dataesr/scientific-paragraphs-categorization huggingface dataset:
NMR analysis Before NMR analysis, 200 ml of deuterium oxide (D 2 O) phosphate buffer (pH 7) solution containing sodium trimethylsilyl propionate (TSP) was added to milk samples to avoid possible chemical shift drifts due to pH effects  (De Marco, 1977). Samples were then vortexed and centrifuged at 5,500 g for 10 min at 4 C and the supernatants were analyzed by 1H NMR spectroscopy. The supernatants (600 ml) of each sample were transferred into 5 mm NMR tubes. 1 H NMR spectra were acquired on a Bruker Avance III HD spectrometer (Bruker, GMBH, Karlsruhe, Germany) operating at 600. 13 MHz, equipped with an inverse detection 5 mm 1 H-13 C-15 N-31 P cryoprobe connected to a cryoplatform and a cooled SampleJet sample changer, using TopSpin 3. 2 software (Bruker, GMBH, Karlsruhe, Germany). The temperature was allowed to stabilize for 5 min after insertion into the magnet.
Data quality assessment Before analysis of milk samples, sensitivity, lineshape, and water suppression were checked using NMR sample reference tubes 0. 1% Ethylbenzene in CDCl 3 (Bruker, Z10121), 1% CHCl 3 in acetone-d 6 (Bruker, Z10250), and 2mM sucrose 10% D 2 O (Bruker, Z10268), respectively. After shimming optimization, values obtained corresponded to manufacturer's specifications. Temperature stability was checked with the 4% Methanol in methanol-d 4 NMR standard tube (Bruker, Z10128). The shim quality was appraised using the line width of TSP at 50% of its full height (0. 86 Hz), as it is recommended by  (Sumner et al. , 2007). The line width at half height of residual water was 2. 72 Hz indicating that the water suppression was within specification. The baseline was manually corrected, removing distortions and setting the corrected baseline near to the zero intensity level.
Data processing and statistical analysis The NMR spectra were imported in the Amix software (version 3. 9, Bruker, Rheinstetten, Germany) for data processing. A variable size bucketing was used based on graphical pattern (166 buckets), each integrated region was normalized to the TSP signal. Data were analyzed by chemometrics tools as unsupervised principle component analysis (PCA) and supervised orthogonal projections to latent structuresdiscriminant analysis (OPLS-DA)  (Trygg and Wold, 2002)  using SIMCA Pþ (V14, Umetrics AB, Umea, Sweden). PCA was first performed to look for trends, clusters between protocols or identify potential outliers. For the samples used for validation, based on both Hotelling and the distance to the model (DModx) plots, two samples from the treated group were outliers and then removed.
Metabolite annotation The annotation of metabolites was performed, in the first instance by comparing chemical shifts and coupling constants, with 1 H NMR spectra of reference compounds acquired under the same conditions. We further followed the annotation by comparing our data with published studies  (Sundekilde et al. , 2013a,b; Yang et al. , 2016; Li et al. , 2017; Klein et al. , 2010)  and on standards implemented in databases like the Human Metabolome Database  (Wishart et al. , 2007)  (http://www. hmdb. ca/metabolites) and the Biological Magnetic Resonance Data Bank  (Ulrich et al. , 2008)  (http://www. bmrb. wisc. edu/metabolomics/metabolomicsstandards. shtml). Identified chemical structures were confirmed using two-dimensional NMR experiments (2D J-Resolved 1H NMR spectroscopy, 2D 1 H-1 H Correlation Spectroscopy, 2D 1 H-13 C Heteronuclear Single Quantum Spectroscopy).
According to  Sumner et al. (2007) , metabolites are identified at the Metabolomics Standards Initiative (MSI) level 1 if two independent and orthogonal data are identical to those of a reference compound analyzed under the same conditions (for example 1 H NMR spectrum and 2D 1 H-13 C HSQC NMR spectra). If this information is not available the metabolite is putatively annotated from publications or databases (MSI level 2). The identification MSI level is given for each metabolite.
en edict Yanibada, C ecile Canlet: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Hamid Boudra, Diego P. Morgavi: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Laurent Debrauwer: Conceived and designed the experiments; Wrote the paper. C ecile Martin: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data. Funding statement B en edict Yanibada was supported by a CIFRE (Industrial Agreements of Training by Research) PhD studentship funded by the ANRT (National Association of Research and Technology). This study is part of a collaborative project led by INRA and funded by 11 institutes and private companies: Adisseo France SAS (Antony, France), Agrial (Caen, France), APIS-GENE (Paris, France), Deltavit (Janz e, France), DSM Nutritional Products AG (Kaiseraugst, Switzerland), Institut de l'Elevage (Paris, France), Lallemand (Blagnac, France), Moy Park Beef Orl eans (Fleury-les-Aubrais, France), Neovia (Saint Nolff, France), Techna France Nutrition (Cou€ eron, France), and Valorex (Combourtill e, France).
