from the far field profile elevation across the fault. Based on paleoseismic evidence from active faults further west in Kyrgyztan  (Thompson et al, 2002) , we considered series of 0. 5 to 2. 5m slips that were generated on a single fault plane dipping at 35+/-5°. Though the total deformation rate is greater in this region, the range of slip values considered is large enough to correspond to the eastern part as well. Thus, if either the time of abandonment or the diffusion coefficient is independently known, the other can be determined by adjusting the model to the elevation data using a least square procedure.
The discs were washed with MS grade water (Sigma-Aldrich, Zwijndrecht, NL) three times to remove additional salts and then air-dried. The discs were then covered with a 2 nm gold layer using a sputter coater (Quorums Technologies SC7640, Newhaven, UK) equipped with a FT7607 quartz crystal microbalance stage and a FT7690 film thickness monitor. For LCMS/MS analysis, a Bligh and Dyer extract was prepared from the wing discs prior to analysis. 15 icroscopy. Discs were imaged with a Leica DMRX (Leica, Wetzlar, Germany) microscope equipped with an OSRAM HBO 50W/L2 short arc mercury lamp (Osram AG, Munchen, Germany) at 10× magnification. Data Acquisition. ToF-SIMS. Secondary ion mass spectrometry was performed using a Physical Electronics (PHI) TRIple focusing ToF ion (TRIFT-II) instrument equipped with a gold liquid metal ion gun (Physical Electronics, Chanhassan, MN, USA).
Then, 22 keV Au + primary ions were "micro" focused on the sample surface. The total primary ion dose density amount was kept well below the static limit. Positive or negative secondary ions were extracted to the mass analyzer with a static voltage of ±3. 5 kV and postaccelerated in front of the ion detector (dual-stage microchannel plate) by an additional 10 kV. The signal from ions in the m/z range 1-1500 m/z was recorded. Full wing discs were imaged step-bystep in a mosaic formed by 8 × 8 individual tiles between which the stage moved. 16, 17 Each tile of about 80-95 μm in width was probed by the primary ion beam in a 256 × 256 pixel raster for a duration of 30 s to ensure that the total ion dose was below the static limit of SIMS. The size of each pixel was approximately 0. 35 μm. The resulting image was saved as a RAW file for further data processing.
■ RESULTS Third instar wing imaginal discs were analyzed with ToF-SIMS imaging mass spectrometry in both negative and positive ion mode. Principal component analysis (PCA) was used to unravel the complexity of the ToF-SIMS data sets. Inspection of the principal components (PC) from the negative ion mode measurements revealed four PCs reflecting molecular distributions (Figure  1 , top row), which colocalized with known genetically predicted tissue subtypes such as anterior vs posterior compartmentalization (A/P; Figure  1A ) and dorsal from ventral tissue (D/V; Figure  1B ). Additionally, patterns reminiscent of the pouch/nonpouch (Figure  1C ) and the body wall/wing blade (B/W; Figure  1D ) differentiation were observed. 9] [20] [21] [22]  Positive ion mode experiments repeatedly confirmed the B/W distribution, and it was therefore chosen to follow up (Figure  1E  and Figure  S1 ).
Using CARS, we acquired the vibrational signatures of the molecules (in the frequency range corresponding to Raman shifts from -3200 cm -1 to -900 cm -1 ) from late third instar wing imaginal discs. In the fingerprinting region (-1800 cm -1 to -900 cm -1 ), the chemical moieties giving rise to specific peaks are largely known and can be found in the literature. 27 Multivariate statistical analysis (PCA) was applied to screen for patterns in the CARS data set, similar to that performed for the ToF-SIMS data sets. A clear B/W pattern similar to the one found by ToF-SIMS (Figure  3A, B,  and C ) is observed. No other CARS PCA pattern was observed that resembled any of the previously described patterns. Canonical correlation analysis (CCA) revealed a positive correlation score between the CARS and ToF-SIMS data sets for the B/W patterns (Figure  3C  and Supporting Information Table  1 ).
However, we cannot exclude that the MAG and DAG related peaks in the SIMS experiments are fragments from TAGs present on the cellular surface. The analysis conditions were optimized to minimize the fragmentation of intact phospholipids and TAGs. To confirm this identification of the ToF-SIMS peaks, we performed orthogonal validation using high-performance liquid chromatography and electrospray ionization tandem mass spectrometry (HPLC-ESI-MS/MS, hereafter referred to as MS/MS for simplicity) of lipid extracts of whole wing imaginal disc. 28 This data set was utilized as a high-mass-accuracy MS reference data set for tissue-specific lipids. We identified 156 lipid species (for details, see Materials and Methods section) containing most of the major lipid classes previously  assigned. 12, 13 Of the 156 lipid species, five were MAGs, 22 DAGs, and 10 TAGs.
