dataesr/scientific-paragraphs-categorization huggingface dataset:
There was no significant correlation between FATE1 mRNA expression levels and patients' DFS (Figure  1D ). We also measured the prevalence of circulating anti-FATE1 autoantibodies in a cohort of 12 children with ACT (all positive for TP53 R337H). For 10 of them, FATE1 IHC expression data in their tumors were available, expressed in all tumors with H-scores ranging from to 0. 1 to 3 (Table  1 ). Circulating anti-FATE1 antibodies were detected in 6/12 (50%), 2/12 (17%) and 12/12 (100%) patients' sera using IF, ELISA and WB, respectively (Table  1 ). In total, 6/12 (50%) sera were positive using two (IF or ELISA in addition to WB) out of three methods and only one serum (8%) was found positive using all three methods. There was no correlation between the presence of circulating anti-FATE1 antibodies, stage of the disease, steroid secretion pattern or prognosis.
Cancers 2020, 12, x 3 of 20 WT TP53, 10 bearing the R337H and 10 other TP53 mutations; Table  1 )  [13]  than in age-matched normal adrenal glands (Figure  1C ). There was no significant correlation between FATE1 mRNA expression levels and patients' DFS (Figure  1D ). We also measured the prevalence of circulating anti-FATE1 autoantibodies in a cohort of 12 children with ACT (all positive for TP53 R337H). For 10 of them, FATE1 IHC expression data in their tumors were available, expressed in all tumors with H-scores ranging from to 0. 1 to 3 (Table  1 ). Circulating anti-FATE1 antibodies were detected in 6/12 (50%), 2/12 (17%) and 12/12 (100%) patients' sera using IF, ELISA and WB, respectively (Table  1 ). In total, 6/12 (50%) sera were positive using two (IF or ELISA in addition to WB) out of three methods and only one serum (8%) was found positive using all three methods.
FATE1 Expression and Circulating Anti-FATE1 Antibodies in Adult ACC FATE1 mRNA expression is preponderant in ACC among all malignancies in the TCGA pancancer dataset (Figure  2A ). These data are consistent with our previous results, which showed only minimal or undetectable FATE1 protein expression by IHC in a variety of other cancers  [5]. Similar to pediatric tumors, FATE1 expression was heterogeneous in adult ACC (Figure  2B ). We extended our mRNA analysis to 189 adult ACC samples (including the 79 TCGA samples) and 18 normal adrenal tissues. FATE1 mRNA expression level was heterogeneous across all samples with a range of intensities over nearly five decades in log10 scale (Figure  2C ). FATE1 was significantly overexpressed in primary tumors when compared with normal samples (p = 4. 47 × 10 -16 , Student ttest).
84 × 10 -3 and p = 4. 34 × 10 -2 , respectively, in the multivariate analysis and Wald test). The presence of circulating antibodies directed against FATE1 was assessed in a cohort of adult patients with adrenocortical tumors from three different ENSAT centers (46 malignant-among these, six patients had a pre-surgery and a post-surgery sample available for analysis-and 10 benign). Patients' data are reported in Table  4. Anti-FATE1 antibodies were detected in 20/62 (32%), 44/62 (71%) and 62/62 (100%) of patients' sera using IF, ELISA and WB, respectively (Table  4 ). In total, 30/62 (48%) sera were positive using two (IF or ELISA in addition to WB) out of three methods and 17/62 (27%) sera were positive using all three methods. There was no correlation between the presence of circulating anti-FATE1 antibodies, stage of the disease, steroid secretion pattern or prognosis.
The presence of circulating antibodies directed against FATE1 was assessed in a cohort of adult patients with adrenocortical tumors from three different ENSAT centers (46 malignant-among these, six patients had a pre-surgery and a post-surgery sample available for analysis-and 10 benign). Patients' data are reported in Table  4. Anti-FATE1 antibodies were detected in 20/62 (32%), 44/62 (71%) and 62/62 (100%) of patients' sera using IF, ELISA and WB, respectively (Table  4 ). In total, 30/62 (48%) sera were positive using two (IF or ELISA in addition to WB) out of three methods and 17/62 (27%) sera were positive using all three methods. There was no correlation between the presence of circulating anti-FATE1 antibodies, stage of the disease, steroid secretion pattern or prognosis.
