dataesr/scientific-paragraphs-categorization huggingface dataset:
The false discovery rate (FDR) was set to 0. 01 for both peptides and proteins. The MS/MS spectra of the identified peptides were then used to generate the spectral library for SWATH peak extraction using the add-in for PeakView Software (version 2. 2, Sciex) MS/MSALL with SWATH Acquisition MicroApp (version 2. 0, Sciex). Peptides with a confidence score above 99% were obtained from Protein Pilot database searches were included in the spectral library. Relative quantification by SWATH acquisition. MCF-7 cells were analyzed using a Data Independent Acquisition method  (33). Each sample (5 µg) was analyzed using the LC-MS equipment and LC gradient described in the previous section following a SWATH-MS acquisition method. The method involved repeating the whole gradient cycle, which consisted of the acquisition of 35 TOF MS/MS scans of overlapping sequential precursor isolation windows (25 m/z isolation width, 1 m/z overlap, high sensitivity mode) covering the 400 to 1,250 m/z mass range, with a previous MS scan for each cycle.
Data analysis. The targeted data extraction of the SWATH runs was performed by PeakView using the MS/MSALL with SWATH Acquisition MicroApp. PeakView processed the data using the spectral library created from the shotgun data. Up to ten peptides per protein and seven fragments per peptide were selected, based on signal intensity; any shared and modified peptides were excluded from the extraction. The retention times from the peptides selected for each protein were realigned in each run according to the iRT peptides (Biognosys AG) spiked in each sample and eluting along the whole-time axis; the extracted ion chromatograms were generated for each selected fragment ion. PeakView computed a score and FDR for each assigned peptide using chromatographic and spectra components; only peptides with an FDR <5% were used for protein quantitation.
The peak areas for peptides were obtained by summing the peak areas of the corresponding fragment ions; protein quantitation was calculated by summing the peak areas of the corresponding peptides. MarkerView (version 1. 2; Sciex) was used for signal normalization and differential abundance was tested by applying a t-test at protein level. GSEA Analysis. GSEA analysis was performed using GSEA software from Broad Institute (https://www. gsea-msigdb. org/gsea), the raw data tables were uploaded on the software. The data bases used are Hallmarks (h. all. v. 7. 0. symbol), Oncogenic signatures (C6. all. v. 7. symbol) and Senescence signature (C2. CP. REACTOME. REACTOME_OXIDATIVE_ STRESS_INDUCED_SENESCENCE). Data deposition. The mass spectrometry proteomics data have been deposited to the ProteomeXchange Consortium via the PRIDE (http://www.
proteomexchange. org/) (34) partner repository with the dataset identifier PXD014194 for breast cancer analysis and PXD028073 for emergent MCF7 analysis. Statistical analysis. All data were expressed as mean ± standard deviation. Differences were analyzed using nonparametric tests (Mann-Whitney, Kolmogorov-Smirnov and one-way ANOVA with Dunnett's multiple comparison test). Pearson's correlation test was performed to assess the correlation between protein expression in breast cancer tumors. P<0. 05 was considered to indicate a statistically significant difference.
(A) The AGR2 concentration was quantified in the sera of patients with breast cancer using an ELISA kit (number of patients with breast cancer=118) ( **** P<0. 0001). (B) Kaplan-Meier survival curves based on AGR2 expression were obtained from data of Tang et al  (36). (C) Correlation between the relative expressions of AGR2 and p16 proteins following a mass spectrometry analysis performed on tumors from patients with breast cancer (number of patients=119; P-value=0. 0016). AGR2, anterior gradient 2; w/, with; w/o, without. a slight increase in AGR2 expression in emergent LS174T compared to senescent LS174T (data not shown). Finally, AGR2 expression during CIS escape was assessed with two days' interval. The result presented in Fig. 2D  showed that AGR2 expression during CIS escape increased after four days of emergence.
Therefore the effect of eAGR2 on CIS escape was studied. For this, the growing MCF-7 cells and 293 cells were transfected with a plasmid-encoding AGR2 or with a control plasmid. After two days of transfection, the culture media was collected and added to MCF-7 senescent cells. eAGR2 production was monitored by western blotting (Fig. 4A ). eAGR2 significantly increases the number of emergent clones, as shown in Fig. 4B. Recombinant eAGR2 (200 ng/ml) was also added during CIS escape (Fig. 4C , upper left). The count of emerging clones showed that the recombinant molecule significantly increased CIS escape (Fig. 4C , right and lower part). The effect of eAGR2 on CIS escape after inhibition AGR2 expression was also assessed using siRNA. This experiment showed no influence of eAGR2 on cell emergence (data not shown), thus eAGR2 alone is not sufficient to favor CIS escape and a cooperation is needed between its two forms.