Management of women with OAB OAB is frequently unrecognized in women Data collected in Belgium from women aged 40 years or over visiting their general practitioner for any reason who answered the Bladder Control Self-Assessment Questionnaire (B-SAQ) revealed that 18% of them were affected by moderate-severe SUI and a third had mild bladder-control symptoms. 8 In this study, urgency and nocturia were found to be the most bothersome (higher score of bother on the B-SAQ) symptoms. In another study,  9  women from the Finnish population register were contacted to assess the bother associated with 12 different lower-urinary-tract symptoms (LUTS). From an individual perspective, UUI was found to be the symptom most likely to be rated as bothersome by patients and SUI was second. Women and men with OAB symptoms report that OAB affects their health-related QL (HRQL): they have significantly poorer work productivity, sexual satisfaction, higher rates of major depressive symptoms, and less physical activity than controls.
The effects of flexible-dose fesoterodine were tested in a double-blind, placebo-controlled Vulnerable Elderly Study. 44 Vulnerable elderly subjects were defined as having a risk of deteriorating health with a score ≥3 on the Vulnerable Elders Survey (VES) 13 and a score ≥20 on the Mini-Mental State Examination (MMSE), indicating possible mild cognitive impairment (MMSE maximum score 30, scores ≥27 correspond to normal cognition, 20-26 indicate some cognitive impairment). In both the placebo and fesotero- Notes: Data from Chancellor and Boone,  31  Cetinel and Onal,  33  Kerdraon et al,  38.
The EndoC-βH1 and -βH2 cells were generated from human fetal pancreatic bud and express numerous β-cell markers. These human β-cell lines respond to elevated glucose with stimulation of insulin secretion  (7, 8)  and are increasingly used to explore various aspects of human β-cell biology  (9) (10) (11) (12) (13) (14) (15) (16) (17) (18) (19) (20). Here, we monitored different parameters that constitute the triggering pathway of the GSIS  (1, 21) , the electrophysiological and ultrastructural properties of EndoC-βH1 and -βH2 cells. We correlate our functional findings to transcript levels determined by RNA sequencing. Overall, our data show consistency between the EndoC-βH1 and -βH2 cells and primary human β-cells.
RNA sequencing RNA was extracted from EndoC-βH1 and EndoC-βH2 cell lines using TRIzol and sequenced at the Oxford Genomics Centre (Wellcome Centre for Human Genetics, University of Oxford)  (24, 25). These data are deposited at the European Nucleotide Archive (https://www. ebi. ac. uk/ena) under the accession number PRJEB23293 (the transcriptome analysis can also be found in Supplementary table  1 ).
These discussions highlight the importance of defining preindustrial consistently and more precisely. In AR5, the observed increase in global temperature was calculated as the mean of 1986-2005 minus the mean of 1850-1900 in the HadCRUT4 dataset (0. 61°C;  Morice et al. 2012) , which was the only combined global land and ocean temperature dataset available back to 1850 at the time. The 1986-2005 modern period was chosen 1  because the design of the CMIP5 simulations required a recent reference baseline for the projections of future climate [discussed further in  Hawkins and Sutton (2016) ]. Note that the warming between 1850-1900 and the most recent decade covered  was given by AR5 as 0. 78° ± 0. 03°C  (IPCC 2013). The choice of 1850-1900 as the historical reference period benefits from relatively widespread, but still sparse, temperature observations, and quantified uncertainties in the estimates of global temperature.
Since the AR5, two further datasets have been produced that allow a comparison for the 1850-1900 period. In the  Cowtan and Way (2014, hereafter CW14)  dataset, which is based on interpolating the spatial gaps in HadCRUT4, the difference from 1850-1900 to 1986-2005 is 0. 65°C and in the Berkeley Earth global land and sea data (BEST-GL; berkeleyearth. org), it is 0. 71°C, suggesting that the AR5 value may be slightly too low. 2 Also,  Cowtan et al. (2015)  presented GCM-based evidence that sparse observation-based datasets may have significantly underestimated the changes in global surface air temperature due to slower warming regions being preferentially sampled in the past. However, infilling the gaps in the early period is especially problematic owing to the sparse observations and may accentuate the dominant observed anomaly.
Annual means are used everywhere. We also use an ENSO index (E, scaled by γ) as a "forcing" to remove the effects of the leading mode of interannual variability from the observations. This E index is defined as the linearly detrended Niño-3. 4 anomaly from 1857 to 2015  (Kaplan et al. 1998 ) and zero before 1857, with a lag (τ) of 4 months to maximize the variance explained (i. e. , the annual mean is a September to August average). A similar approach to fitting global temperatures was taken by  Lean and Rind (2009)  and  Suckling et al. (2016). All global temperature data are referenced to 1986-2005 to match the analysis in IPCC AR5  (Kirtman et al. 2013)  and β is a constant offset to account for this reference period. We perform the analysis separately for five global temperature datasets to represent the uncertainty in temperature reconstructions, although this is an underestimate of the true uncertainty because they are all based on similar observations.
