dataesr/scientific-paragraphs-categorization huggingface dataset:
This reference section also provides the opportunity to integrate data from the drill cores GT1 and GT2 of the Oman Drilling Project  (Kelemen et al. , 2020) , which are located in the same Wadi. The 400 m long gabbro sections from drill sites GT1 and GT2 are located in the LG and in the layered/foliated gabbro transition of the Wadi Gideah, respectively. Wadi Gideah drains southward from a divide located in the harzburgites of the mantle horizon (Figure  1a ). The crustal section dips gently ∼28° to the south  (Pallister & Hopson, 1981) , a value which is representative of the whole Wadi Tayin block  (Nicolas et al. , 1996).
Crystallographic Preferred Orientations Plagioclase, clinopyroxene, and olivine CPO were measured using the Electron Back-Scattered Diffraction (EBSD) technique (e. g. ,  Prior et al. , 2009) , using a JEOL JSM-5600 Scanning Electron Microscope (SEM) at Géosciences Montpellier. The system is equipped with an Oxford/Nordlys EBSD detector; the diffraction patterns were collected using the Channel 5® software suite, and later the AZtec software from Oxford Instruments. The SEM was used at an accelerating voltage of 15 kV. Crystallographic orientation maps were obtained for each sample, covering about 80% of a thin section (2. 5 × 4 cm) with a sampling step size ranging from 14 to 35 μm. The indexing rate (fraction of patterns that are automatically indexed during mapping) ranges from ∼25% to 94% in the raw maps (75% on average).
The raw data contains all indexed pixels with a mean angular deviation (i. e. , the angle between the acquired diffraction pattern and the indexing solution proposed by the software) ≤1°. A first stage of post-acquisition data processing was done using the Tango software of the Channel 5® suite to increase the quality of the maps. It consists of removing isolated pixels (either non-indexed or indexed as a given phase and surrounded by pixels indexed for another phase) and filling non-indexed pixels that have a minimum of 5 neighbor pixels with the same orientation. The EBSD data sets were then processed using MTEX (version 5. 2. 3), a free MATLAB toolbox for analyzing and modeling crystallographic orientations (http://mtex-toolbox. github. io; Bachmann et al. , 2010; Hielscher & Schaeben, 2008).
Crystal Size Distributions For the determination of CSD, we used the software CSDCorrections 1. 60 provided by  Higgins (2000). The inserted parameters length, width, area and angle of every plagioclase crystal were obtained by EBSD measurements. We used the ellipse minor axis length for the calculation. An average value of the plagioclase roundness was calculated from the EBSD data used for the CSD determination with the software. On the basis of the two-dimensional (2D) data obtained for every indexed grain, we calculated the three-dimensional (3D) aspect ratio of plagioclase following the procedure proposed by  Higgins (1994)  normed to 1 for the short axis length. Using the equation  Intermediate Short L l L w ( 2 ) where L is the 3D length of the indexed parameter, l is the 2D length, and w the 2D width of the grain, the ratio of the 3D intermediate to short axis was calculated for every plagioclase grain with a width of at least 30 µm (=thin section thickness; cf.
Crystal Size Distributions CSD results for plagioclase are shown in Figure  4. They are displayed in a conventional way (Figure  4c ) with the population density (n; the number of crystals per volume in a size bin divided by the width of the size bin;  Garrido et al. , 2001; Marsh, 1988)  as a function of the binned crystal size (3D length of the crystals). The CSD plots show linear trends in the mid to large grain sizes and a decreased population density of the finer grains leading to roughly convex upward trends (Figure  4c ). Regression lines were calculated using at least three data points and taking a best fit quality of Q > 0. 1 into account, as suggested by the CSDCorrections software  (Higgins, 2000)  for an acceptable regression quality. While the linear regression MOCK ET AL. 10.
1029/2021JB021864 13 of 27 includes the maximum population density at small grain sizes within most of the gabbros of the AML, VG/FG TZ, and FG units, it is restricted to the larger crystal sizes in the LG unit and the MTZ. The slopes of the regression lines as well as their intercept with the y-axis were plotted for every sample (Figures  4a  and 4b ). Marsh (1988)   (Marsh, 1988). Orange diamonds are used for calculating the regression line (dashed line). Sample name and corresponding crustal unit are given beside each plot. h, height above base of the MTZ, s, slope, I, y-axis intercept, Q, quality of the regression given by the CSDCorrections software  (Higgins, 2000) ; Q max = 1, Q > 0. 1 is acceptable. Lithological units are given at the right. Dotted horizontal lines mark transitions between lithological units.