6 , upper part). This analysis revealed only two gene sets from the Hallmarks database with a nominal P-value <0. 05 and a FDR >25%. However, assessing the oncogenic signature database revealed seven significantly enriched gene sets (P-value <0. 05), including five with a FDR <25% (Table  II ). The enriched gene sets in the results were related to AKT and mTOR signaling pathways, suggesting that AGR2 could regulate these pathways during emergence (Fig. 6 , lower part). Previous studies in the authors' laboratory have shown that these two pathways are involved in CIS escape  (7). Therefore, AKT and mTOR pathways were inhibited using two inhibitors (iAKT1/2 and Torin). The results presented in Fig. 7A  show that these inhibitors prevented CIS escape. The present study first focused on the AKT pathway.
Ethical Approval This study was approved by the "Ile de France II" personal protection committee on September 8, 2017, under number 00001072. Written informed consent was obtained from each participant before inclusion in this study after a full explanation of the goal and methodology of this study. This study was registered on the clinical trials on September 28, 2017, under number NCT03305198 before first inclusion and performed according to the recommendation of the Declaration of Helsinki.
Exercise Oximetry Exercise TcPO 2 measurements were performed during a treadmill exercise test under electrocardiographic supervision according to a standardized procedure and set of equipment (PF6000 Perimed R , Stockholm, Sweden, United Kingdom) connected to a computer with the software AcqTcPO 2 , which automatically calculates in real time the decrease from rest of oxygen pressure (DROP: limb changes minus chest changes)  (Abraham et al. , 2005). We used eight probes (one on each calf, one on each thigh, one on each buttock, and two on the chest). The left thoracic probe was considered a default by the computer as the reference probe for the DROP calculation. In the event of measurement anomalies or failure of the left thoracic TcPO 2 sensor head, the right thoracic probe was used as a reference probe for the calculation of DROPs.
5 s; IS voltage for positive mode 5,500 V, for negative mode -4,500 V; source temperature 200 • C; with nitrogen as the collision gas medium. The corresponding parameters for LC-MS were set as follows: scan time 0. 5 s, source temperature 500 • C, with nitrogen as the collision gas medium. All the reagents used in the processing and analysis were of LC-MS grade. Milli-Q Water Ultrapure was used fresh after being prepared by the high-purity water system with Millipore (Milli-Di). The raw data of the Analyst software (AB Sciex, Framingham, Massachusetts, United States) were processed by the MetIDQ software, which is an integral part of the p180 Kit (Biocrates Life Sciences AG). This streamlines data analysis by the automated calculation of metabolite concentrations providing quality measurements and quantification.
e. , metabolites) was based on the variable importance for the projection (VIP) and the loading parameters. VIP values summarize the importance of each variable for the pPLS-DA model, whereas the loading values are indicators of the relationship between the Y vector containing the class information (i. e. , whether rest or postexercise groups) and variables in the X matrix (i. e. , metabolites). Variables with a VIP value greater than unity are important for group discrimination. Multivariate analysis was performed by using the mixOmics R package  (Kim-Anh et al. , 2016). In addition, we performed the Pearson correlation between the clinical criteria of LEAD severity (ABI, DROPs, MWT, and WELCH score) and metabolite exercise-induced changes.
Metabolomic Profiles The flowchart for metabolomic analyses is presented in Figure  4. Among the 188 analyzed metabolites, 150 metabolites were considered accurately measured after validation of the quality controls. These raw data are available in Supplementary Table  3. The non-supervised pPCA approach (Figure  5 ) highlighted an outlier. The two samples of this patient were excluded from the metabolomics datasets during subsequent statistical analyses. Supervised pPLS-DA based on 23,345 models enabled a clear distinction between rest and postexercise samples (Figure  6 )  and showed very good predictive performance of training sets on validation sets with a median AUROC value of 0. 95 and a p-value of 0. 00067 (Figure  7 ). Considering this high global predictive performance, the AUROC cutoff was placed at 0.