For HadCRUT4, BEST-GL, and CW14, the multiple linear regression is performed over the period 1850-2015. The NOAA GlobalTemp  (Karl et al. 2015)  and NASA Goddard Institute for Space Studies (GISS) Surface Temperature Analysis (GISTEMP)  (Hansen et al. 2010)  datasets are fitted over the full extent of their available data. We use the HadCRUT4 uncertainties in the weighted regression (except for BEST-GL and NOAA GlobalTemp, which have their own uncertainty estimates), so that the older (and more uncertain) data have less weight. Figure  2  (top) shows one estimate of GMST (HadC RU T4) a nd t he scaled forcings for the full 1765-2015 period, using the regression parameters derived over 1850-2015. The correlation between the scaled forcings (including ENSO) and observed temperatures is 0. 94 for each of the global datasets.
There are two ways to estimate a change in temperature using this approach. 3 First, we can average the scaled forcings over 1765-1800 to produce an estimate of the preindustrial global temperature for each dataset with associated uncertainties, accounting for the covariance in derived α f 's. Note that this is the longest period available using the CMIP5 forcings in the 1720-1800 period. The Paleoclimate Modeling Intercomparison Project (PMIP) protocol does not currently provide consistent forcing estimates in this way for the 850-1850 period  (Schmidt et al. 2012). For the five temperature datasets, the best estimates are found to range from 0. 64° to 0. 76°C with uncertainties of around ±0. 05°C. Alternatively, the value of the regression constant (β) is an estimate of the temperature change from a state of zero forcing (in this case 1750) to 1986-2005.
For the five temperature datasets, β ranges from 0. 69° to 0. 82°C (with uncertainties of ±0. 02°C), which is around 0. 06°C larger than using the 1765-1800 average. This difference is consistent with the small increase in greenhouse gas forcing and the relatively weak volcanic forcing after 1765. Overall, these results suggest that preindustrial was slightly cooler than the 1850-1900 period. Also, the derived estimates for the warming are all larger than the value used in IPCC AR5 (0. 61°C), with the HadCRUT4-based estimates being the smallest and GISTEMP the largest. The differences between estimates from the various datasets are larger than the stated uncertainties and are dominated by the uncertainty in global change since 1850, partly related to the way missing data are treated. The CW14 dataset, which interpolates between the gaps in HadCRUT4, finds slightly larger warming, consistent with  Cowtan et al.
(2015)  who show a similar effect when examining simulated data to determine the effects of incomplete spatial coverage. The NOAA and GISTEMP datasets also use slightly different interpolation techniques. These various infilling approaches may reduce the bias from poor spatial sampling, especially for fast warming regions such as the Arctic, but may simply accentuate the dominant anomaly and add uncertainty. These inconsistencies merit further investigation elsewhere. This approach does not account for nonlinearities in the temperature response to forcings, or uncertainties in the assumed CMIP5 forcing history itself, which are likely to be particularly large for aerosols 3 These estimates are largely insensitive to whether a lag is introduced in the greenhouse gas forcing [as done in  Lean and Rind (2009) ], or if only the 1900-2015 period is used for fitting, or if the anthropogenic forcings are combined before fitting.
APPROACH 3: USING LONG INSTRU-MENTAL RECORDS. The above two approaches have considered the response to estimated radiative forcings. An alternative approach to estimate GMST further back in time is to use direct observations from long instrumental records and calibrate them against each of the five global mean temperature datasets. For example, central England temperature (HadCET, herein referred to as CET;  Manley 1974; Parker et al. 1992 ) is available for 1659-present. CET covers just 0. 005% of Earth's surface but is highly correlated with GMST on multidecadal time scales  (Sutton et al. 2015). Here, we utilize this correlation and scale GMST to CET: CET = δGMST + ε, ( 2 ) using the overlapping periods (1850-2015), and adopt the same parameters to scale CET back to 1659 as an estimate of GMST (Fig.
64° ± 0. 08°C (for 1720-1800) when using HadCRUT4 for GMST. The other global temperature datasets give larger values for the warming to 1986-2005, by up to 0. 09°C (Fig. 3 , top). The quoted uncertainty ranges account for the uncertainties in the regression parameters and assume the uncertainty in each CET annual mean from 1720 to 1800 is independent and equal to 0. 2°C [based on  Parker (2010) ]. The difference between the two averaging periods is mainly because the 1720s and 1730s were unusually warm in the CET record. Internal climate variability and a recovery from the negative forcings of the previous decades are possible explanations, although this warmth was less pronounced in some other European instrumental records (e. g. , Berlin)  (Jones and Briffa 2006). Figure  3  (bottom) repeats this analysis with the Berkeley global land temperature (BEST-Land;  Rohde et al.