Pole Figures and Symmetry Representative pole figures for plagioclase and olivine CPO are shown in Figures  5a  and 5c  compilation of all pole figures is available in the PANGAEA database  (Mock et al. , 2020b). The trace of the foliation, corresponding to the preferred orientation of plagioclase (010), is plotted on pole figures of every phase. Plagioclase shows point maxima of (010) along the entire section, mostly correlated with point to girdle distributions of  [100]. For quantifying the CPO symmetry, we used BA and BC for plagioclase and clinopyroxene, respectively (Figure  5b ). Distinct trends along the crustal section are observed for both phases: the upper 1,500 m of the section are dominated by more or less constant indices with an intermediate symmetry in the section from 5,000 down to 3,500 mam (BA ≈ 0.
Discussion As summarized in the introduction, apparently contradictory conclusions in several published studies lead to a still on-going debate about the lower fast-spread oceanic crust accretion mechanism(s). Our results obtained in Wadi Gideah provide an opportunity to test the accretion models presented in the introduction in terms of consistency with the microstructural features of the primary phases plagioclase, clinopyroxene and olivine. Magmatic deformation is the most dominant type of deformation in our profile. Low values (≤1°) for the mean angular deviation in all samples emphasize the high data quality, and the relatively high spatial resolution (one sample every 81 m on average) is unique for an entire crustal profile. Compared to previous fabric studies, the high density of our data allows us to identify trends along the crust that were not documented before.
Acknowledgments The authors wish to thank  J. Feige ,  C. Nevado , and  D. Delmas  for careful thin section preparation. F. Barou  is acknowledged for helpful assistance during EBSD measurements. The authors thank  Maël Allard  and  David Mainprice  for their help with MTEX. We acknowledge  Michael Denis Higgins  for providing the CSDCorrections 1. 60 software we used to calculate crystal size distributions. The support of the  Sultan Qaboos University  ( Prof. Sobhi Nasir ), as well as the friendly cooperation established in Oman with the  Public Authority for Mining (Department of Geological Research ,  Dr. A. Al.
Critical care clinicians are trained to analyze simultaneously multiple physiological parameters to predict critical conditions such as hemodynamic instability. We developed the Multi-task Learning Physiological Deep Learner (MTL-PDL), a deep learning algorithm that predicts simultaneously the mean arterial pressure (MAP) and the heart rate (HR). In an external validation dataset, our model exhibited very good calibration: R 2 of 0. 747 (95% confidence interval, 0. 692 to 0. 794) and 0. 850 (0. 815 to 0. 879) for respectively, MAP and HR prediction 60-minutes ahead of time. For acute hypotensive episodes defined as a MAP below 65 mmHg for 5 min, our MTL-PDL reached a predictive value of 90% for patients at very high risk (predicted MAP ≤ 60 mmHg) and 2‰ for patients at low risk (predicted MAP >70 mmHg).
Datasets The Medical Information Mart for Intensive Care version 3 (MIMIC-III) is a publicly and freely available database including clinical data, physiologic measurements, treatment administration and administrative data of ICU patients. The dataset comprises de-identified data from patients admitted to any of the five ICUs of Boston's Beth Israel deaconess medical center (BIDMC, Boston, USA) for a period of seven years (2008-2014)  [28]. A unique identifier number was attributed to each patient to match information available in the different tables. Data collection was approved by the institutional review boards (IRB) of BIDMC and the Massachusetts institute of technology (MIT, Cambridge, Massachusetts, USA). We specifically used the MIMIC-III waveform matched subset database (version 1.
0), which contains 22,247 numeric records (recording of physiological signals every minute) matched and time-aligned with 10,282 MIMIC-III clinical database records (global clinical information about the ICU stay)  [29]. The Lariboisière cohort was used for external validation. This database includes clinical data, physiological signals, treatment and administrative data prospectively consecutively collected at the bedside over a two-year period (2017-2018) in the surgical ICU of Lariboisière hospital (AP-HP, Paris, France). This cohort was notably built to be similar in structure to MIMIC-III. This study received IRB approval (CE-SRLF 14-356), and signed informed consent was waived  [30]. Every patient was orally informed about inclusion in this database.
e. , medical, surgical, cardiac, mixed), time-evolving treatment characteristics (including mechanical ventilation, vasopressors, and sedation), as well as the five following physiological signals collected every minute: HR, pulse oximetry (SpO 2 ), systolic arterial pressure (SAP), diastolic arterial pressure (DAP) and MAP. Patients or periods with missing clinical information (baseline characteristics, time-evolving characteristics, severity scores, time-evolving treatments) were excluded. Finally, only patients with at least one period with available data for the 5-min time gap window were included in the analyses.
