dataesr/scientific-paragraphs-categorization huggingface dataset:
P-glycoprotein 3. 2. 1. Data analysis Kinetic data were available for European, East Asian, South Asian, Southeast Asian, North American and Middle East healthy adults with the majority of the datasets from North America, East Asian and European studies. In order to estimate inter-ethnic differences, European healthy adults were used as the reference group with the highest number of P-gp substrates and parameters for the oral and intravenous routes. Data gaps were identified for specific groups including Central and Southern Americans, as well as North and sub-Saharan Africans. CV values from the meta-analyses provide inter-individual variability considering all substrates and highlight lower interindividual variability for the IV route compared to the oral route (Table  2 ). Overall, inter-individual variability in kinetic parameters for healthy adults is around 40% for the oral route (AUC/clearance and Cmax respectively) and 20% for the IV route (AUC/clearance).
4 [1. 1-2. 7] 1. 4 [1. 2-3. 3] 1. 4 [1. 0-2. 0] 1. 4 [1. 0-2. 0] North America b 4 2 31 28. 1 1. 6 [1. 3-3. 1] 1. 7 [1. 3-3. 9] 1. 7 [0. 9-3. 4] 1. 9 [0. 9-3. 6] a Fexofenadine was not studied in the reference group. b Digoxin was the only common substrate with the reference group; ns: number of studies, nc: number of compounds, n: number of individuals, CV: coefficient of variation (lognormal distribution), GM: geometric mean (lognormal distribution), ratio GM: ratio of geometric mean between healthy adults from Europe and subgroup (lognormal distribution, 1/ratio GM for AUC and Cmax). caution, since only one chemical was included for the variability and UF calculation. Most PK data included in the meta-analysis was measured in Asians, showing large variability in measured data, which may be due to polymorphisms.
OAT1/3 An ELS was performed for OATs to identify human PK studies in healthy adults from different geographical ancestry or ethnic background. The literature data highlight a broad overlapping substrate specificity among different OATs that does not allow to draw conclusions clearly referred to a single transporter; the PK parameters measured are in most cases based on the net result of the actions of more than one carrier. Adefovir and sitagliptin have been identified as in vivo probe substrates for OAT1 and OAT3, respectively. The meta-analysis showed that the overall variability of the OAT1/3 transporters is lower compared to P-gp and BCRP (Table  5 ). The default kinetic UF would be protective of 97. 5% of the healthy adult population considering median value, but number of subjects available from kinetic studies is limited, especially regarding adefovir, and therefore results should be considered with caution.
Statistical analyses All fluorescence data treatment was performed using MATLAB  [46]  and the statistical tests cited below were performed using SigmaPlot or R Software. Multiple regressions were performed to estimate which parameters influenced the dynamics of the biological parameters. Correlations between parameters were tested using Pearson correlation tests. After testing the application conditions (normality of residuals, homoscedasticity), ANOVA (AOV) was used (or the non-parametric Kruskal-Wallis test (KT) when conditions were invalid) to estimate significant differences between sites. A principal component analysis (PCA) was performed on the dataset using the "MissMDA" and "FactoMineR" packages in R Software.
gDW -1 ) of bound proteins were recorded in September than in April at sites G, H and I (characterized by between 51 and 58% of fine particles). Significantly lower colloidal and bound protein contents were observed in the southern mudflat than in the two others. Positive correlations were observed between chl a content and concentration of the different fractions of EPS. However, the ratios of EPS per chl a unit were higher in April than in September in the southern mudflat with a factor 10 for proteins and 5 for carbohydrates but were relatively similar in both the other mudflats. Relationships between biological and environmental variables. Principal component analyses (PCA) were performed on the dataset to explore the relationships between biological and physical parameters (Fig  11A).
Acknowledgments Many thanks to  Thomas Lecarpentier , ChloeDancie and  Bastien Chouquet  for their help with preparation for the sampling campaigns and their assistance in the field. The authors also wish to thank those who participated in the sampling campaigns and in the treatments particularly  Samuel Davoult  and  Sandric Lesourd  in September and  Claire Josso  and  Brenda Hervieux  in April. We are also grateful to  Guillaume Izabel  for his work on the map and to  Nicolas Bacq  and  Jean-Philippe Lemoine  for their help throughout the project.
