dataesr/scientific-paragraphs-categorization huggingface dataset:
Indeed, the 8-states prediction was characterized (i) by two nonpredicted (and rare) states, namely π-helix  [7]  and β-bridge  [13] , (ii) the 310 helix, turns and bends  [9]  prediction did not gain by using MD results, being biased to prediction of α-helix for the two first and coil for the last one (data not shown). It could also underline the potentiality of improvement for these more complex predictions. Interestingly, the only case of a change in the assignment from extended to helical regions was a small region of a Plasmodium falciparum lactate dehydrogenase. The β-strands analysed with DSSP were short and highly twisted. Using STRIDE  [95]  software to assign, a methodology highly related to DSSP (i. e. 95% of identical assignment  [72] ), the region was considered as coil not β-strands.
Acknowledgments AdB thanks a lot all the reviewers who spent time evaluating this work and asked particularly relevant questions; their comments have greatly improved the manuscript. AdB would also like to thank  Professor Nenad Mitic ,  Professor Catherine Etchebest ,  Dr. Jean-Christophe Gelly ,  Dr. Tatiana Galochkina ,  Dr. Julien Diharce ,  Dr Aline Floch ,  Mr Gabriel Cretin , his former students,  Pierrick Craveur ,  Agnel Praveen Joseph ,  Tarun J. Narwani ,  Snoopy de Brevern ,  Nicolas K. Shinada , and  Akhila Melarkode Vattekatte , and the organizers and participants of Belbi'2016 (Belgrade, Serbia), International Conference on IDP'2017.
( IISER Mohali, Chandigarh, India ) and Belbi'2018 ( Belgrade, Serbia ) for fruitful discussions. This work was supported by grants from the  Ministry of Research, France, University of Paris (formerly University Paris Diderot, Sorbonne, Paris Cité ),  France ,  National Institute for Blood Transfusion (INTS) ,  France ,  National Institute for Health and Medical Research (INSERM) ,  France , and labex GR-Ex. AdB acknowledges the  Indo-French Centre for the Promotion of Advanced Research / CEFIPRA  for collaborative grants (numbers  5302-2 ). AdB acknowledges the  French National Research Agency  with grant  ANR-19-CE17-0021  (BASIN). This study was supported by grants from the  Laboratory of Excellence GR-Ex , reference  ANR-11-LABX-0051. The labex GR-Ex is funded by the  programme "Investissements d'avenir " of the  French National Research Agency , reference  ANR-11-IDEX-0005-02.
Calculations were performed on an SGI cluster granted by  Conseil Régional Ile de France  and  INTS  ( SESAME Grant ). The author was granted access to high performance computing (HPC) resources at the  French National Computing Centre CINES  under grants no. c2013037147 , no. A0010707621 , no. A0040710426  and no. A0070710961  funded by the  GENCI (Grand Equipement National de Calcul Intensif). The funding bodies have no role in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript.
MATERIAL AND METHODS First, a previous analysis of Petrobras database was made, presenting the following information: cartographic bases; lithologic (well depths, water table levels, pumping rates etc. ) and geophysical well logs (resistivity, spontaneous potential and Estimating groundwater resources of the Içá-Solimões Aquifer System in the Urucu Oil Province Central Amazon Region, Brazil, focused on a balance between availability and water demand gamma ray); lithological and geophysical oil well logs (depth data available up to 450 m). It was also analyzed the ISAS hydraulic data (hydraulic parameters: T, K, and S; geometry configuration: unsaturated and saturated aquifer thicknesses) established in previous researches performed at the GPMOB  (Galvão, 2011; Galvão et al. , 2012; Souza et al.
Lithology Effective porosity (%) The terrain elevation subtracted from its respective water table corresponds to the hydraulic head (h). The interpolation of all calculated hydraulic heads generates the potentiometric surface. For this, UTM coordinates and h values were imputed in the Surfer software, where a map of h isolines was generated by using a kriging interpolation. This primary map was subsequently adjusted in the CorelDraw software, resulting in the final potentiometric surface map (Figure  4 ).