B en edict Yanibada, C ecile Canlet: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Hamid Boudra, Diego P. Morgavi: Conceived and designed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper. Laurent Debrauwer: Conceived and designed the experiments; Wrote the paper. C ecile Martin: Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data. Funding statement B en edict Yanibada was supported by a CIFRE (Industrial Agreements of Training by Research) PhD studentship funded by the ANRT (National Association of Research and Technology). This study is part of a collaborative project led by INRA and funded by 11 institutes and private companies: Adisseo France SAS (Antony, France), Agrial (Caen, France), APIS-GENE (Paris, France), Deltavit (Janz e, France), DSM Nutritional Products AG (Kaiseraugst, Switzerland), Institut de l'Elevage (Paris, France), Lallemand (Blagnac, France), Moy Park Beef Orl eans (Fleury-les-Aubrais, France), Neovia (Saint Nolff, France), Techna France Nutrition (Cou€ eron, France), and Valorex (Combourtill e, France).
Second, they were asked to complete the following story-"Joe, drinking his coffee, starts to think of the future. He thinks that" -and give the duration of the end they had imagined. 5) The degree of physical similarity of one's present self to the future self was determined by asking patients how they imagined they would look like physically, in comparison to today, in 1 month, 1 year, 5 years and 10 years. 16 6) Social deprivation was measured using the 11-item EPICES questionnaire, with scores. 30 indicating social deprivation. 17 Finally, depression was assessed with the 13-item (short-form) Beck Depression Inventory questionnaire, with scores greater than four indicating depression. 18 The complete questionnaire had 45 items and can be obtained from the main author by request. Questions asked to the patients to collect their demographic data and to address time projection are to be found in Figure  A1.
Demographical and psychological data In all, 120 patients were included in the study. Recruitment was limited only by refusal to participate or inability to read and write or understand the questionnaire, either due to a linguistic barrier or limited literacy, as is frequently met with in this hospital. The mean age of the patients was 58. 6±8. 9 years old and the mean diabetes duration was 14. 5±8. 9 years. A mean BMI of 29. 0±5. 4 kg/m 2 was found, and their mean HbA1c levels were 9. 8%±2. 4% (84±14. 5 mmol/mol). In all, 90. 8% of the patients had children and 47. 5% had grandchildren. Of the total, 44. 2% never smoked, 27. 5% were active smokers and 27. 5% had quit smoking. Table  1  presents the complete demographic description of the patients. Table  2  presents the results of the psychosocial questionnaires.
g. vehicles, industry, and agricultural practices. Several studies have concluded that the most significant effects on aerosol Fe solubility result from the source/composition of the aerosols, rather than changes in physicochemical parameters, such as temperature, pH, and oxygen concentration of the leach medium, or the choice of batch versus flow-through techniques (e. g. Aguilar-Islas et al. , 2010; Fishwick et al. , 2014). There have been a number of studies that have focused on the role of aerosol TEs on biogeochemical cycles in the North Atlantic (e. g. Sarthou et al. , 2003; Baker et al. , 2013; Buck et al. , 2010; Ussher et al. , 2013; Powell et al. , 2015). More recently, the GEOTRACES programme has produced a number of aerosol datasets, which has stimulated further discussion on the use of these data to look for trends that link TE solubility and aerosol source (e.
Some TEs fit into more than one of these categories. Here, we use the term "trace element" in the context of open ocean water column concentrations, thus acknowledging that elements such as Al, Fe, and Ti are not present in trace concentrations in aerosol source material. Aerosol concentrations for a suite of other elements (Li, Na, Mg, P, Sc, V, As, Se, Rb, Sr, Sn, Sb, Cs, Ba, La, Ce, Nd, Th, U) were also determined, but will not be discussed further here. However, these data are available at BCO-DMO (GA03; www. bco-dmo. org/, last access: 14 February 2018) and BODC (GA01; https://www. bodc. ac. uk/geotraces/data/inventories/geovide/, last access: 13 April 2018) and on request from the lead author. In this study a two-stage leach protocol was followed. the first leach employed was the "instantaneous" leach described by  Buck et al.