FATE1-Coregulated Genes in Adult ACC Next, to further explore the biological pathways associated with FATE1 mRNA expression in ACC, we compared the expression profiles of all genes between "FATE1-high" tumors and "FATE1-low" tumors within the whole TCGA data set (learning set). We identified 1084 differentially expressed genes, including 659 genes upregulated and 425 genes downregulated in the "FATE1-high" samples (Figure  4A  and Table  S2 ). The robustness of this gene signature was confirmed in the independent validation set (pool of three other public ACC gene expression datasets; n = 110; Figure  4B ) with high significance. Ontology analysis of these 1084 differentially expressed genes revealed that expression of genes involved in steroidogenesis is associated with high FATE1 expression, while expression of immune response genes is associated with low FATE1 expression (Figure  4C , Table  S3 ).
FATE1 -Coregulated Genes in Adult ACC Next, to further explore the biological pathways associated with FATE1 mRNA expression in ACC, we compared the expression profiles of all genes between "FATE1-high" tumors and "FATE1low" tumors within the whole TCGA data set (learning set). We identified 1084 differentially expressed genes, including 659 genes upregulated and 425 genes downregulated in the "FATE1high" samples (Figure  4A  and Table  S2 ). The robustness of this gene signature was confirmed in the independent validation set (pool of three other public ACC gene expression datasets; n = 110; Figure  4B ) with high significance. Ontology analysis of these 1084 differentially expressed genes revealed that expression of genes involved in steroidogenesis is associated with high FATE1 expression, while expression of immune response genes is associated with low FATE1 expression (Figure  4C , Table  S3 ).
Gene Expression Data in Pediatric Patients with ACC They were retrieved by NCBI GEO, accession code GSE76019  [13]. The H295R/TR N-Flag FATE1 stable cell line was established and cultured as described  [5]  and can express N-terminally flagged FATE1 in a doxycycline-inducible manner. To induce FATE1 overexpression, cells were treated for 24 h with doxycyline (Dox; 1 g/mL, Sigma-Aldrich, St. Quentin Fallavier, France). We developed an IF assay to measure the presence of anti-FATE1 antibodies in patients' sera by exploiting our H295R/TR Nflag-FATE1 cell line. Briefly, cells were seeded on 96-well Clear Bottom Black Polystyrene microplates (Corning, Corning, NY, USA) and treated for 24 h without or with doxycycline. Immunofluorescence was performed as previously described  [5]. Cells were incubated overnight at 4 • C with different dilutions of patients' sera (1/20 to 1/100) prepared in phosphate-buffered saline (PBS/0.
In addition, sera from patients with pediatric ACC were analyzed (n = 12; Curitiba, PR, Brazil). The diagnosis of ACC was made on established criteria based on clinical, biochemical, and morphological data and clinical data were collected through ENSAT and hospital centers registries. Patients' sera were diluted at 1:100 in PBS/0. 1% non-fat dry (NFD) milk and added in duplicate to the ELISA plates (100 µL per well). After 2 h incubation at room temperature on a plate shaker, plates were washed five times with PBS/0. 05% Tween-20. Anti-human IgG-horseradish peroxidase (HRP) conjugate (Southern Biotech #2010-05) diluted 1:20,000 in PBS/0. 1% NFD was added (100 µL per well) and incubated for 1 h at room temperature on a plate shaker. After five washes, tetramethylbenzidine (TMB) was added, and the reactions were developed and then stopped with stop solution (TMB solution for ELISA; Interchim, Montluçon, France).
Plates were read at 450 nm. A positive serum was used in each plate as a positive control. We defined a positive reaction as an OD value of a diluted serum that exceeds the mean OD value of negative control (coated with irrelevant protein) by two-fold. Results for sera from ACC and ACA patients tested in ELISA are shown in Figure  5B. Curitiba, PR, Brazil). The diagnosis of ACC was made on established criteria based on clinical, biochemical, and morphological data and clinical data were collected through ENSAT and hospital centers registries. Patients' sera were diluted at 1:100 in PBS/0. 1% non-fat dry (NFD) milk and added in duplicate to the ELISA plates (100 μL per well). After 2 h incubation at room temperature on a plate shaker, plates were washed five times with PBS/0. 05% Tween-20. Anti-human IgG-horseradish peroxidase (HRP) conjugate (Southern Biotech #2010-05) diluted 1:20,000 in PBS/0.
Acknowledgments: We thank  E. M. Pinto  and  G. P. Zambetti  for pediatric ACC gene expression data;  C. Birck  and the  IGBMC Structural Biology and Genomics Technology Platform  for  FATE1 1-162  protein expression;  C. Zaghrini ,  C. Payré  and  G. Lambeau  for advice in FATE1 ELISA set-up and gift of recombinant THSD7A protein;  J. Cazareth  and  S. Abelanet  for help in imaging. The authors apologize with readers for having been forced by the journal's style to quantify bands in Figure 5C,D, which clearly does not make sense.
