dataesr/scientific-paragraphs-categorization huggingface dataset:
Supplementary Materials: The following are available online at https://www. mdpi. com/2072-6694/11/6/795/s1, Figure  S1 : Heatmap of the correlations of the subtype centroids in four public omic datasets: Chung et al. [5] , Walter et al. [6] , TCGA  [7]  and Keck et al. [8] , Figure  S2 : Boxplots of the IC 50 of Gefinitinib (A), Cetuximab (B), Erlotinib (C) and Lapatinib (D) on basal-like and non-basal-like HNSCC cell lines, determined from the Garnett  [20]  (A,B) and CCLE (C,D)  [21]  datasets, Figure  S3 : Heatmap of the differentially expressed genes between cell lines of the basal and non-basal subtypes in the CCLE  [21]  and GDSC  [20]   S1 : List of markers found to be up-and down-regulated in basal HNSCC tumor samples, Table  S2 : List of the 25 HNSCC cell line, Table  S3 : List of the 26 clinically relevant cancer mutated genes analyzed in the 25 HNSCC cell lines, Table  S4 : Results of Next Generation Sequencing of a panel of 26 clinically relevant cancer genes in the 25 HNSCC cell lines.
public datasets, Figure S4: Correlation matrix of the AUC obtained upon treatment with Afatinib, Erlotinib and Cetuximab, Figure S5: Cell viability dose-response curves obtained on BHY (upper panels) and KYSE-510 (lower panels) cells following 96 h of treatment with Cetuximab (left panels), Afatinib (middle panels) and Erlotinib (right panels), Figure S6: Analysis of AREG and EREG gene expression in BHY and KYSE-510 cell lines, Figure S7: Analysis of the response of the 25 HNSCC cell lines to treatment with Gedatolisib, Trastuzumab and Cobimetinib, Figure S8: Boxplot of the Bliss synergy scores for the 25 cell lines treated with the nine drug combinations, Table.
Acknowledgments: This work is part of the  Cartes d'Identité des Tumeurs (CIT) program  from the  Ligue Nationale Contre le Cancer. We are grateful to  Jacqueline Godet  and  Mira Ayadi  for their invaluable contribution to this project. We thank  Pierre Henault ,  HoGeun Joon  and  Francis Brasseur  for providing cell lines, and  Arnaud Kopp  for his assistance with drug data analysis. Tumor samples were provided by  Guy Bronner  and  Guy Flesch.
Sequencing and identification of milkfish NKA α-subunits Genes coding for NKA α-subunits were derived from the milkfish transcriptome database  (Hu et al. , 2017). They were confirmed through RT-(reverse transcription) or RACE-(rapid amplification of cDNA ends) PCR (polymerase chain reaction) and then sequenced by the Tri-Biotech (Taipei, Taiwan). To characterize NKA proteins, the amino acid sequences were deduced from sequenced genes using the NCBI Open Reading Frame Finder (https://www. ncbi. nlm. nih. gov/orffinder/). Subsequently, the transmembrane domains were predicted using the MEMSAT-SVM provided by the PSIPRED protein structure prediction server (http://bioinf. cs. ucl. ac. uk/psipred/)  (Buchan et al. , 2013) , and comparisons were made with transmembrane domains of teleostean NKA including the Senegalese sole (Solea senegalensis;  Armesto et al.
Quantitative real-time PCR (QPCR) The methods, including PCR protocol and data calculation, were described previously  (Chang et al. , 2016; Hu et al. , 2017). Expression levels of target genes were quantified using 2× FastStart Universal SYBR Green Master (04913850001; Roche, Mannheim, Germany) with the MiniOpticon real-time PCR system (Bio-Rad). Then, mRNA values for the target genes were normalized using the expression of β-actin mRNA from the same cDNA sample  (Tang et al. , 2010). Primer information used is listed in Table  S2. The amplification efficiency was similar for all primer pairs used in this study (range: 0. 93-1. 08). To confirm the specificity of these amplifications from each test sample, sequencing, melting curve analysis, and electrophoresis were carried out. For each unknown sample, relative fxyd gene expression was obtained using the formula 2^-[(Ct target genes, n -Ct β-actin, n ) -(Ct target genes, c -Ct β-actin, c )], where Ct corresponds to the threshold cycle number  (Livak and Schmittgen, 2001).