, 2013 (Wozniak et al. , , 2014;; Shelley et al. , 2015 Shelley et al. , , 2017)). Briefly, air was simultaneously pulled through 12 acid-washed 47 mm diameter Whatman 41 (W41) ashless filter discs at approximately 1. 2 m 3 min -1 (134 cm s -1 face velocity) using a highvolume aerosol sampler (TSP model 5170V-BL, Tisch Environmental). The metadata and concentration data for the aerosol leaches can be found in the Supplement (Table  S1 ). All filters were stored frozen (-20 • C) and double bagged prior to processing, both on the ship and upon returning to the home laboratories. To avoid contamination from the ship's stack exhaust, aerosol sampling was controlled with respect to wind sector and wind speed using an anemometer interfaced with a data logger (CR800, Campbell Scientific). The samplers were programmed to run when the wind was ±60 • from the bow of the ship and > 0.
2 ). However, this correlation was largely driven by the heavily loaded North African samples (r 2 = 0. 997, P < 0. 01). For each of the other source categories, simple linear regression of the data resulted in r 2 values of 0. 879 (P < 0. 01) for High Latitude dust (GA01), 0. 890 (P = 0. 057) for European samples (GA03), 0. 983 (P = 0. 34) for North American samples (GA03), and 0. 751 (P = 0. 70) for Marine samples (GA03) (Fig. 2b ). Further discussion of sub-regional differences in the Fe / Al ratio are addressed later in the Dis-   S1 ) are not included so as not to obscure the symbols. cussion. For the other TEs, strong correlations for the combined GA01 and GA03 datasets were found between Ti / Al (r 2 = 0. 999, P < 0. 01), Mn / Al (r 2 = 0. 994, P < 0. 01), and Co / Al (r 2 = 0. 996, P < 0.
As the aerosol source has a direct bearing on the type and composition of aerosols, determining the source could provide useful data that might be used to predict the fractional solubility of aerosol TEs. As positive matrix factorisation (PMF) can be used for source apportionment, we used the US Environmental Protection Agency's EPA PMF model (v. 5. 0) with the total TE concentration data to look for trends in the data. However, the GA01 and GA03 dataset is relatively small (n = 57) and the model was not stable with more than two factors. The two factors were a mineral dust factor (high contributions from lithogenic TEs, in particular Al, Ti, Fe, and Zr) and a pollution/anthropogenic factor (with high contributions from Zn and Pb) (Fig. S2a  in the Supplement). As anticipated, the mineral dust factor dominated where North African aerosols were sampled, and the pollution factor was relatively more important closer to the European and North American continents (Fig.
, 2009 (Sholkovitz et al. , , 2012;; Jickells et al. , 2016)  and Al  (Jickells et al. , 2016). Jickells et al. (2016)  compiled solubility data from the North Atlantic and found that the general trend between Fe and Al solubility and atmospheric loading was robust over the range of atmospheric loadings found in the North Atlantic, regardless of the leach protocol employed. In this study, both the UHP soluble and 25 % acetic acid soluble fractions of Fe and Al (Fig. 4a  and b ) were re- lated to atmospheric loading, i. e. the highest loaded North African samples had the lowest solubility. The possible exception to this trend is the fraction of Al that dissolved from North African aerosols following the 25 % acetic acid leach (Fig. 4b ). However, it could simply be that we are observing scatter in our data that is smoothed out in the larger dataset (n > 2000) examined by  Jickells et al.
Manganese is the only TE that had a slope close to unity (0. 98; Fig. 6b ), suggesting that solubility estimates were not impacted by the choice of leach medium used. This is consistent with other studies that have found that Mn solubility is less sensitive to the choice of leach media or to aerosol provenance than other TEs  (Baker et al. , 2006b; Jickells et al. , 2016). Due to the large variability in the dataset, there was no significant difference between Mn solubility in UHP water or seawater (32 ± 13 and 24 ± 17 %, respectively; Fig. S3  and Tables  S3  and S4  in the Supplement). Table  S5  shows which regions had slopes for UHP water versus seawater fractional solubility that did not differ significantly from 1. 0 at the 95 % confidence level (t statistic). Lead was the only TE with all slopes differing significantly from 1.