II. GNSS KINEMATIC SURVEYS From 2001 to 2019, 148 kinematic GNSS surveys suitable for this analysis were performed. Fig. 1  shows the location of the surveys and Table  I  provides details for each year. The data was processed with the Canadian Spatial Reference System Precise Point Positioning (CSRS-PPP) online software. We compared its solutions with those of two other online software, GIPSY-PPP, and IGN-PPP, and with our local calculations made with GIPSY 6. 4. We found that it was the one giving the best repeatability of the traces and the lower number of outliers. Moreover, for observations made after mid-2011, it processes not only the GPS (global positioning system) data but also the data of the GLONASS constellation, which is valuable in the case of kinematic observations that are very sensitive to the loss of satellites in vegetated or urban areas.
Fig. 7  shows how the resampling was performed with a method that fits smoothly the curves. The 1-m resampling gives more weight to the areas where we believe both the DEM and GNSS dataset are more reliable. This is the case in particular along the main roads where the vehicle is moving relatively fast with a three-dimensional (3-D) trajectory very smooth. It leads to an increase of one order of magnitude of our dataset from 885 252 to ∼10. 8 million points. Along most of the observed roads those points sample, pixel by pixel, the 1-m resampled TanDEM-X DEM, except in some rare forest and urban areas where GNSS data is missing because of the masks. With this oversampling and the redundancy of the surveys, most of the homologous pixels located on the surveyed roads contain several observations.
In the case of our calibration trail of Fig. 4 , observed many times from 2001 to 2019, the total number of GNSS elevations ranges between 135 and 350 data points every 1 m along the path as shown in Fig. 8  (left). The standard deviation increases to ∼0. 2 m, which is twice that of the single survey of September 25, 2016, because of the different experimental conditions. Fig. 8  (right) also shows a local peak of 0. 5 m in a place where the road was reworked for the installation of wind engines nearby (visible in the video). 3 We consider that this value of 0. 2 m is a good estimate of the accuracy of the vertical coordinates of our GNSS 3 [Online]. Available: https://youtu. be/1NDTSt0GZRI points. As we will see below, this accuracy is five times better than that of the TamDEM-X DEM and therefore appropriate for the assessment of the DEM's accuracy.
III. VERTICAL ACCURACY OF THE TANDEM-X DEM We now compare the TanDEM-X DEM with the elevation of the GNSS points using strategies already used in previous works  [21] ,  [22] , and other guidelines for DEM assessment  [23] ,  [24]. We use two different approaches, the first one is using the whole GNSS data set as a global entity and the second one is based on a preliminary survey-per-survey analysis.
A. Resampling of the DEM We first resample the DEM at a higher resolution. Indeed, its native resolution is too coarse to take into account precisely enough the local slopes and the exact location of the GPS points within the pixels. Resampling the DEM by a factor of 10 leads to pixels of size ∼1 m, which allows efficient comparison of the DEM and GNSS, as this size is smaller than the width of the sampled road even the smallest ones. We tested a bilinear interpolation and a cubic interpolation (see validation in Section IV-C) and hereafter we use the second one because, as we will see later, it leads to lower scatters between the DEM and the GNSS. The resampling is performed with the GDAL software package, using the GDAL VRT (virtual data set) routine. The instruction to make the resampling is "gdalbuildvrt -tr 0.
B. Vertical Shift With Respect to All the GNSS Points Fig. 9  shows the histogram of the difference DEM-GNSS for the whole set of 885 252 points. Using the mean as the metric, the DEM is shifted upward by 0. 466 m with respect to the GNSS. The histogram is not perfectly symmetric and the median is 0. 376 m thus 0. 09 m below the mean. Fig. 10  shows the difference DEM-GNSS of the whole dataset plotted as a function of the elevation. The colors show the density of points. Most points are located at low elevation, with a gentle density decrease while going up. All elevations are sampled up to 1750 m which is close to the ∼2200 m top elevation of the area. Fig. 11  shows that the areal distribution of the scatters is relatively homogeneous. To the northwest, some red sections fit with the location of a highway built after the production of the DEM.
495 ± 0. 026 m, close to the 0. 466 m found previously. The uncertainty is estimated by dividing the mean of the 148 rms scatters (0. 315 m) by the square root of the number of observations (148), assuming a Gaussian distribution of the 148 single rms scatters. The fact that the value of 0. 315 m is larger than the 0. 2 m mean accuracy of our GNSS coordinates (as determined in Section II-B), suggests the possible existence of spatial heterogeneities of the anomalies of the DEM with respect to the GNSS. Fig. 12  shows that the DEM is shifted homogeneously in the entire area without tilts. It shows also no dependence at the first order of the shift with the elevation, yet when we fit the data with a second-order polynomial, we see a slight dependence.