Statistical analysis All values are shown as the mean ± standard error (SE). The Shapiro-Wilk normality test was applied. Because some of the data were not normally distributed, non-parametric tests were used. Differences in the mRNA levels between groups were analyzed via the Mann-Whitney U test or Kruskal-Wallis test for pair-wise or multiple comparisons, respectively. All analyses were performed using SPSS 20 software (SPSS, Chicago, IL, USA), and P values of < 0. 05 were considered to be significant.
6E ). Meanwhile, in both control groups (transfer from SW to SW and from FW to FW), no difference was found among time-points after transfer. Fig. 2. Phylogenetic analysis of putative amino acid sequences of NKA α-isoforms using the maximum likelihood method. The numbers at the forks indicate the bootstrap values (%). Sea urchin NKA α was used as the outgroup for the root tree. Accession numbers are listed in Tables  S1  and S2. Table  1  Percentage of cDNA (open reading frame only; above diagonal) and amino acid (below diagonal) sequence identity among milkfish Na + /K + -ATPase (CcNKA) α-subunits.
In mammals and other teleosts, nka α2 is expressed predominantly in the muscle  (Armesto et al. , 2014; Bollinger et al. , 2016; Richards et al. , 2003; Suhail, 2010). Similar to results found in other animal species, Ccnka α2 mRNA was present primarily in milkfish Accession numbers are listed in Tables  S1  and S2. Table  3  Amino acid identity (%) among NKA α2and α3-isoforms. Accession numbers are listed in Tables  S1  and S2. muscle in our preliminary study (data not shown). In particular, the tissue distribution patterns of Ccnka α3, which was found mainly in the brain and liver, were different from those of other teleosts. Some teleostean nka α3 are expressed predominantly in the brain alone, as in the Senegalese sole (both α3a and α3b;  Armesto et al. , 2014). Other teleostean nka α3 are ubiquitously expressed in various organs, as in the Mozambique tilapia (Oreochromis mossambicus; α3;  Feng et al.
Acknowledgements This study was financially supported in part by the  Ministry of Science and Technology (MOST) ,  Taiwan , through the  MOST Research Project  ( MOST-106-2313-B-005-038-MY3 ) and the  MOST-CNRS (France)   International Project for Scientific Cooperation  ( MOST-105-2911-I-005-501 ) grants to T. H. L. This work was also financially supported in part by the  iEGG and Animal Biotechnology Center from The Feature Area Research Center Program  within the framework of the Higher Education Sprout Project by the  Ministry of Education (MOE) ,  Taiwan  ( MOE-107-S-0023-F)  to T. H. L. W. K. Y. was supported by postdoctoral fellowships from  MOST  ( MOST-106-2811-B-005-004 ) and  MOE  ( MOE-107-S-0023-F ).
At higher latitudes, the Mekong River is too steep and narrow, causing the loss of many data, as the altimeter tracking loop remained locked on the top of the hills (see Reference  [36]  for a detailed explanation). As a consequence, ENVISAT altimetry does not perform well over the river, with very noisy estimates, and it is impossible to properly extract the variations of water level of the Mekong River. The period 2003-2009 was chosen as it covers the years for which all datasets used in this study are overlapping in time. Details of satellite and ancillary datasets used in this study are described in Section 2. In Section 3, the methodology to estimate the monthly variations of SWS and SSWS, including groundwater and soil moisture, for the 2003-2009 period, is presented. Results, comparisons, and discussions are presented in Section 4.
ENVISAT Satellite Altimetry Data The ENVISAT mission was launched on 1 March 2002 by the European Space Agency (ESA). It carried 10 instruments, including the advanced radar altimeter (RA-2). It was based on the heritage of the sensor on-board the ERS-1 and 2 satellites. It orbited at an average altitude of 790 km, with an inclination of 98. 54°, on a sun-synchronous orbit with a 35-day repeat cycle. They provided observations of the Earth surface (ocean, land, and ice caps) from 82. 4°latitude north to 82. 4°latitude south. This orbit was formerly used by the ERS-1 and 2 missions, with an equatorial ground-track spacing of about 85 km. RA-2 was a nadir-looking pulse-limited radar altimeter operating at two frequencies at Ku-(13. 575 GHz), as ERS-1 and 2, and S-(3. 2 GHz) bands  [38]. ENVISAT tracks over the LMB are shown in Figure  1 -right, along with locations of 45 virtual stations (VS) used in this study for monitoring of the surface water height.