S1b ). At each sampling time (day 12 and 17), half of the sampled giant clam mantles (4 cm 2 ) and 80% of the sampled coral fragments were immediately snap-frozen in liquid nitrogen and stored at -80 °C until further analysis. The remaining portions of the sampled giant clams and corals were stored in 70% ethanol for DNA analysis. Seawater temperature data were recorded every 10 minutes with Temperature/Light Data Loggers (P/N U22-001, Onset, Bourne, Massachusetts; or Ruskin, Ottawa, Canada; Fig. S1b ). Seawater temperature was controlled with the Biotherm pro system (Hobby, Stukenbrock, Germany). Health status checks on the coral nubbins were carried out using the standardized coral coloration scale as a proxy of symbiont density and chlorophyll a content variation (Coral Health Chart, www.
Nuclear magnetic resonance data acquisition and dimethylsulfoniopropionate quantification. All 1 H spectra were recorded on a Bruker Avance 1 spectrometer (Bruker, Germany) at 300 MHz in the same conditions with 128 transients; chemical shifts were reported in ppm from tetramethylsilane (TMS). Absolute Area was determined by NMR analysis on the topspin 2. 1 Bruker software. Two external calibration curves of DMSP were performed under the same conditions as the samples with 128 transients (Figure  S1c, d ). Integration zone A (3. 41 ppm to 3. 34 ppm) was used for A. cytherea and P. damicornis samples because there were no overlapping signals. Integration zone B was used for T. maxima samples because of overlapping signals on zone A and no overlapping signal on zone B (Fig. S1e-h ). The concentrations of DMSP calculated with the calibration curve (in mg per mL) were normalized with the mass ratio corresponding to the dry weight of the extract.
Identification of candidate genes. A previous predicted peptide data set of T. maxima metatranscriptome  53  was blasted against a custom database at a threshold of 10 -3. This database contained molluscan and cnidarian sequences extracted from the Protein database of the National Center for Biotechnology Information (NCBI) filtered with the DMSP enzyme names or EC numbers and from the DMSP candidate genes previously identified in Acropora millepora  15  (Table  S2 , sheet 2). The selected sequences were blasted against the whole NCBI Protein database and any sequence with a top hit with a plant, algae or bacterial taxonomy was considered a false positive and removed (Table  S2 , sheet 1). A differential gene expression analysis was then performed from previous RNAseq results of T. maxima under thermal stress at 32 °C for 5 days (Bioproject accession number: PRJNA309928).
Identification of candidate genes for dimethylsulfoniopropionate biosynthesis in Tridacna maxima. The blasting of the predicted peptide T. maxima dataset against a DMSP enzyme custom database allowed the selection of 475 predicted peptides potentially involved in pathways of DMSP biosynthesis (Table  S2  and S5, sheet 2). After functional clustering, all identified peptides with potential roles in DMSP synthesis were found to exhibit at least 1 homolog per step. Among them, 69 were significantly (False Discovery Rate <0. 05) up-regulated during a thermal stress event (Table  S5 , sheet 1). More precisely, 29 peptides were identified as homologs to those of the coral pathway: 6 homologs of AT1 and AT5 for transamination step, 16 for the reduction step, 6 homologs of REDOX2,3,4,6 and 10, for the methylation step of METHYL 1 and 2, and one homolog of DECARB3 for the decarboxylation step (Fig.
Acknowledgements This work was supported by  LabexCorail "SynTox". I. Guibert  was a fellow of Sorbonne University -Doctoral School 129. We acknowledge the  InterContinental Resort & Spa Moorea  and the  Moorea Dolphin Center  for providing a coral garden protected area. We also thank  Franck Lerouvreur ,  Pascal Ung ,  Ewen Morin  and all the students from CRIOBE who helped us in the field. We are much indebted to  Roy Benyon  for English improvement of the manuscript.
For using the second harmonic, only the SHG crystal is used. The focused laser beam enters the sample chamber via a tripleband anti-reflection coated viewport flange of optical surface quality. The focus size at the sample can be controlled by adjusting the distance of the focusing lens L, which is mounted on a motorized linear stage between both Mk mirrors (see Fig. 4 ). The beam position and pointing are monitored by three CCD cameras installed in the near and far-field (CCD1/2 and CCD3). CCD3 is mounted at the same distance from the focusing lens L as the distance from the lens L to the sample, thus working as a virtual focus monitor. This camera uses the light leaking from the last deflection/filtering mirror Mk. The laser intensity upstream and downstream of the sample is measured by two fast photodiodes (PD1/2, Model AXUV63HS1) connected to ADCs which are included into the FLASH Data Acquisition (DAQ) system  (Agababyan et al.