Sampling periods and predicting on new patients As the length of ICU stay was different for each patient, our data were uneven in terms of the number of periods per patient, creating two types of challenges: i. The model should not overfit to patients with more data, i. e. more periods ii. The model should be aware of the correlation between periods coming from the same patient To address (1), at the beginning of each iteration of the learning process (i. e. , epoch) a new balanced dataset was obtained by sampling with replacement the same number of periods per patient. The median number of periods per patient included in the study was used to determine the number of periods to be drawn per patient. Models were trained with balanced datasets for multiple epochs. Thus, at each epoch, a different sample of the data was used; consequently, all the data were used during the training process.
Determining both can optimize the learning of F by averaging F across the different noise processes  [42]. In addition, focusing on one task carries the risk of overfitting while learning to predict MAP and HR values jointly is associated with increased generalizability  [50]. This was confirmed in the present study, where we were able to integrate MTL to jointly predict MAP and HR up to 60 min in advance and found high calibration and accuracy even when tested in an external dataset. In most studies on hypotension prediction in the ICU, AHE is defined as a binary status based on a single MAP threshold. This binary approach carries some limitations. First, definitions are often heterogeneous across studies. Second and most importantly, a definition based on a single cutoff value may not be suitable for individual patients.
According to Le Cun et al. [39] , this is the most optimal way to encode temporal information about the entire patient ICU stay since it preserves the longitudinal changes and the original time-dependent order in patient physiological signals. Our study carries some limitations. Although appealing, our results will need to be confirmed in a larger validation set. Indeed, the external validation dataset was relatively limited in size. Real-life data from   bedside monitors and electronic medical systems are prone to missing values, errors and artifacts, adding significant noise to the data  [52]. In this study, we only included patients with complete data and particularly complete physiological time-series. However, missingness is likely to be informative in some ICU patients. Therefore, our algorithm may lack generalizability to patients presenting a lot of missing data.
48t. MTL-PDL was associated with more accurate predictions as illustrated by an average difference between observed and predicted MAP consistently closer to zero. In MIMIC-III validation set, the average difference between observed and predicted MAP for each time gap was of 0. 59 [95% LOA = -7. 48-8. 67], -0. 06 [-9. 34-9. 22], 0. 85 [-8. 54-10. 24], 1. 52 [-10. 15-13. 2], 1. 69 [-12. 52-15. 9] for STL-PDL, while it was of 0. 28 [-7. 41-7. 97], 0. 12 [-8. 54-8. 77], -0. 27 [-9. 59-9. 06], -1. 26 [-12. 89-10. 36], -0. 26 [-14. 73-14. 2] for MTL- PDL. The average difference between observed and predicted MAP were larger in the external validation cohort than in the MIMIC-III validation dataset, but MTL-PDL was also superior to STL-PDL: average difference for the STL-PDL was 2. 81 [-9. 47-15.
10 replicates (n = 10, as in the osmoregulation capacity measurements), the rearing of more than 10,000 larvae was required (n = 10 implies rearing more than 10 × 125 = 1,250 individuals per treatment; 1,250 × 8 = 10,000 larvae). Since we were not able to rear such a large number of larvae simultaneously, we used larvae harvested from four different females and took 3 replicates per female per treatment (all females together: n = 12 per treatment, 12,000 larvae were sampled). We are aware of the variation among females in the responses to temperature, salinity, and other stressors  (Spitzner et al. , 2019; Torres et al. , 2020) ; therefore, we maintained the factor "Female" as a random factor in our statistical analyses and present the averaged values of all females as well as the data discriminated by female (see Results).