Surface Water Storage from the WaterGap Global Hydrology Model The WaterGAP Global Hydrology Model (WGHM) is a submodel of global water use that computes groundwater storage, total water storage, baseflow, groundwater recharge (diffuse and below surface water bodies at a spatial resolution of 0. 5°  [45, 46]. WGHM is based on the best global datasets currently available. For comparison with our multisatellite-based results, outputs from the WaterGap 2. 2a model are used to compute monthly variation of the land surface-and subsurface-water storage within the LMB (area shown in Figure  1 -right). WGHM model outputs are available at https://www. uni-frankfurt. de/49903932/7_GWdepletion.
Land Surface-Water Extent Mapping Monthly surface-water extent maps at 500 m spatial resolution of the study area can be constructed based on a methodology introduced by Sakamoto et al. (  2007 )  [47]  that was specifically designed and developed for tropical regions like the Mekong basin  [3]. This methodology uses low values of water indices as main indicators for surface-water presence, and it has been used in previous studies over the LMB  [3, 5]. Here, we present a quick summary of the methodology. First, the Enhanced Vegetation Index (EVI), the Land Surface-Water Index (LSWI), and the Different Value between EVI and LSWI (DVEL) are calculated at pixel level, from the original atmospherically corrected surface reflectance data derived from the MOD09A1 eight-day composite products. The EVI and the LSWI are obtained using Equations (  1 ) and (2), while the DVEL is calculated by Equation (3): and with Landsat-derived inundated maps at the 10 km grid level (derived from the Normalized Difference Water Index (NDWI) with a threshold of 0.
Interpolated Surface-Water Level Maps Monthly maps of surface-water level of the study area can be estimated by combining monthly satellite altimetry data at all 45 ENVISAT VSs and MODIS-derived inundated maps. At a given month, satellite altimetry data at the VSs are linearly interpolated over the inundated areas (from the corresponding MODIS-derived surface water extent map described in Section 3. 1) to estimate the surface-water level at each grid point of 500 m spatial resolution following the approach developed by  Frappart et al. (2006)   [33] , and applied to MODIS-based inundation products  [34, 35]. Monthly maps of surface-water level of the study area at 500 m spatial resolution can be constructed for the 2003-2009 period.
40°N, 104. 20°E), was compared to the variation of the in situ water level at the Tan Chau station for the 2003-2007 period (Figure  4 -top panel). As expected, a high linear correlation of 92% was found between the two independent datasets. Comparisons between the same satellite-based water level and in situ water level (provided by the MRC) at other VSs in the LMB for the 2008-2016 period can be found in Reference  [3] , where they showed very good agreements between the two datasets. In addition, a high linear correlation of 92% was also found when comparing time series of the MODIS-derived surface-water extent (Figure  3 ) and time series of surface-water level at VS 14 (Figure  4 ) over the common period  (2003) (2004) (2005) (2006) (2007) (2008) (2009). This high correlation confirms the consistency between these two highly related components.
A very high linear temporal correlation is observed (96%) when comparing these two datasets during a seven-year period, as expected. Low and high peaks of these two components agree very well for all the study period. The mean annual amplitude of SWS in the study area amounts to ∼21% of the total volume of water that flows out of the Mekong River annually. Disaggregation of GRACE-derived TWS into satellite-derived SSWS variations is then carried out following Equation (  5 ) and using the new SWS estimates. Monthly estimates of water storage in the three different hydrological reservoirs are computed for the entire period and are shown in Figure  7. Firstly, it compares monthly variation of SWS to the variations of TWS derived from GRACE data. A linear temporal correlation of 95% is observed that concludes to a similar dynamics and seasonal variations from peak to peak between the two parameters during the 2003-2009 period, with positive variations observed from May to October and negative variations between November and April.
Conclusions and Perspectives In this study, monthly variation of SWS within the LMB (located in Cambodia and Vietnam) was estimated for the first time during the 2003-2009 period by combining MODIS imagery and ENVISAT altimetry data. MODIS atmospherically corrected land-surface reflectance eight-day Level 3 products (MOD09A1) were used to create monthly surface-water extent maps at 500 m spatial resolution in the study area, mainly based on the methodology introduced in Reference  [47]. ENVISAT altimetry data at 45 satellite VSs were used to estimate monthly variation of the surface-water height at these locations. Then, linear interpolation was applied to estimate monthly surface-water height maps at similar spatial and temporal resolution as MODIS-derived surface-water extent maps. Finally, monthly variation of the SWS in the region was estimated as the product of the variations of the surface-water extent and surface-water height, following the approach described in References  [18, 30, 35, 40].