NH 4 + concentration was also measured in these samples by colorimetric assays in accordance with the NF ISO 15923-1 standard (AFNOR, 2014). Concentrations of solids were also determined to validate the experimental setup. XRD was used on the bioleaching residues (Bruker D8 Advance diffractometer, 4-75 °2θ, 0. 03 °2θ/s, 288 s each step, Cu tube) and the diffraction patterns were processed with Diffrac Suite software.
(2016). The amplification products were analysed on agarose gel. Community fingerprints were obtained by CE-SSCP (Capillary electrophoresis-single strand conformational polymorphism). One microlitre of 5-to 500-fold diluted PCR product, 0. 4 μL of Genescan-600 LIZ internal standard (Applied Biosystems) and 18. 6 µL of deionized formamide HiDi (Applied Biosystems) were heat-denatured for 5 minutes at 95 °C and immediately cooled on ice for 10 minutes. Fragment analyses were conducted with the ABI Prism 310 (Applied Biosystems). GeneScan software (Applied Biosystems) was used to realign the peak profiles based on internal standard migration, assign peak position, align community peaks with peaks of known acidophilic strains, and calculate relative abundances on the basis of peak areas.
(2015). When the concentration of Fe(III) was high (8 g. L -1 ), the dissolution kinetics were fast compared to those obtained with 1 g. L -1 Fe(III) or with sulphuric acid only. The same results were obtained for Zn and Co (data not shown). For Pb, low dissolution yields were observed under all conditions, probably due to precipitation (data not shown). For Fe, an increase in concentrations, related to the dissolution of Fe contained in PCBs, was measured initially; the subsequent decrease observed was related to precipitation phenomena (data not shown). Overall, these simultaneous phenomena (dissolution and precipitation) could barely be distinguished, so the Fe dissolution yield and rate were not determined. A residual concentration of Fe(III) (about 1 g. L -1 ) was measured at the end of the tests carried out with 8 g.
5 ng of exosomal DNA. The amplification was performed under the following programs: 95 • C for 10 min, 40 cycles of (94 • C for 30 s, 60 • C for 60 s), 98 • C for 10 min. The results were analyzed by the Quanta-Soft Analysis Pro software (Bio-Rad). For denaturation-enhanced ddPCR  (17) , the reaction solutions were placed in a T100 Thermal Cycler (Bio-Rad) for DNA denaturation at 95 • C for 1 min before droplet generation, in order to double the number of positive droplets obtained from a given amount of input DNA.
This inhibitory effect was higher with C 8 -HSL as expected due the higher catalytic efficiency of SlPA on this compound (Table  1 ). Similar results were observed for AuAAC in the forward bioassay of C 8 -HSL hydrolysis (data not shown). Furthermore, the effect of both enzymes in violacein production by enzymatic hydrolysis of long-chain AHLs was detected by reverse bioassays (data not shown). adequate kinetic characterization. In fact, a low catalytic activity (kcat) was also observed in the case of KcPGA using C6-HSL (0. 03 s -1 ) and 3-oxo-C6-HSL (0. 06 s -1 )  [19].
This inhibitory effect was higher with C8-HSL as expected due the higher catalytic efficiency of SlPA on this compound (Table  1 ). Similar results were observed for AuAAC in the forward bioassay of C8-HSL hydrolysis (data not shown). Furthermore, the effect of both enzymes in violacein production by enzymatic hydrolysis of long-chain AHLs was detected by reverse bioassays (data not shown). QS interference by the wild-type strains producing SlPA and AuAAC was confirmed using C. violaceum CV026-based forward bioassay (Figures  2B, C ). Previously, C. violaceum CV026 was assessed to produce violacein when C8-HSL was added to the agar plate (Figure  2A ), and this production was not affected by the presence of wild-type S. lividans (Figure  2D ). Finally, AHL acylase cleavage was also demonstrated employing the recombinant strains of Streptomyces lividans CECT 3376 and CECT 3377, expressing SlPA and AuAAC respectively (Figures  2E, F ), demonstrating that both enzymes can interfere in quorum sensing signaling in vivo.
Bifunctional acylases (with both penicillin acylase and AHL acylase activities) are indicated by asterisks, but among them only SlPA and AuAAC present aculeacin A acylase activity as well. The evolutionary history was inferred using the neighbor-joining (NJ) method within the 3DM and MEGA X packages. The optimal tree with the sum of branch length = 8. 78001801 is shown. This analysis involved 31 amino acid sequences. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There was a total of 965 positions in the final dataset.