A. Global Analysis of the Whole Dataset We offset the DEM in steps of 0. 1 m in longitude, latitude, and elevation, and calculate at each step the rms scatter between DEM and GNSS elevations, hereafter called δ. As an example, Fig. 16  shows the distribution of δ as a function of the longitudinal and latitudinal offsets α and β, for two values of the vertical offset γ of -0. 2 m (left) and -0. 5 m (right). The latter leads to lower deviations, which was expected as we know from Section II-B and II-C that the vertical shift is close to -0. 5 m. Fig. 17  shows that, at the first order, δ evolves as a parabolic function of the variables α, β, and γ, which can be written as follows: δ = Aα 2 + Bβ 2 + Cγ 2 + Dα + Eβ + Fγ + G + Hαγ + Iβγ.
It is remarkable that our GNSS dataset permitted us to make this assessment. Moreover, by using GNSS points on both sides of the gulf and outside of it, we ensured the presence of slopes in all azimuths in the dataset, which reduces the covariance between vertical and horizontal shifts. Looking now at the best fitting vertical shifts of the DEM, associated with the best fitting rms scatters, Fig. 20  shows that their dependence as a function of longitude and latitude can be assessed precisely. For example, when the DEM is shifted northward (red curve), the vertical shift between the DEM and the GNSS becomes lower, which is understandable intuitively and is quantified here.
C. Using the GNSS Data Resampled at 1 m We redo the analysis of Section IV-A with the resampled 10. 8 million GNSS coordinates presented in Section II-B. This resampled dataset contains more additional points where the vehicles are moving fast, thus along the large roads and highways. The values of the best fitting shifts in longitude, latitude, and height become α = -0. 024 m, β = -0. 023 m, γ = -0. 338 m. The rms scatter is now 1. 150 m, very close to the previous one. In horizontal, the DEM appears now to be perfectly accurate. In the vertical, there is a small (well below the value of the rms scatter) but significant difference of 0. 13 m with the range of values of γ found previously, and quite systematic in most of the surveys as shown in Fig. 21. This difference is presumably an attenuated global effect of similar origin to the one that causes the overestimated shift of 0.
V. CONCLUSION Using a dense and accurate set of GNSS coordinates produced by 148 kinematic GNSS surveys performed from 2001 to 2019 in the western Gulf of Corinth, covering a total distance of ∼25 000 km, we establish that 1) the average vertical accuracy of our GNSS data is ∼0. 2 m, 2) the TanDEM-X DEM is shifted with respect to the GNSS ground true by -0. 10 ± 0. 10 m toward the east, and -0. 36 ± 0. 10 m toward the north, -0. 47 ± 0. 03 m toward up. Those values are 20 times below the nominal resolution of the DEM. We observe no lateral tilt of the DEM over the entire area and no or minor dependence of the vertical DEM offset as a function of the elevation. After shifting the DEM to its right vertical location, the rms deviation between the DEM and the GNSS ground truth is 1. 125 m. This average scatter is below the nominal expected precision of the TanDEM-X DEM  [13]  and below the values found in some publications where GNSS is used to assess its precision, e.
In the case of a large earthquake, which is occurring typically every few decades  [28] , the seismic rupture can reach the surface and shift it from a few centimeters to a few decimeters over distances of several hundred meters to a few kilometers. In such a case, DEM having the quality of the TanDEM-X DEM can contribute to quantifying local vertical ground deformations, provided that two DEMs are available, one before and one after the event. In the last 10 years, there was not such a large event but there were active landslides and active erosion phenomena along the rivers and the shores of the gulf. Those changes might already be detectable by comparing two TanDEM-X DEM produced at two different epochs separated by a few years. Our study leaves open questions, e. g. , is the metric precision of the DEM found along roads still valid in the rest of the DEM? Would the comparison of two TanDEM-X DEM (or DEM with equivalent precision) allows us to confirm the scatters found in some sections of newly built highroads? SUPPORTING INFORMATION 1) PDF containing metadata for the 148 GNSS surveys and description of the format of the CSV files.