As  Feitosa et al. (2008)  comment in the book Hydrogeology: concepts and applications, sustainable yields depend on many restrictive factors, and even variations of time, been redefined by different authors (see summarized list in chronological order of key authors and concepts in  Kalf & Woolley, 2005). Thus, it is acceptable to adopt an arbitrary percentage value of the total groundwater resources that can vary from case to case, and in time. This value can be quantified by decision makers based on a set of technical, social, economic, and database factors. In the case of the GPMOB, due to its context of hydrogeological knowledge and limited database, the arbitrary and conservative value of 20% for the ISAS total resources was adopted for an exploitation estimation until 2050 (i. e. 20% divided by 32 years from 2018).
Well Q (m 3 h -1 ) Q (m 3 day -1 = 24 hours) Q potential facility (m 3 year -1 ) Q (m 3 day -1 = 6 hours) Q (m 3 year -1 ) Estimating groundwater resources of the Içá-Solimões Aquifer System in the Urucu Oil Province Central Amazon Region, Brazil, focused on a balance between availability and water demand made for the next decades, specifically, up to 2050. As the annual consumption data covers the years between 2004 and 2018, the median growth for this period was calculated, resulting in a coefficient of 2. In other words, future effective facility rates in the GPMOB have an annual growth trend of 2%. Thus, the effective facility until 2050 would be close to 2. 7 x 10 -3 km 3 yr -1 (Table  4 ), instead 1. 4 x 10 -3 km 3 yr -1 (Table  3 ). Reevaluating the abstraction, as the sustainable yield is equal to 3 x 10 -3 km 3 yr -1 for area A, and the future effective facility for the GPMOB is 2.
7 x 10 -3 km 3 yr -1 , the future resource allocation for consumption is 90% of the ISAS sustainable yield, being considered sustainable. Considering a hypothetic situation of the use of total potential facility (5. 5 x 10 -3 km 3 yr -1 ), this value is about 80% above the ISAS sustainable yield. So, despite the abstraction results (considering the effective facility until 2050) appear to be safe, managers should recognize that yield varies over time as environmental conditions vary, especially due to current climate changes. Therefore, this work estimated resources values for groundwater management based on a potentiometric map, hydraulic parameters and aquifer geometry obtained from previous studies within the same project. However, it is known values estimated for the GPMOB should be reviewed in the future, using more updated data to confirm the growth trend coefficient, as well as the ISAS resource and sustainable yield values proposed here.
INS measurements The INS measurements were performed on the LAGRANGE spectrometer at ILL in Grenoble (France)  [30] [31] [32]. The INS spectra were collected in the 24-1020 cm -1 range, with a resolution of ΔE/E of about 2%. The spectra were recorded at 20 K to reduce the thermal effects that broaden the spectral features. The raw data were opportunely reduced to the scattering function S(ω) and plotted versus energy transfer (in units of cm -1 ). In order to ensure an easy comparison of each catalyst with its respective support, we renormalized the intensity of the former to match the same background intensity of the latter at 1020 cm -1. The analysis, described in section 3. 1, confirmed that contributions other than the background are negligible in this region. Prior to the measure, all the samples were outgassed at 120 °C in dynamic vacuum (equilibrium pressure below 10 -4 mbar) in order to remove the physisorbed water, and any further manipulation was performed within an Ar-filled glovebox in order to avoid any contamination with air moisture.
DFT simulations of the INS spectra The DFT simulations of the INS spectra were performed employing the CRYSTAL17 software  [33]  in combination with the aClimax software  [34]. A good compromise between results reliability and computational cost was found in the combination between the B3LYP hybrid functional  [35, 36]  and the 6-31G** basis set, as also proposed in the literature for similar systems  [37, 38]. Some models presented an open shell electronic configuration, for which the UB3LYP method was chosen. The general models employed for simulating the structure of activated carbons consist in polycyclic aromatic systems displaying different geometries and extensions, with hydrogen atoms used to saturate dangling bonds at edges and corners. Similar models have been already adopted in the literature to simulate the vibrational properties of the graphenic domains in carbon-based materials  [15, 24, 38].
A total of 16 regular and 6 defective H-terminated different structures are reported in this work (see Figure  3  and Figure  8 ), while a greater number of them have been effectively simulated. The geometry of all the models was fully optimized, and successively a calculation of the vibrational modes was performed. All the structures were categorized as true energy minima by the absence of imaginary frequencies. The condition for the SCF convergence was set to 10 -8 and 10 -10 hartrees during geometry optimization and frequency calculations, respectively. In order to ensure the best accuracy, very thigh values (8 8 8 25 50) have been set for the five CRYSTAL17 integrals screening tolerance. The output of the frequency calculation was used to simulate the INS spectra by employing the aClimax software  [34].