Templates   S5  for details). Quantitative PCRs (in duplicate plates as technical replicates) were run on an Applied Biosystems StepOne Plus™ machine using the following cycling parameters: 95°C for 2 min followed by 40 cycles of 5 s at 95°C and 30 s at 60°C. PCR amplification factor and efficiency were determined using the qPCR Efficiency Calculator Thermo Fischer Scientific (See Table  S6  for details) with slopes calculated (GraphPad software) from the calibration curves performed with cDNA from gill tissues; standards were accepted only when amplification efficiency was >90%. Relative expression of target genes (CamaNaK and CamaCOT) was determined against the values from freshly hatched larvae as control using the 2 -∆ ∆Ct method  (Livak & Schmittgen, 2001; Pfaffl, 2006)  modified to consider different amplification efficiencies and multiple reference genes  (Hellemans et al.
| Statistical analyses The OC data (Experiments 1 and 2) were analyzed through 3-way factorial ANOVA  (Underwood, 1997)  with temperature, acclimation salinity, and test salinity as factors. Preliminary tests showed that residuals approached normal distribution and variances were homogeneous (Cochran test). For the mRNA expression data (Experiment 3), we used mixed modeling  (Zuur et al. , 2009)  in order to control for the random variation associated with female of origin. In that case, analysis was based on generalized linear squares modeling using the lme function of the package nlme  (Pinheiro et al. , 2018) , implemented in R (R Core Team, 2013). Thus, for the mRNA expression data, female of origin was considered a random factor in addition to the acclimation temperature and salinity used as fixed factors in a factorial design.
In this study, we aimed to evaluate the expression of HPV16 genes at a genomic and transcriptomic level, using a qPCR assay. This helps to understand, which genes of HPV16 genome are present in the tumor at the DNA level, and which of them are transcribed in HNSCC. The data were related to the presence of IgG, IgA and IgM antibodies against the HPV16 E2 and E7 antigens, as determined by ELISA. We used paired serum samples before and after treatment, to measure IgG and IgA antibody response to HPV16 E2 and E7 to determine whether treatment influences the antibody response and how the removal of the cancer as the source of antigen affects antibody levels over time. Additionally, we assessed the TIL status for those tumors and related this to the HPV16 expression and antibody responses.
qPCR for HPV16 Quantitative polymerase chain reaction (qPCR) was performed (GoTaq qPCR systems assay, Promega, Southampton, UK) using 1 µl each of DNA (10ng) and 1 µl of cDNA. Samples were loaded onto 384 well plates in triplicates, read on the Applied Biosystems 7900HT workstation and analyzed using SDS 2. 3 software (Thermo Fisher, Waltham, MA, USA). The qPCR settings on the workstation were adjusted according to the manufacturer's protocol for the GoTaq ® assay. Published primer pairs for all HPV16 genes used are shown in Table 1  (19) (20) (21) (22) (23) (24) (25) (26). Primer pair annealing to the HPV16 genome NC_001526. 4 was confirmed using Primer-BLAST  (27, 28). Primer pairs were confirmed in silico to be specific for HPV16 and to not bind to other high-risk virus genomes. Specificity was verified using NCBI without detecting any unintended templates of common human viruses (taxid: 10239).
HPV16 Viral Genome Level Analysis RNA was extracted from snap-frozen tissue of four patients (Maxwell ® RSC simplyRNA Tissue Kit(AS1340), Promega, Southampton, UK). RNA concentration and quality were analyzed using the RNA Nano Kit for the 2100 Bioanalyzer System (Agilent Technologies, Santa Clara, CA, USA). RNAsequencing was undertaken by Edinburgh Genomics (University of Edinburgh, Edinburgh, UK). An automated TruSeq stranded mRNA-seq library preparation from total RNA and the NovaSeq sequencing-system was used (100 bp paired-end; 1,750M+1,750M reads, Illumina, San Diego, CA, USA). This RNA sequencing dataset was generated in an independent collaboration with Transgene, and the fact that Transgene funded the RNA sequencing for four cases of the study, did not influence the study design, execution, and results interpretation.
ELISA A standard ELISA was performed in triplicates including a standard curve and negative control. Plates were coated with GST HPV16 E2 and E7 with a working concentration of 2µg/mL at 4°C overnight. Total IgG (goat anti-human (Fc), HRP, A0179, Sigma, St. Louis, MO, USA), IgA (Goat anti-human IgA (Heavy chain), HRP, PA1-74395), and IgM (Goat anti-human IgM (Heavy chain), HRP, A18835, Thermo Scientific, Waltham, MA, USA) were used as secondary antibodies. The Multiskan FC Microplate Photometer was used to analyze the plates, and OD values were exported using the SkanIt software (Thermo Scientific, Waltham, MA, USA). A standard curve was generated by seven steps of doubling dilutions starting at a concentration of 1:100 using a pool of positive sera. Antibody titers were measured in arbitrary units (AU) using this standard curve.