G. Software control The experimental control at TRIXS is currently distributed over different software packages. The spectrometer itself is operated via a LabView based server and a client implemented by BESTEC GmbH, while the spectrometer CCD detector is integrated into the FLASH Distributed Object-Oriented Control System Framework (DOOCS)  (Hensler and Rehlich, 1996). A Python-based (Python Software Foundation) software package provides measurements of the spectrometer dispersion and resolution. Moreover, integrated RIXS spectra can be easily plotted in the live mode. The RIXS spectra are stored in the FLASH DAQ system in a binary format and can be automatically converted into the "hdf5" format (HDF Group) for near-online analysis. Typical RIXS spectra are collected within approximately 30 min, while the online conversion into the hdf5 format takes approximately 5 min.
C. Static RIXS measurements To demonstrate the TRIXS spectrometer performance, we present static RIXS measurements of NiO which was resonantly excited by the FEL at the Ni M 2,3 -edge at 70. 1 eV photon energy (see Fig. 10 ). For this measurement, FLASH was operated in burst mode with 4000 pulses/second. The primary PG monochromator was set to 15 meV bandwidth. The measured TRIXS resolution was 86 meV. Figure  10  shows the elastic line (intensity is cut in the graphical presentation) and the characteristic d-d excitations at 1. 05 eV, 1. 7 eV, and 3. 3 eV which agree very well with the data recorded at a synchrotron  (Chiuzb aian et al. , 2005). The central energy of the spectrometer was The sensitivity to changes in experiments with the TRIXS endstation depends strongly on the overall signal strength from the sample.
ACKNOWLEDGMENTS The authors are very thankful to the FLASH group, DESY Mdivision, MEA2 group, FS-LA group,  E. Jal ,  B. Vodungbo ,  V. Chardonnet  from  Sorbonne Universit e and the review committee of the beamline (F. Parmigiani (Elettra) ,  H. Zacharias  (U. M€ unster),  Th. Schmitt (PSI) ,  T. Pfeifer  (MPIK), and  S. Molodtsov  (European XFEL)) for the support and constructive comments. The spectrometer was funded within the  BMBF  project ( BMBF  Project No. 05KS4GU2 DESY ). P. S. M. , J. O. S. , R. Y. E. , and  M. Be. acknowledge funding from the  Helmholtz Association  through Grant No. VH-NG-1105.
[31, 34, 43, 44]  and for weak GW turbulence see Ref. [30] ). Therefore, the slight discrepancy between our prediction and the Planck data could be the signature of an anomalous scaling. Finally, we can show that the tensor-to-scalar ratio r (ratio between the metric and scalar spectra) is independent of k. Its amplitude is, however, difficult to estimate without numerical simulation because it requires the knowledge of the Kolmogorov constants.
The form of (A7) is precisely what results from the second term of Eq. (  8 ) in Ref. [54]. The complete expression for hh R µκ is lengthy and will not be reproduced here. It can be determined through the use of computational algebra software such as Mathematica. Note that by considering the Einstein vacuum equations at order O(ǫ 2 ), it gives rise to two equations: one equation for the mean and one equation for the fluctuations. Let us introduce the spatial average • := lim V →∞ [(1/V ) V • dx], then up to order O(ǫ 2 ) we have R µκ = R (0) µκ + R (1) µκ + R (2) µκ = R (2) µκ = 0, ( A8 ) where we have taken into account that R (0) µκ = 0, while R µκ is linear in terms of the perturbation h µκ and vanishes upon the spatial average. (Actually, this implies that the averaged metric remains of FLRW form which will be seen from the structure of the final averaged equations.
) Now note that ah R (2) µκ has zero mean (being linear in h µκ ) and that a R (2) µκ = a R (2) µκ. We evaluate terms hh R (2) µκ assuming a distribution of gravitational waves with amplitudes h µκ = (2π) -3 ĥµκ (k, t) exp(ik • x) dk, where ĥµκ (k, t) are time-dependent wave amplitudes for a wave with wave vector k = (k 1 , k 2 , k 3 ). For the calculation below, it suffices to take the leading order time dependence of ĥk (t) which follows from the wave equation (A6), namely ĥµκ (k, t) ∼ exp(-i t 0 ω k dt ′ ) with ω k = c|k|/a(t ′ ). In terms of waves with + and × polarizations, we have that We consider the monochromatic wave amplitudes λk and μk of the form λk =(2π) 3 [Λ 0 δ(k -k 0 ) + Λ * 0 δ(k + k 0 )], μk =(2π) 3 [M 0 δ(k -k 0 ) + M * 0 δ(k + k 0 )], which we substitute into the expression for hh R µκ and apply space averaging, with the aid of the Mathematica software, to get a rather simple result: hh R (2) 00 = c 2 |k 0 | 2 a 6 (|Λ 0 | 2 + |M 0 | 2 ), hh R (2) 0l = c|k 0 |k 0l a 5 (|Λ 0 | 2 + |M 0 | 2 ), hh R (2) lm = k 0l k 0m a 4 (|Λ 0 | 2 + |M 0 | 2 ), l, m = 1, 2, 3.