The variation of the SWS was validated by comparing with the variation of in situ discharge of the Mekong River and the variation of the TWS derived from GRACE data, showing very high linear temporal correlation of 96%, and 95%, respectively. Monthly variation of the SSWS of the region was also estimated for the first time by removing the variation of the SWS from the variation of the of TWS. Results showed that the variation of the SSWS corresponds to 55-60% of the variation of the TWS of the LMB located in Vietnam and Cambodia. Results from this study could also be used to improve hydrological models (e. g. , the WGHM) at a regional scale. Extension of this product from 1995 until the present time requires to use longer time series of inundation maps, water level, and TWS. Long-term inundation maps can be obtained, for instance, from GIEMS  [28, 29]  or GIEMS-D3  [60]  datasets from multisource observations, or from the Landsat instrument  [61].
Water levels can be obtained from other altimetry missions, e. g. , European Remote-Sensing Satellite-2 (ERS-2, 1995-2003), SARAL (2013-2016), and Sentinel-3A and -3B (in operation since 2016 and 2018). Since GRACE mission data are only available from 2003, an attempt could be done to estimate the TWS from indirect measurements. Such an approach was used, for instance, in Reference  [62]  to extend back in time the GRACE data over the Mississippi basin using: (1) terrestrial water-cycle budget closure, and (2) precipitation, evapotranspiration, and runoff measurements. This approach can be extended to the global scale  [63] , and current efforts are focusing on the Asian region. Other GRACE solutions (like the global mascons solutions from JPL, GSFC, or CSR) could be evaluated to investigate the sensibility of our results to these different solutions.
Tolerance to telomerase can readily be broken both naturally and experimentally  [6]. We have developed a powerful hTERT recombinant DNA plasmid (INVAC-1) that elicited cell mediated memory responses and curtailed tumor growth in mice yet was devoid of any immortalizing activity  [7]. Another group has demonstrated similar findings using a secreted hTERT construct  [8]. Both studies have led to ongoing phase I clinical trials (NCT02301754 and NCT02327468). TERT is also overexpressed in the majority (>90%) of canine cancer cells regardless of their origin  [9]  and there is a good correlation between TERT expression and telomerase activity in dog tissues  [10]. Accordingly like its human counterpart, cTERT is considered to be a near universal tumor associated antigen (TAA)  [11]  justifying its use in clinical immunotherapy as a treatment for dog cancers  [9].
RESULTS pDUV5 design and characterization We generated a cTERT construct based on the same principles as the INVAC-1 telomerase plasmid in human phase I trials (NCT02301754  [7] ). Briefly, the construct carries two safety features, notably deletion of the nucleolar localization sequence (NoLS) and deletion of the catalytic VDD triplet leading to an inactive form of the enzyme. To facilitate processing by the proteasome, cTERT was fused to ubiquitin following the so-called N-end rule (destabilizing residue at N-terminal position) which orients degradation through the ubiquitin-dependent proteasome pathway and subsequently for antigen presentation by the MHC class I  [16]. At flu epitope and the V5 tag were added to the C-terminus allowing the expressed fusion protein to be characterized, there being no cTERT specific antibodies available.
The pcDNA3. 1 based plasmid is referred to as pDUV5 (Figure  1A ). Canine TERT-V5 protein expression was assessed by Western blotting 24 hours after pDUV5 transfection of HEK293T cells. As can be seen from Figure  1B  a single band was identified which corresponds well with the expected molecular weight of 130 kDa. When analyzed by immunofluorescence, overexpressed recombinant cTERT was mainly localized in the cytoplasm and nucleus but not in the nucleolus, which contrasts with normal human TERT (Figure  1C ) that is found there in abundance in the nucleolus  [17]. To confirm inactivation of cTERT, telomerase activity was determined in transfected CRFK cells using the telomeric repeat amplification assay. Relative telomerase activity (RTA) data derived using pDUV5 transfected cell extracts showed that cTERT was completely devoid of telomerase activity (Figure  1D ).