47) and high (r=0. 70) correlation value respectively. Whereas the E5/E6 correlation was low and not statistically significant. We quantified the HPV16 viral transcripts in 4 cases (15, 16, 23, and 27) using RNA-sequencing and compared this to the qPCR results. The 40-DDCt values assessed by qPCR for DNA and RNA are displayed together with the HPV16 genome alignment using RNA-sequencing data in Figure  2. Consistent with data from qPCR, case 27 did not have any expressed HPV16 genes detected by RNA-sequencing either (previously identified as HPV16 neg ). The other three cases showed high amount for all HPV16 genes and RNA transcripts assessed by qPCR as well as by using RNA-sequencing alignment.
Correlation of IgG and IgA Antibodies to HPV16 E2 and E7 Total IgM, IgG, and IgA antibody responses to E2, E6, and E7 antigens were evaluated using ELISA. E6 protein showed unreliable and inconsistent results, most likely due to problems in protein folding in the protein expression system (data not shown). 3/73 pre-treatment samples (4%) contained strongly detectable IgM antibodies against E2 and E7 simultaneously, perhaps reflecting recent re-exposure of HPV16. Interestingly, the HPV neg case 25 had low levels of circulating E2 IgG (AU=0. 2) and E7 IgA (AU=0. 8), likely reflecting an unrelated, cleared infection with HPV16. The other two HPV neg cases (26 and 27), had no detectable antibodies to E2 and E7. Of the remaining 70 pre-treatment serum samples, 46 were positive for E2 IgG, 56 for E7 IgG, 28 for E2 IgA, and 63 for E7 IgA.
ACKNOWLEDGMENTS We would like to thank following groups for their contribution to this project: The  Research Histology Team  ( University of Southampton ) for performing the immunohistochemistry, the  Core Protein Facility  ( University of Southampton ) for expression and purification of the  HPV16 E2  and  E7  proteins. We would also like to thank  Transgene  for funding the RNA-sequencing. RNA-sequencing was carried out by  Edinburgh Genomics ,  The University of Edinburgh. Edinburgh Genomics  is partly supported through core grants from  NERC  ( R8/H10/56 ),  MRC  ( MR/K001744/1 ) and  BBSRC  ( BB/J004243/1 ).
Data obtained at PJ7 for the GRS structure was compared to the background signal at that latitude by calculating the median of the brightness temperatures obtained for PJ1,3,4,5,6,8 and 9. The offset due to the GRS was calculated from the difference between these values. The corresponding uncertainty was estimated from the standard deviation of the background during the multiple perijoves (except PJ7). The results are listed in Table  S3. The GRS is a very old, extended and complex vortex. We focus on a representative region of the anticyclone, however other latitudes do not show a similar inversion in the brightness temperature. This implies powerful circulation inside the vortex. Tables S1 to S3 list brightness temperature anomalies for the 6 MWR frequencies of each vortex, compared to the background longitudinal averages.
Acknowledgments We thank  T. Fujiyoshi ,  J. Sinclair  and  T. Momary  for help in acquiring the Subaru data. Funding: SJB, JHW, SML, JA, STB, SG, GO and HB were funded by the  National Aeronautics and Space Administration. A portion of this research carried out at the  Jet Propulsion Laboratory, California Institute of Technology , was performed under contract  80NM0018D0004  with the  National Aeronautics and Space Administration. JL was funded by the  Juno Project  through a subcontract with the  Southwest Research Institute. TG was funded by the  Centre National d'Etudes Spatiales. LNF was supported by a  Royal Society Research Fellowship and European Research Council   Consolidator Grant  (under the European Union's  Horizon 2020 research and innovation programme , grant agreement No  723890 ).
Data and materials availability: The MWR data are available from the Planetary Data System archive (https://pds. nasa. gov) as 'Juno Jupiter MWR reduced data records v1. 0' (dataset JNO-J-MWR-3-RDR-V1. 0). The reduced HST and Gemini data are available at https://doi. org/10. 17909/T94T1H. The raw HST data are available at https://archive. stsci. edu/hst/search. php under Proposal ID 15665 and raw Gemini data at https://archive. gemini. edu/searchform under Program ID GN-2019A-Q-202. The model results are available at ?? The code used for the Monte Carlo analysis is available at ??.