The second pass was a clustering with an aggregation distance equal to 3. Taxonomic assignment of amplicons into operational taxonomic units (OTUs) was produced by Blast+ v2. 2. 30+ with the Silva 132 Parc databank (Max Planck Institute for Marine Microbiology and Jacobs University, Bremen, Germany). The following specific filters were applied to the pipeline: the last ten bases of reads R1 and R2 were removed (lower quality preventing good read pairing); amplicons shorter than 350 nucleotides or longer than 500 nucleotides were removed; and OTUs with abundance lower than 0⋅005 per cent of the whole data set abundance were removed. For additional quality control of the sequencing data, all sequence files were assessed with the software FastQC version 0. 11. 8 (Babraham Bioinformatics, Cambridge, UK).
The workflow in this study has been validated previously and the universal primers used provide high sensitivity (targeting 95 per cent of all bacterial sequences in the Ribosomal Database Project) and 100 per cent specificity (no mitochondrial, archaea or eukaryotic DNA targeted). Furthermore, the workflow has been optimized to ensure a very low overall background signal from bacterial contaminants present in reagents and consumables  19, 23, 24. Although more OTUs were identified in the DNA extraction negative control than the PCR negative control, suggesting some level of contamination from the DNA extraction step, the overall level of OTUs in the negative controls was clearly lower than in blood samples, and they formed separate clusters in MDS. The methods applied here do not provide any information on the direct source of the microbiota identified.
A new precipitation dataset is provided since 2014 by the Global Precipitation Measurement (GPM) satellite constellation measurements combined in the Integrated Multi-satellite Retrievals for GPM (IMERG) algorithm. This recent GPM-IMERG dataset provides potentially useful precipitation data for regions with a low density of rain gauges. The main objective of this study is to evaluate the accuracy of the near real-time product (IMERG-E) compared to observed rainfall and its suitability for hydrological modeling over a mountainous watershed in Morocco, the Ghdat located upstream the city of Marrakech. Several statistical indices have been computed and a hydrological model has been driven with IMERG-E rainfall to estimate its suitability to simulate floods during the period from 2011 to 2018.
In this study, IMERG-E half-hourly data from 2011 to 2018 were downloaded from the NASA website (https://gpm. nasa. gov/data-access/downloads/gpm). This product was chosen due to its short time latency (4-h), making it interesting for the early warning of flood events. We extracted four pixels of GPM IMERG-E that cover the Ghdat basin and we interpolated observed precipitation data with the inverse distance method to represent areal precipitation over the studied basin.
Soil Moisture Dataset To estimate the initial soil moisture condition prior to flood events, the European Space Agency Climate Change Initiative (ESA-CCI) data is considered. ESA-CCI V04. 2 is a soil moisture product (http://www. esa-soilmoisture-cci. org/) available over a 38-year period from 1978 to 2018. This database includes three products: active, passive and combined. It provides daily soil surface moisture with a spatial resolution of 0. 25 degrees [31,  45, 46]. The product has been validated by  [47] , and recently validated in the High Atlas of Marrakech by  [18, 48]  for flood modeling. The ESA-CCI product shows in the mountainous area some missing values  [49]  that are related to a filter that is applied to the ESA-CCI product in order to ensure the data quality  [45]. The ESA-CCI soil moisture pixel covering the Ghdat basin has been extracted.