Acknowledgements. Many thanks to the captains and crews of the  R/V Knorr  ( GA03-2010  and  2011 ) and N/O Pourquoi Pas? (GA01); the chief scientists (GA03:  Bob Anderson ,  Ed Boyle , and  Greg Cutter ;  GA01 :  Geraldine Sarthou  and  Pascale Lherminier );  Alex Baker  for the loan of the aerosol sampler used on GA01; and  Alina Ebling Petroc Shelley ,  Alex Landing , and  Sarah Huff  for their help with sample processing and analysis. This work was supported by grants to WML ( NSF-OCE 0752832 ,  0929919 , and  1132766 ) and  GS  ( ANR-13-B506-0014  and  ANR-12-PDOC-0025-01 ). RUS was supported by a  LabexMER International Postdoctoral Fellowship  and  CG29 Postdoctoral Fellowship. A portion of this work was performed at the  National High Magnetic Field Laboratory , which is supported by  National Science Foundation  Cooperative Agreement No.
All chemical analyses were done by inductively coupled plasma optical emission spectroscopy (ICP-OES). Beam bending viscometry was conducted on LSX-c glass using samples with dimensions of 47 × 4 × 3 mm 3 , at a heating rate of 10 K min -1 in two separate scans at loads of 20 and 400 g (Figure  S2 , Supporting Information). Structural Characterization XRD: High-resolution powder synchrotron radiation diffraction was performed on the zeolite LSX, the reference glass, and on the collapsed HDA zeolite at DESY. The radiation wavelength (0. 02080 nm) was calibrated from a CeO 2 powder standard in capillaries and slab-shaped samples. The 2D scattering patterns were integrated to functions of scattering angle (2Θ) and corrected for container scattering, absorption, and background. [41] Intensities I corr (Q) were normalized according to the sample's chemical compositions using tabulated data  [42, 43]  of the atomic coherent and Compton scattering.
The X-ray structure factor S(Q) was calculated as S Q I Q N f Q I f Q ( ) ( ) ( ) ( ) corr 2 Compton 2 = ⋅ - - (3) where 〈…〉 indicates the average sample composition, f(Q) is the coherent atomic scattering amplitude, and N the normalization factor. The mass density of the melt-quenched glass was 2. 49 g cm  -3. By reference to a similar LSX20 crystal (ICSD 85621  [44]  ) we estimated 8 H 2 O per formula unit for the zeolite, based upon its mass density of 2. 06 g cm  -3. XRD Collapse Measurements: In situ collapse studies of LSX samples were performed in air using a Bruker D8 diffractometer equipped with an Anton Paar HTK1200 heating stage and a Vantec position-sensitive detector that enables rapid data collection. A collimated spectrally pure Cu Kα X-ray beam was produced using an incident beam Goebel mirror, Adv.
Sci. 2018, 5, 1700850 www. advancedscience. com enabling low backgrounds to below 3° 2Θ. Samples were mounted as thin layers of powder onto nonreactive sapphire crystal substrates. Time-temperature profiles for the stepwise measurement of reaction sequences included heating at 120 K min -1 to the target temperature of 725 °C, followed by subsequent ramping to higher temperatures at 20 K min -1. For the kinetic studies, a ramp rate of 120 K min -1 was used to the target temperature, at which point back-to-back XRD measurements were performed using time intervals from 15 to 120 s per XRD pattern. Data analysis was performed using Bruker's software Diffrac. EVA and Topas. Fluorescence Spectroscopy: Room-temperature fluorescence spectroscopy was employed to examine local structural symmetry. Static photoluminescence excitation (PLE) and emission spectra (PL) were recorded on compressed powders of 5 mm in diameter (produced by uniaxial compaction at 1 GPa), using a high-resolution spectrofluorometer (Fluorolog 3, Horiba) with a continuous-wave 450 W Xe lamp as excitation source, and a Hamamatsu R2658P photomultiplier tube (PMT) for detection.
Acknowledgements This project received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation program  (L. W. ,  ERC  grant  UTOPES , Grant Agreement No. 681652 ). S. T. M. was supported via an  Inamori Professorship. G. N. G. acknowledges a fellowship at  Sidney Sussex College, Cambridge. In situ XRD beamtime on BL P02. 1 was conducted on PETRA III at DESY and IXS beamtime on ID16 at the  ESRF. L. W. conceived of the experiments and wrote the manuscript together with G. N. G. T. P. and S. T. M. conducted laboratory X-ray diffractometric studies. Z. P. , A. E. , T. P. , and M. S. performed computational experiments, to which M. M. , together with Z. P. and L. W. provided further input on structural dynamics. J. D. conducted experimental viscometry.