Supplementary Materials: The following are available online at http://www. mdpi. com/2073-4344/10/7/730/ s1. Figure  S1 : Hyperbolic regression of the activity at different concentrations of several AHLs. Figure  S2 : Structure-guided phylogenetic analysis of the Ntn-hydrolases superfamily as analyzed from 3DM database. Figure  S3 : Correlated mutations matrix for the 5C9IA subfamily. Figure  S4 : Sequence alignment of SlPA and AuAAC with other homologues from the AHL acylase group A. Table  S1 : Amino acids residues at different positions selected in SlPA according to the structure-based alignment from the 3DM database.
Ntn-hydrolases with reported cyclic lipopeptide acylase activity were as follows: SsCLA, from Streptomyces sp. FERM-BP5809 (BAD07025). Bifunctional acylases (with both penicillin acylase and AHL acylase activities) are indicated by asterisks, but among them only SlPA and AuAAC present aculeacin A acylase activity as well. The evolutionary history was inferred using the neighbor-joining (NJ) method within the 3DM and MEGA X packages. The optimal tree with the sum of branch length = 8. 78001801 is shown. This analysis involved 31 amino acid sequences. All ambiguous positions were removed for each sequence pair (pairwise deletion option). There was a total of 965 positions in the final dataset.
52 to 2. 0. A visual comparison of light curves based on standard and relaxed filtering showed that no spurious signals were introduced in this way. Using these criteria, we retained 257. 7 ks of good time exposure. These good time data were corrected to the Solar System barycenter using the barycorr tool, where we used the optical coordinates of  Hartman et al. (2008)  and the JPL DE405 planetary ephemeris  (Standish 1998). All times reported in this work are therefore expressed in units of Barycentric Dynamical Time (TDB). Finally, we estimated the background contributions to our data from NICER observations of the RXTE blank-field regions  (Jahoda et al. 2006). Two thermonuclear X-ray bursts were observed with NICER, the first on August 9 and the second on August 21. We exclude both events from the analysis presented here.
(2009). From Figure  5  we see that this offset is a continuation of a recent trend, which suggests that the binary orbit has been contracting in the recent decade. Indeed, when we consider only the second   (Hartman et al. 2009 ). The curves show parabolic trends fit to the  1998 -2008 (dashed), 2008 -2019 (dash-dot), and 1998 -2019 (solid)  subsets of the data. Middle: residuals relative to the 1998 -2008 parabolic model. Bottom: residuals relative to a parabolic model fit to the entire dataset. A sinusoid with a 18. 2 yr period and 7 s amplitude was added to guide the eye. In all panels the uncertainties are smaller than the point size. decade of measurements (2008 through 2019), then we can fit the ∆T asc evolution with a parabolic function (χ 2 r of 0. 02 for 1 dof). This fit yields an orbital period derivative of Ṗb = (-5.
Hence, the reference frame of our flux-adjusted phase model is not guaranteed to be the same as that of  Patruno et al. (2009c). Because a complete reanalysis of the archival RXTE data is beyond the scope of this work, we applied our spindown analysis to the linear phase model. While this choice may have introduced some bias into our best-fit solution, the main implication of our analysis is that we now have a sufficiently long baseline of measurements for SAX J1808 that astrometric effects can no longer be treated as uncertainties, but instead should be accounted for in the timing analysis. Finally, we note that we did not include a spin frequency measurement from the 2015 outburst. While a spin frequency was determined for this outburst using XMM-Newton and NuSTAR  (Sanna et al. 2017) , as well as with Chandra  (Patruno et al.
2017) , each of these measurements suffers from substantial statistical and systematic uncertainty. Specifically, the XMM-Newton and NuSTAR data suffered from clock drifts, which limited the absolute time resolution. The Chandra data, on the other hand, was collected late in the outburst, when the source had already decayed to low flux levels. These data have relatively poor statistical uncertainty on the frequency measurements. Additionally, all these observations only sample a very limited flux range, and are potentially biased by the flux-phase relation.
In this analysis we only consider the phase residuals, but additional information is encoded in the precise shape of the waveform  (Poutanen & Gierliński 2003; Ibragimov & Poutanen 2009; Morsink & Leahy 2011) , and in its energy dependence. Hence, comprehensive time-resolved waveform modelling of SAX J1808 may not only reveal the neutron star mass and radius, but also constrain the detailed physics of the disk-magnetosphere boundary region. Facilities: ADS, HEASARC, NICER Software: heasoft (v6. 26), nicerdas (v6a), tempo2  (Hobbs et al. 2006).