Pb*/U concordia diagrams (Fig. A1 ) and probability density plots (Figs. A2  and A3 ) were generated using the routines in Isoplot  (Ludwig, 2008). The age-probability diagrams show each age and its uncertainty (for measurement error only) as a normal distribution, and sum all ages from a sample into a single curve. Probability density plots for individual samples are presented in Figures  A2  and A3 , and compiled formation-level datasets are shown in Figure  2. For samples that yielded youngest age groups that could represent conceivable maximum depositional ages, we calculated error-weighted mean ages based on the following criteria: age clusters contained at least two overlapping concordant grains at 2σ uncertainty following  Dickinson and Gehrels (2009)  and  Schwartz et al. (2016)   (Fig.
80-66 Ma) and P2 (ca. 35-25 Ma) -that are not wellrecognized in in-situ batholith geochronology datasets  (Hervé et al. , 2007)  and extend the record of pulsed activity of arc magmatism (Fig. 2 ). In the most comprehensive summary of the Southern Patagonian Batholith magmatism,  Hervé et al (2007)  document a Paleogene phase of magmatism from 67-40 Ma, and a Neogene phase from 25-16 Ma. These detrital findings of K4 and P2 zircon populations highlight the value of the sedimentary archive in recognizing phases of magmatism not represented in available bedrock records. By ca. 26 Ma and the end of the marine sedimentation at this latitude, detrital zircons derived from the Late Jurassic Tobífera thrust sheets (Fig. 1 ), which were once a dominant sediment source to the Cenomanian-Paleocene basin, are virtually absent in the basin fill.
Legend and Symbols Denotes published zircon U-Pb data from  Fosdick et al. (2011). Detrital zircons were extracted from ~5 kg medium-grained sandstone hand-samples using standard mineral separation techniques at the ZirChron, LLC. (Tucson, Arizona), including crushing and grinding, fractionation of magnetic minerals with a Frantz isodynamic magnetic separator, and settling through heavy liquids to exclude phases with densities less than 3. 3 g/cm 3. Final zircon separates were mounted in epoxy resin together with fragments of the Sri Lanka standard zircon. The mounts are polished to a depth of ~20 µm, imaged, and cleaned prior to isotopic analysis. U-Pb geochronology of zircons is conducted by laser ablation multicollector inductively coupled plasma mass spectrometry (LA-MC-ICPMS) at the Arizona LaserChron Center  (Gehrels et al.
ACKNOWLEDGMENTS Research was supported by the  U. S. National Science Foundation  Award  EAR-1649585  to JCF, the  Robert R. Shrock Foundation at Indiana University , faculty research funds from the  Dorotea Formation samples  included in data comparison are:  CCS-01  and CM-1 (Romans et al. , 2010) and  JCF09-226  (Fosdick et al. , 2015). Santa Cruz Formation samples included in data comparison are:  JCF09-235  (Fosdick et al. , 2015).
Three independent experiments were performed for all cell lines (24 arrays in total). Signals were corrected for background, normalized using the Protein Array Analyzer (PAA) package  (Turewicz et al, 2016) , and analyzed using both the Welch and the Wilcoxon-Mann-Whitney statistical tests (see the Materials and Methods section for  details). This led to the identification of 988 ubiquitylated and 83 SUMOylated proteins, 72 proteins being both ubiquitylated and SUMOylated (Figs 1C, S1, and Table  S1 ). Identification of a UbL signature of AML resistance to standard chemotherapy Among the proteins identified as robustly ubiquitylated or SUMOylated on the arrays, we then selected those that were differentially modified between cell extracts from chemoresistant and parental cell lines. A first analysis was performed by comparing the pooled data obtained for all resistant cell lines to those from all sensitive ones.
(B) IC 50 of chemosensitive and chemoresistant acute myeloid leukemia cell lines. IC 50 of chemosensitive parental HL-60 and U937 (wt) cells and of their resistant counterparts (see the Materials and Methods section) (ARA-R and DNR-R) was assayed after 24 h of exposure to drugs. n = 3, Mean ± SEM, paired t test with * corresponding to P < 0. 05. (C) Identification of ubiquitylated-and SUMOylated proteins. Normalized Ub and SUMO-1 fluorescence data obtained on all arrays incubated with extracts were compared with averaged signals on control arrays (extracts supplemented with NEM to inhibit UbL conjugation activities) to identify robustly UbL-conjugated proteins. Proteins showing significant differences between the two groups when using both the Welch-and the Wilcoxon-Mann-Whitney statistical tests and having mean fluorescence intensity values higher than 800 (arbitrary threshold) on ProtoArrays were selected for further analysis.