) with sterile glass beads. Plates were incubated at 20 • C for 10 d. The resulting colonies were picked at random, placed into 200 µL LB broth, and incubated for 48 h. Samples were then centrifuged, the supernatant was removed, and the pellet was resuspended in a mix of LB broth and 17% glycerol before being frozen at -80 • C. Isolates were assigned taxonomy using 16S PCR followed by Sanger sequencing within the 16S rRNA gene (SI Appendix, section 1. 1). Using Mothur v. 1. 39. 5  (36) , sequences longer than 974 bp were aligned to the Silva. Bacteria. Fasta database, and taxonomy was clas-sified using the Ribosomal Database Project (RDP) trainset 9 032012 and NCBI as a reference database (SI Appendix, Fig. S5  and Table  S6 ). Phylogenetic trees were constructed using iToL (itol. embl. de/) (37).
Biomass was estimated by measuring optical density at 600 nm. Growth rates were derived by fitting the logistic growth equation to the biomass time series. Thermal tolerance curves were quantified by fitting the Sharpe-Schoolfield equation  (39)  to the growth rate data using the methods outlined in Padfield et al. (40)  (SI Appendix, section 1. 2). Biodiversity Ecosystem Functioning Experiment. We assembled the 24 taxa into artificial communities with different levels of species richness  (2, 4, 8, 16, and 24). For each richness level we built 10 different replicate communities where the species composition was determined by randomly sampling the 24 species. Each community was then grown at eight different temperatures (10  C, and 40 • C). Stock cultures of all 24 taxa were first grown to stationary phase at each of the experimental temperature treatments and then diluted back to a common biomass density across all taxa and treatments.
4). Effects of Temperature on the Community Composition. To determine whether the composition of the communities present at the end of the experiment differed significantly between the temperature treatments (10 • C, 20 • C, and 40 • C) we performed a NMDS ordination analysis on the relative abundance of the reisolated taxa. Analyses were performed using the R package "vegan"  (41). The analysis was based on a Bray-Curtis dissimilarity matrix derived from the square-root-transformed relative abundances data. NMDS projected this matrix into a new coordinate space with a small number of dimensions (in this case, four) while preserving the original Bray-Curtis dissimilarities among samples to the extent possible. Orthogonal rotation was applied to the axes in this new coordinate space to maximize the variance in "scores" among samples along the first NMDS axis.
We performed a permutational multivariate analysis of variance (PERMANOVA) to test whether there were significant differences in community composition between the different temperature treatments, using the "adonis" function from the R package vegan (SI Appendix, Table  S4 ). We then ran separate PERMANOVA analyses for each pair of treatments (e. g. ,  10-20, 10-40, 20-40)  to determine which pairwise treatment contrasts were significantly different (SI Appendix, Table  S3 ). We used the Bonferroni correction to adjust the resultant P values for multiple comparisons. Linking Thermal Traits to the Impacts of Warming and Species Loss on Ecosystem Functioning. To explore the coupling between biodiversity, thermal tolerance traits, temperature variation, and ecosystem productivity, we quantified the community-mean thermal optima <Topt> c of the taxa inoculated into each community (at each level of temperature and richness) at the start of the diversity-functioning experiment.
Silencing FGFR1 or FOXM1 downregulated genes involved in mesenchymal transition such as GLI2, TWIST1, and ZEB1 in glioblastoma stem-like cells. It also dramatically reduced GSLC migration. Databases analysis confirmed that the combined expression of FGFR1/FOXM1/MELK/GLI2/ZEB1/TWIST1 is significantly associated with patients overall survival after chemo-radiotherapy treatment. All these results, associated with our previous conduced ones with differentiated cells, clearly established that FGFR1-FOXM1 dependent glioblastoma stem-like cells radioresistance pathway is a central actor of GBM treatment resistance and a key target to inhibit in the aim to increase the sensitivity of GBM to the radiotherapy. www. oncotarget. com.
To evaluate whether FGFR1 inhibition may activate radiation-induced cell death, we quantified subG1 fraction in a cytometry analysis. SubG1 level was increased in GC1FGFR1(-) and GC2FGFR1(-) compared to control cells by 61% and 75%, respectively (Figure  2C  and Figure  2D ) strongly suggesting that FGFR1 silencing increased glioblastoma stem-like cell death induced by radiation. These data showed that FGFR1 regulates GSLC radiosensitivity.