ACKNOWLEDGMENT The authors would like to thank all the colleagues and students who were involved in the 148 kinematic surveys carried out from 2001 to 2019, in particular  Ferdaous Chaabane ,  Rana Charara ,  Michalis Delagas ,  Anne Deschamps ,  Emilie Klein ,  Kamel Lammali ,  Marine Roger ,  Nikos Roukounakis ,  Luca Terray ,  Maurin Vidal , and  Christophe Vigny. Most  of the GNSS receivers used were provided by the "GPSMOB" pool of GNSS receivers of the  French CNRS. They would like to thank the  Canadian Geodetic Survey of Natural Resources  for making available on-line the CSRS-PPP service that they used to process the kinematic GNSS data. For some figures, they used the Generic Mapping Tool [29]. They would like to thank the  German Aerospace Center (DLR)  for making available the TanDEM-X DEM in the framework of the  science  project  DEM_CALVAL2013  " Corinth Rift Laboratory TanDEM-X DEM  and kinematic GNSS array.
The in vivo whole epidermis data were complemented by more lesion-specific HPLC tandem mass spectrometry (HPLC-MS/ MS) measurement of CPD, 6-4(PP) and Dewars. The SSR source was a 300W-16S xenon arc solar UV simulator (Solar ® Light; Glenside), and calibrated and used as previously described  (Fajuyigbe, Lwin, et al. , 2018). Irradiance was routinely measured with a PMA 2100 radiometer (Solar ® Light; Glenside) after calibration by spectroradiometry. One standard erythema dose (SED) was delivered in 6 s.
, 1991). Slides were washed three times in PBS and incubated with goat anti-mouse Alexa Fluor 488 (Thermo Fisher Scientific RRID: AB 2534069) at a 1:200 dilution for 30 min. Slides were then washed in PBS, then in distilled water, counterstained with Prolong Gold anti-fade with DAPI (Thermo Fisher Scientific), cover-slipped and left at room temperature in the dark for 2 hr before image capture. Immunofluorescence images were captured by Zeiss Axio-Observer Z1 Microscope (Carl Zeiss) and quantified using Cell Profiler v2. 1. 1 software (Cell Profiler, Broad Institute). DAPI-stained nuclei (blue) were gated, and mean green (CPD) intensity (Alexa Fluor 488) was assessed in arbitrary units (a. u. ) in three epidermal zones. The lowest two cell layers were deemed the basal layer. The epidermis above that was divided equally into two horizontal zones, termed the middle and upper layers.
Morphological aberrations To measure morphological aberrations that may arise from an uneven window surface, we imaged a microgrid (Argolight SLG-075, pattern B) using a laser excitation wavelength of 405 nm through either water only (uncovered), glass, relaxed PDMS, or stretched (to 200%) PDMS (wavy) window in duplicates. Dot grids were segmented using Fiji v1. 53 (threshold > analyze particles), and dot coordinates were extracted. Using a central motif (cross 0;0) and the top left dot (-105; -75), we corrected the rotation of the grid under the different materials and compared the corrected coordinates S C I E N C E A D V A N C E S | R E S E A R C H A R T I C L E 7 of 10 of each dot to either "medium only" (measured) or the theoretical (15 × 15 m) pattern B matrix. Graphical deformation maps were generated using the R package ggplot2  (34).
Acknowledgments: We acknowledge  P. Jacquemin  ( Lycée Marcellin Berthelot , Questembert-plastic engineering section) for help on the window and mold design and initial prototyping and for teaching us the basics in polymer properties and processing. We thank  L. Sengmanivong ,  M. Irondelle , and  O. Leroy  from the  Cell and Tissue Imaging (PICT-IBiSA)  facility and the  Nikon Imaging Centre, Institut Curie , member of the  French National Research Infrastructure France-BioImaging  ( ANR10-INBS-04 ). We are also grateful to  R. Margueron  ( Institut Curie ) for advice and constructive discussions. We acknowledge  O. Delattre  and  S. Zaidi  ( Institut Curie ) for providing the PDX tumors, in addition to the  Institut Curie In Vivo Experimental Facility  for help in the maintenance and care of the mouse colony.
Statistical Analysis The statistical analysis of the results was carried out using the R Studio Software (R version 3. 5. 2). The influence of both wood fiber proportion and initial MC on the wettability measurements and water retention properties were tested by one-way analysis of variance (one-way ANOVA, linear models), after checking for normality of datasets by Shapiro-Wilk tests (p < 0. 05). Whenever significant differences were observed (p < 0. 05), Tukey's range tests (Tukey-HSD) were applied to identify where differences occurred.