The Venn diagram shows the number or proteins identified as modified by SUMO-1 and/or ubiquitin. Materials and Methods section). The GA was run on the cell line datasets multiple times, with different parameter settings to obtain a total of 40 solutions (combinations of selected proteins). Of the 30 variables, 25 were selected at least once in the 40 solutions obtained. They were ranked by their frequency of selection (Fig  3A ), as frequently selected proteins are more likely to be predictive. We then divided this list in five embedded subsets containing an increasing number of selected variables/proteins (Fig 3A ) for linear discriminant analysis (LDA), which uses an optimized linear combination of all variables to assign observations to target classes (here, sensitive or resistant). As expected, we observed that the most frequently selected proteins were present in the most predictive selected solutions, which suggested that they were not random artefacts because of the GA.
The number of proteins showing a significant P-value in one sample t test and a ratio between drug-resistant and parental cells higher than 1. 5, or lower than 0. 66, are shown. (C) Ontology analysis of the UbL signature. An ontology analysis of the 122 proteins of the UbL signature was performed using the Panther software. specific secondary antibodies linked to two different fluorochromes (Fig  4A ). If ubiquitylation was detected on all proteins tested, this was, however, not the case for SUMOylation. A most likely explanation for this discrepancy is that ubiquitin can make long polyubiquitin chains, facilitating their detection on protein substrates, whereas SUMO cannot. We consequently focused the rest of our study on ubiquitylated proteins. Of the 10 proteins tested, four proteins (UBADC1, STAM, SQSTM1, and HIP2) showed significant differences in their ubiquitylation levels between parental, Ara-C-and/or DNR-resistant U937 (Fig  4B  and C ).
This suggests that some of the alterations resulting in changes in UbL enzyme activities are specific to the genotoxics they are resistant to and/or to the AML subtype. We then generated scores based on the most robust proteins of the signature in terms of level and differences of UbL modification between the parental and the resistant AML cell lines we used as models. These scores were used on protein array data obtained with four patients' samples and one of the scores could retrospectively predict the response of these four patients to the chemotherapy. Even if the number of patients' samples was too small to validate this assay on a definitive statistical basis, our work nevertheless provides a proof of concept that such an approach could be used to help predict AML patients' response to current standard chemotherapies.
IC 50 measurement Cells were seeded at a concentration of 3 × 10 5 /ml in Roswell Park Memorial Institute medium complemented with 0. 1, 1, 10, 50, 100, or 250 μM of Ara-C (Sigma-Aldrich) or 0. 01, 0. 05, 0. 1, 0. 5, 1, or 10 μM of daunorubicin (Sigma-Aldrich). Viability was measured 24 h later using the MTS assay from Promega following the manufacturer's protocol. IC 50 was calculated using the GraphPad PRISM software.
Analysis of ProtoArray data Measured fluorescence intensities were associated to the corresponding protein ID according to their coordinates on the arrays using the Mapix software (Innopsys). Intensities were processed using the PAA R package  (Turewicz et al, 2016). In brief, (i) duplicated protein spot intensities were averaged using the LoadGPR function and (ii) the background was corrected using the BackgroundCorrect function. Fluorescence intensities within the same experiment were normalized by quantiles using the NormalizeArray function. Finally, intensities were normalized between the different experiments using the BatchAdjust function.
GA and linear discriminant analysis The R package GA, providing a GA  (Scrucca, 2013) , was used and its code is provided as Supplemental Data 1. The aim was to determine the right number of variables to create a parsimonious predictive model. GAs are mathematical models inspired by Charles Darwin's model of natural selection. The natural selection preserves only the fittest individuals over the different generations. An evolutionary algorithm improves the selection over time and allows the best solution to emerge from the best of prior solutions. The selected features were then tested with LDA using the R package MASS  (Ripley, 1996; Venables & Ripley, 2002). This mathematical method uses a linear combination of all variables to assign observations to target classes. To this aim, it creates a decision rule based on the n available variables (score = α × R 1 + β × R 2 + ….