Hydrological Model In this study, we used the SCS-CN model implemented in the HEC-HMS software [50]. This software includes hydrological analysis procedures such as unit hydrographs and hydrologic routing (http://www. hec. usace. army. mil/software/hec-hms/). The SCS-CN model is based on the cumulative excess of precipitation by time [50]: Pe = (P -0. 2S)2/P + 0. 8S  (7)  where Pe is the excess of precipitation at time t, P is the rain accumulation at time t and S is the maximum retention capacity, that is related to the curve number (CN). The excess precipitation is transformed into direct runoff through the concept of unit hydrograph. The modeling tool used has several transfer functions: Clark, Snyder and SCS unit hydrographs, Modclark transformation and the kinematic wave. For event modeling, Clark's unit hydrograph  [50]  has been widely used in the semi-arid mountainous basins which are known by a complex and variable topography  [18, 51] : A t A =          1.
GPM IMERG-E Product Evaluation The GPM IMERG-E product is evaluated against the rain gauge data using six statistical indices considered (CC, ME, RMSE, BIAS, POD and FAR) at both basin scale and the individual grid-cells corresponding to rain gauges during the available period from January 2011 to December 2018. The statistical evaluation indices of the IMERG-E product are presented at the basin scale in Table  3  and at the grid-cell scale in Table  4. At the basin scale, satellite precipitation data overestimates the amount of rainfall with a relative Bias of +35. 61%. Several studies found that the IMERG precipitation products overestimate the amount of rainfall; in Italy [57], in Peru and Ecuador  [58] , in Iran  [59]  and in Spain  [60]. About the overall agreement between the gauge observations and satellite precipitation data, the Pearson's correlation coefficient is equal to 0.
Hydrological Model Validation with Observed and GPM IMERG-E Precipitation The calibrated CN are correlated with the ESA-CCI soil moisture data in order to estimate the initial soil moisture condition of the model. This correlation is good with a coefficient equal to r = 0. 96 when the SCS-CN model is calibrated with rain gauge data (Figure  3 ). For the data based on the IMERG-E satellite precipitation, the correlation between the CN and the ESA-CCI data are also strong and equal to 0. 95 (Figure  3 ). This correlation confirms the good relationship between the CN parameter of the production function and ancillary source of soil moisture conditions, as demonstrated in similar studies  [18, 55, 64]. The validation will therefore be based on ESA-CCI data. The equation based on the correlation allowed us to calculate the validation CN of each event in turn by the leave-one-out method.
This method involves correlating the calibrated CN values of the sixteen events with ESA-CCI soil moisture to determine the CN of the event that remains. Table  6  presents the results of the efficiency criterions used to evaluate the agreement between observed and simulated hydrographs. For the validated events, the average Nash values is equal to For the data based on the IMERG-E satellite precipitation, the correlation between the CN and the ESA-CCI data are also strong and equal to 0. 95 (Figure  3 ). This correlation confirms the good relationship between the CN parameter of the production function and ancillary source of soil moisture conditions, as demonstrated in similar studies  [18, 55, 64]. The validation will therefore be based on ESA-CCI data. The equation based on the correlation allowed us to calculate the validation CN of each event in turn by the leave-one-out method.
Next, we explored the possibility to classify the MMTV-R26 Met tumors to TNBC subtypes by analyzing the RPPA data applying the Random Forest machine learning algorithm previously used with transcriptomic data. [43] As subtype classification is usually done on transcriptomic data, we first used the RPPA data of 152 TNBC patients from the TCGA dataset to build a model for subtyping prediction. We trained the model using tenfold cross validation to optimize the method parameters (Figure  S1f , Supporting Information). The model was sensitive to the M class (balanced accuracy 0. 89), and had lower sensitivity in distinguishing between BL1 and BL2 classes (balanced accuracy of 0. 69 and 0. 55, respectively). This was done to take into account that most of the patients are BL1, with a consequent 30% correct BL1 prediction called "no information rate.
" Our Random Forest model had accuracy of 57% (or 71% without distinguishing BL1 and BL2), with a significance of p-value = 0. 002 compared to the "noinformation rate. " We then applied the model on the RPPA data of MMTV-R26 Met tumors to predict their classification. Remarkably, we found that all TNBC subtypes are represented by the MMTV-R26 Met tumors with an enrichment of the mesenchymal subtype (Figure  2c, d ). Collectively, these results showed that a moderate increase of MET levels in the mammary gland is sufficient to perturb tissue homeostasis, is able to initiate the TNBC program including the formation of lung metastasis, and that the resulting tumors recapitulate the heterogeneity characteristic of TNBC patients. To further exploit the MMTV-R26 Met cancer model, we established and molecularly/biologically characterized six mammary gland tumor (MGT) cell lines from individual MMTV-R26 Met tumors (Figure  3a ).