DISCUSSION Paralleling its human counterpart currently in clinical trials (NCT02301754), a ubiquitin-canine TERT DNA construct was synthesized given that ubiquitin-fused DNA vaccines have showed a significant improvement of the antigen specific cellular immune response  [22]. Although tagged by ubiquitin, overexpression was so strong that some recombinant cTERT made its way to the nucleus. However, it was excluded from the nucleolus unlike wild type TERT, which is most abundant in this compartment  [17]. As the TRAP assay showed the fusion protein to be totally devoid of enzymatic activity, in keeping with the role of the VDD amino acid triplet in telomerase function  [23] , there is no danger of recombinant telomerase immortalizing target cells. DNA vaccination per se alone is known to be poorly immunogenic requiring electrogene transfer to greatly increase DNA uptake.
digimorph. org for further details). Giraffa camelopardalis (voxel size of 0. 976 mm) and Camelus dromedarius (voxel size 0. 804 mm) were scanned in a medical CT (Siemens Sensation 64 scanner) at St. Vincent's Hospital, Melbourne, Australia, and Dicerorhinus sumatrensis was scanned in a medical CT at Yxlon facilities in Hamburg, Germany using a Y. CT Modular (YXLON International GmbH) equipped with a Y. TU450-D11 x-ray tube (voxel size of 0. 23 mm). CT datasets were imported to the software Avizo 7. 0 (FEI-VSG Company). The scans were manually segmented and a surface model was reconstructed with multiple materials. Molar teeth (M1, M2 and M3) were reconstructed as a separate material from the mandibular bone, as they bear the highest functional load in comminution  [63]. Molars were then included in the models as auxiliary geometry to apply the force in the bite point.
During this step, irregularities in the surface resulting from model generation during scanning were repaired using Rhinoceros 5. 0 software (McNeel & associates) refinement and smoothing tools. The segmented models were converted to a CAD models following Marce-Nogueet al. [64]. A CAD model is a vector-graphic standard for three-dimensional design used in mechanical design and engineering which does not involve any tetrahedralization of the geometry such as stereolithographic formats. The CAD-step allowed us to create the mesh of finite elements in ANSYS FEA Package. All models were standardized and oriented in anatomical position with maxilla and mandible articulated to facilitate comparison.
Model properties A structural static analysis was performed to evaluate the biomechanical performance of the mandible during biting using the finite element package ANSYS 17. 1 (Ansys, Canonsburg, USA) and a Dell Precision Workstation T7910 with 256GB. Our primary interest was the comparison of von Mises stress distribution throughout the mandible under loading conditions defined by masticatory movement. The von Mises criterion is the most accurate value for predicting fracture location when isotropic material properties are applied to cortical bone  [65]. For this study, elastic, linear and homogeneous material properties were assumed for bone tissue. Although the specific mechanical properties of bone for the investigated individuals are unknown, Gil et al. [66]  demonstrated that the use of general values for bone does not alter results to an extent that a relative comparison between models is inappropriate.
For non-uniform meshes (where different elements have different sizes), new statistics, which take into account non-uniformity such as the mesh-weighted arithmetic mean (MWAM)  [57]  have been proposed. The phylogenetic signal was estimated for the biomechanical data using a mathematical generalization of the K-statistic appropriate for multivariate data (i. e. Kmult)  [78]. For the mandibles, the MWAM, and the stress quartile values given in the boxplots, the percentage error of the arithmetic mean (PEofAM) and the percentage error of the median (PEofM) (described in Marce-Nogueet al. [57] ), as well as the values needed to calculate both percentage errors, can be found in Supplementary information (S1 and S2 Tables) All analyses were performed in Matlab R2017a (Mathworks, Massachusetts, USA).
[58]  (S1, S2, S3, S4, S5 and S6 Figs and S3 Table of the supplementary information). Although fewer intervals could have been be used for the orthal cases, we decided to standardize all the analysis to N = 100, which is suitable for both orthal and lateral cases. The values of each interval when N = 100 for each specimen and for each biting case can be found in the supplementary information (S1 Document). Subsequently, these new vectors for stress intervals were analysed using multivariate methods such as principal component analysis (PCA) and plotted in a biplot. All analyses were performed using Matlab (Mathworks, Massachusetts, USA) and R v. 3. 4. 4. [81]. The phylogenetic signal was also estimated for the intervals' method data using a mathematical generalization of the K-statistic appropriate for multivariate data.
Discussion: Diet-related stress patterns versus phylogenetic patterns In this study, we generated FEA models to test whether the biomechanical performance of the ungulate mandible reflects more feeding adaptations or historical contingencies. Our data include eleven species of ungulate with distinct, well-established dietary traits but belonging to two different clades, Perissodactyla and Artiodactyla. Our results reveal clear differences between Perissodactyla and Artiodactyla (Figs 2, 4 and 5). The mandibles of these taxa differ in biomechanical performance during orthal and lateral biting and in their mandibular movements during chewing and suggesting that Perissodactyla have stiffer mandibles than Artiodactyla. However, differences are also evident between taxa with distinct dietary traits.