2013) , which starts in 1753. A similar approach was adopted by  Mann (2014). Using BEST-Land produces a consistent but slightly lower warming than derived with CET. Using the scaled temperatures over the 1753-1800 period, the estimates of the warming to 1986-2005 range from 0. 62° ± 0. 10°C for HadCRUT4 to 0. 71° ± 0. 12°C for GISTEMP. It may seem surprising that the error bars are not smaller for the BEST-Land dataset than for CET. The regression uncertainty is indeed much larger for the local example; however, the error in representing the whole global land area with sparse data are larger than in representing central England with a small number of stations. These two sources of uncertainty combine to give similar overall ranges. Note that BEST-Land looks very similar to the long European records and the variability increases further back in time (also for CET), highlighting that fewer and fewer (mostly European) stations are used in the reconstruction.
Furthermore, the estimates using radiative forcings are generally larger than when using the observational datasets, as summarized in Fig. 4. Much of the uncertainty in the assessment derives from the range of global temperature change estimates available since 1850. For example, the uninterpolated HadCRUT4 dataset produces lower values than the other infilled records. Our overall assessment is that the change in global average temperature from preindustrial to 1986-2005 is "likely" between 0. 55° and 0. 80°C. This range ref lects the authors' aggregated assessment of the three approaches and contains virtually all of the best estimates using the various combinations of regional and global temperature datasets and scaled radiative forcing estimates. Note that there are potentially important uncertainties in each approach that we cannot quantify.
As in IPCC AR5 we consider that likely refers to greater than 66% probability, although this is not a formal uncertainty quantification. It is also helpful to assess a lower bound and we suggest that the warming from preindustrial until the 1986-2005 period is likely greater than 0. 60°C, implying that the value used by IPCC AR5 for the warming since 1850-1900 (0. 61°C) was probably smaller than the true change since preindustrial. Such differences matter more when considering the chance of crossing lower temperature levels such as 1. 5°C than when considering higher values. Using this lower bound, 2015 was the first year to be more than 1°C above preindustrial levels in each global temperature dataset (Fig. 5 ). The year 2016 was warmer than 2015, but future years could still be cooler than 2015 owing to internal variability, such as a La Niña event.
This is because the global observations are a sparse blend of sea surface temperatures over the ocean and air temperatures over the land, whereas virtually all analyses of GCM simulations use air temperatures with complete global coverage. Cowtan et al. (2015)  and  Richardson et al. (2016)  used GCM simulations to suggest that if we had complete coverage of air temperature, the observed change from 1850 to present would be 24% ± 15% larger than currently estimated in HadCRUT4. The use of infilled temperature datasets only partly overcomes this issue. This creates a dilemma-are the temperature limits adopted by the UNFCCC designed to use observationally based estimates of global temperature change (as generally used here) or on what those observations mean for a "true" global mean air temperature change (as used in most climate impact assessments)? The available evidence suggests that the latter is larger.
We thank John Fasullo and Johann Jungclaus for providing the CESM1 LME and MPI-ESM data, respectively. EH is funded by the U. K. China as part of the Newton Fund. AS and GH are supported by the ERC funded project TITAN (EC-320691). GH was further supported by NCAS and the Wolfson Foundation and the Royal Society as a Royal Society Wolfson Research Merit Award (WM130060) holder. VMD acknowledges support from Agence Nationale de la Recherche, project ANR-15-.
We thank John Fasullo and Johann Jungclaus for providing the CESM1 LME and MPI-ESM data, respectively. EH is funded by the U. K. China as part of the Newton Fund. AS and GH are supported by the ERC funded project TITAN (EC-320691). GH was further supported by NCAS and the Wolfson Foundation and the Royal Society as a Royal Society Wolfson Research Merit Award (WM130060) holder. VMD acknowledges support from Agence Nationale de la Recherche, project ANR-15- National Centre for Atmospheric Science (NCAS) and a Natural Environment Research Council (NERC) fellow- ship (Grant NE/I020792/1). TJO and GH were supported by the NERC (Grant NE/N006348/1, SMURPHS). ES was supported by the European Union Seventh Framework Programme (FP7/2007-2013) under the SPECS project (Grant Agreement 308378) and by the UK-China Research and Innovation Partnership Fund through the Met Office Climate Science for Service Partnership (CSSP) ACKNOWLEDGMENTS.