Three independent experiments were performed for each cell line. (B) IC 50 of chemosensitive and chemoresistant acute myeloid leukemia cell lines. IC 50 of chemosensitive parental HL-60 and U937 (wt) cells and of their resistant counterparts (see the Materials and Methods section) (ARA-R and DNR-R) was assayed after 24 h of exposure to drugs. n = 3, Mean ± SEM, paired t test with * corresponding to P < 0. 05. (C) Identification of ubiquitylated-and SUMOylated proteins. Normalized Ub and SUMO-1 fluorescence data obtained on all arrays incubated with extracts were compared with averaged signals on control arrays (extracts supplemented with NEM to inhibit UbL conjugation activities) to identify robustly UbL-conjugated proteins. Proteins showing significant differences between the two groups when using both the Welch-and the Wilcoxon-Mann-Whitney statistical tests and having mean fluorescence intensity values higher than 800 (arbitrary threshold) on ProtoArrays were selected for further analysis.
Statistical analyses between drug-resistant and parental cells were performed separately for U937 and HL-60 cell lines and for each drug resistance. The number of proteins showing a significant P-value in one sample t test and a ratio between drug-resistant and parental cells higher than 1. 5, or lower than 0. 66, are shown. (C) Ontology analysis of the UbL signature. An ontology analysis of the 122 proteins of the UbL signature was performed using the Panther software.
Acknowledgements We are grateful to the  Institut de G én étique Mol éculaire de Montpellier (IGMM) "Oncogenesis and Immunotherapy " group members and  Dr O Coux  for fruitful discussions and critical reading of the manuscript. We thank the  Montpellier Genomic Collection  and Montpellier Ressources Imagerie platforms for technical assistance. Funding was provided by the  Centre National de la Recherche Scientifique (CNRS) ,  Ligue Nationale contre le Cancer  ( Programme Equipe Labellis ée ),  R égion Languedoc-Roussillon  (" Chercheur d'Avenir " contract),  Association Laurette Fugain  ( ALF-2017/02  contract), the  F éd ération Leuc émie Espoir , and the  ANR  (" Investissements d'avenir" program ;  ANR-16-IDEX-0006 ). P Gâtel was supported by fellowships from the  University of Montpellier , the  Montpellier Centre Hospitalo Universitaire , and the  Fondation Association pour la Recherche contre le Cancer (ARC).
HYSPLIT-GDAS In this study, HYSPLIT hourly five-day back trajectories were performed with the Global Data Assimilation System (GDAS, ftp://arlftp. arlhq. noaa. gov/pub/archives/gdas0p5/) (Rolph et al. , 2017; Stein et al. , 2015)  at 0. 5 degree resolution for each CCN spectrum in the MBL leg (below the cloud layer if clouds are present). The average latitude, longitude and altitude (50-500 m) of the MBL legs on the GV HIAPER were used as starting points for the back trajectories. Antarctica is the only continent over which back trajectories passed; none of the airborne aerosol measurements in the MBL were influenced by continental Australia; the only anthropogenic influences were potentially ship tracks and research stations in Antarctica, which we assume to have a negligible impact in this study.
ECMWF ReAnalysis (ERA5) ERA5 is the 5th generation of a climate reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF)(Copernicus Climate Change Service (C3S), 2017). The ERA5 model assimilates satellite, ground and airborne measurements to archive the state of the weather and climate. The ERA5 total precipitation and low-level cloud fraction was used for the time period covering the SOCRATES campaign to identify the role of clouds and precipitation in changing CN and CCN concentrations. The ERA5 time resolution is hourly, and spatial resolution is 0. 25 degrees.
Using this approach, rather than grouping all values into a single bin, each measurement is represented by a normal distribution and integrated to produce the kernel density estimate. The optimal kernel density estimate bandwidth was found to be 28 and 91 for CCN0. 3 and CN, respectively, and calculated using the "ksdensity" function from Matlab (2019), derived from theory developed by  Bowman and Azzalini (1997). The Hartigan's Dip test  (Hartigan and Hartigan, 1985)  determined that the distribution was not unimodal (P-value <0. 01) for both CN and CCN0. 3, thereby validating the use of a bimodal distribution for this analysis. The bimodal distribution minima correspond to 125 cm -3 and 750 cm -3 for CCN0. 3 and CN, respectively. Even though only CCN0. 3 was used to determine the particle regimes, Figure  2a  illustrates the systematic differences between the averaged CCN spectra and CN concentrations for each of the regimes.