(2019). For the experiments shown here we have performed 15 iterations. At the end of the initialisation procedure, we use the last inferred basal drag coefficient together with the corresponding thermal state to run a relaxation experiment of 65 years with a free evolving grounding line. The simulated ice sheet after this relaxation experiment is used as the initial condition for the historical experiment (hist, see Sec. 2. 3). Our reference ice thickness and bedrock topography is the Bedmap2 dataset  (Fretwell et al. , 2013). This dataset is used as the initial topography for the 65-yr relaxation experiment used to define the initial state for the historical simulation. The ice thickness in Bedmap2 is also used as a target for the iterative initialisation procedure. Our reference present-day surface mass balance comes from RACMO2.
3p2  (van Wessem et al. , 2018 (van Wessem et al. , ) averaged over 1979 (van Wessem et al. , -2016. The reference present-day oceanic forcing used to compute the sub-shelf melt rates (more details available in Sec. 2. 2) comes from an observational dataset  (Jourdain et al. , 2019) , averaged over 1995-2017. These reference atmospheric and oceanic forcing are used during the initialisation procedure and for the relaxation and control experiments (ctrl and ctrl_proj, see Sec. 2. 3). The geothermal heat flux is the one of  Shapiro and Ritzwoller (2004). The model is run on a Cartesian grid at 16 km resolution covering the Antarctic ice sheet using a polar stereographic projection.
The surface mass balance has been computed from the GCM outputs as the total precipitation minus the evaporation and runoff and regridded to the 16 km resolution grid. The anomalies have to be added on top of the reference present-day climatology. -The oceanic forcing consists in the thermal forcing, i. e. the ambient temperature minus the ambient temperature of the freezing point. In the standard ISMIP6-Antarctica approach, which we follow with GRISLI, the thermal forcing is used to compute sub-shelf melt rates using a non-local quadratic parametrisation as described in  Jourdain et al. (2019). For each GCM output, the parametrisation has been calibrated so that it can reproduce the observational dataset around Antarctica. Three estimates for the temperature sensitivity are provided (low, medium and high) for each GCM.
3. 2 Ice sheet evolution projections 3. 2. 1 Ice sheet evolution for CMIP5 models using RCP8. 5 The evolution of the ice volume change for the different CMIP5 models under the pessimistic greenhouse gas emission scenario (RCP8. 5) and using the sub-shelf melt parametrisation calibrated over the Antarctic wide dataset is shown in Fig. 3. The total ice volume (Fig. 3a ) is decreasing for the six CMIP5 models and for most models there is an acceleration in volume loss in the The volume change contributing to sea level rise (e. g. above floatation) shows a different evolution than total ice volume (Fig. 3b ). While the total ice volume change is always negative, the simulated Antarctic contribution to sea level rise in 2100 for the CMIP5 models can be either positive, e. g. ~60 mm of sea level equivalent (mmSLE) for HadGEM2-ES, or negative, e.
Ice sheet evolution for RCP2. 6 and SSP126 The forcing dataset provided by the ISMIP6-Antarctica working group allows for an evaluation of the impact of the future evolution of the greenhouse gas emission on the Antarctic ice sheet response. In Fig. 7  we show the ice volume change under three climate models that have run for a pessimistic (RCP8. 5 or SSP585) and an optimistic (RCP2. 6 or SSP126) emission scenario. The total ice volume loss is systematically smaller when using the optimistic scenario. The model that produces the greater volume loss, NorESM1-M, also shows the most pronounced response to the choice of the scenario. For this model, even if the volume loss contributing to global sea level rise remains almost unchanged, there is a drastic reduction in total ice volume loss when using the optimistic scenario.
, 2019). However, the choice of the dataset used to calibrate the parametrisation can lead to substantial differences in the sub-shelf melt model. Fig. 8  shows the simulated ice volume change when using the sub-shelf melt parametrisation calibrated to reproduce the mean Antarctic melt rate (reference) or calibrated to reproduce the Pine Island's grounding line melt rate (PIGL). The PIGL calibration produces higher melt rates and much greater volume loss than the reference calibration. For the medium oceanic sensitivity, the use of the PIGL calibration leads to an additional total volume loss of 200000 to 300000 km 3 and an additional contribution to global sea level rise of about 40 to 50 mmSLE with respect to the reference calibration. In addition, with the PIGL calibration, the model shows a much larger sensitivity to the oceanic forcing as the difference from a low to a high oceanic sensitivity can be as large as 350000 km 3 (100 mmSLE) when using the CCSM4 forcing.