dataesr/scientific-paragraphs-categorization huggingface dataset:
00 ng m -3 at a depth of 10 cm. During this event, ice precipitation was observed at Concordia Station with depolarization values greater than 30 % (data not shown). This suggests that the presence of ice crystals could enhance the dry deposition of Hg(II) species onto the snow surface leading to increased Hg(0) formation in the upper layers of the snowpack. Indeed, due to an elevated specific surface area, mercury-capture efficiency of ice crystals is high  (Douglas et al. , 2008). Unfortunately, due to a low sampling frequency of surface snow samples (weekly), total mercury concentrations cannot be used to study further the relationship between the occurrence of ice precipitation events and dry deposition of mercury species.
, 2007). Increases in both Hg(II) and Hg(p) have been reported in conjunction with decreases of Hg(0)  (Lu et al. , 2001; Lindberg et al. , 2002; Aspmo et al. , 2005). Conversely, low Hg(0) concentrations that were not correlated or anti-correlated with O 3 were observed at Neumayer and Troll  (Temme et al. , 2003; Pfaffhuber et al. , 2012) , while elevated Hg(II) concen-  ). Concentrations at 25, 210, and 1070 cm above the snow surface were acquired on the meteorological tower while concentrations at 50, 10, -10, -30, -50, and -70 cm were collected on snow tower #1. Data were cubic spline interpolated using software R. trations (up to 0. 33 ng m -3 ) were recorded at Terra Nova Bay in the absence of Hg(0)/O 3 depletion  (Sprovieri et al. , 2002). The intense oxidation of Hg(0) in summer (see Sect.
Acknowledgements. We thank  A. Barbero  and the rest of the overwintering crew:  S. Aubin ,  C. Lenormant , and  R. Jacob. We also gratefully acknowledge  M. Barret  for the development of a  QA/QC software program ,  L. Bonato  for the analysis of total mercury in surface snow samples,  D. Liptzin  for the calculation of the Obukhov length and friction velocity,  M. Legrand  for the 2012 ozone data,  C. Genthon  for the meteorological data,  E. Vignon  for helpful discussion, and  B. Jourdain  and  X. Faïn  for their help in the field. This work contributed to the  EU-FP7  project  Global Mercury Observation System  (GMOS -www. gmos. eu)and has been supported by a grant from  Labex OSUG@2020  (Investissements d'avenir - ANR10 LABX56 ), and the  Institut Universitaire de France. Boris Quennehen  and  Philippe Ricaud  acknowledge  ANR CLIMSLIP  and  HAMSTRAD Program 910 , respectively.
Rational identification of antiviral drugs. On the basis of the demonstration of the antiviral activity of all lipid biosynthesis inhibitors targeting the proviral factors identified by the host genome-wide loss-of-function screen, we next considered all the other hits as putative drug targets. Therefore, to exploit the translational potential of our screen, we systematically assessed the antiviral activity of chemical compounds targeting the identified proviral factors involved in CHIKV replication. We interrogated specialized databases (www. drugbank. ca  27 ; lincs. hms. harvard. edu/kinomescan  28 ; www. ebi. ac. uk/chembl  29  ), linking several drugs to their experimentally proven target proteins/genes, and identified 52 compounds interacting with the gene products of 14 distinct CHIKV proviral genes.
Images were taken with 4,6-diamidino-2-phenylindole and GFP filter sets (AHF-Analysetechnik). Scan R Analysis Software (Olympus Soft Imaging Solutions) was used to automatically identify and quantify GFP-positive cells (indicating infected cells) and cell nuclei. The number of automatically counted nuclei was further used to estimate the cytotoxic effects of specific siRNAs. The siRNA was classified as toxic if 1,000 or fewer nuclei were determined within one well of a 384-well plate. All multi-well pipetting steps were performed using a Biomek FX P Laboratory Automation Workstation (Beckman Coulter). An siRNA library (Qiagen Hu_Genome 1. 0 and Human Druggable Genome siRNA Set V2. 0; Qiagen) containing four siRNAs per gene for the druggable genome  43  and two siRNAs per gene for non-druggable and predicted genes was screened at least three times independently.
Experiments in Supplementary Fig. 5d ,e were performed using the HiPerFect transfection kit to transfect siRNA (Silencer Select Negative Control No. 2 siRNA, catalogue number 4390846, and s33489, s20629 and s31791-target sequences in Supplementary Data 1-at a final concentration of 5 nM) for 48 h before infection. Silencing efficiency was determined by quantitative PCR using the TaqMan RNA-to-Ct 1-Step Kit and the following TaqMan Gene Expression Assays (Life Technologies): Hs00197310_m1, Hs00367471_m1 and Hs00219525_m1. RNAs were extracted following the RNeasy Mini Kit (Qiagen) protocol with DNAse treatment. Intracellular capsid (C) staining for flow cytometry was performed using a permeabilization buffer (PBS, 0. 05% saponin and 0. 2% BSA). We used a mouse monoclonal antibody (clone H3-18, 1:1,000) generated in-house and directly coupled with ATTO532 to reveal CHIKV capsid and a rabbit polyclonal serum recognizing the capsid protein (Ventoso I, Madrid, 1:1,000) to detect SINV.
Flow cytometry acquisition was performed using a MACSQuant VYB system; FlowJo was used for the analysis. The percentage of infected cells was used as infection readout. Data were then normalized by setting the DMSO condition as an arbitrary value of 100. Simultaneous detection of FLT4 (clone 9D9F9, Biolegend, 356201, 1:50) and P-FLT4 (rabbit polyclonal from Cell applications, CY1115, 1:400) was performed on CHIKV-GFP-infected cells (MOI: 10 for 18 h), fixed in 4% PFA, activated with À 20 °C cold methanol for 20 min and stained in permeabilization buffer. Cells were acquired using an LSR-fortessa flow cytometer (BD). Anti-mouse IgG antibodies conjugated with Alexa Fluor 555 and anti-rabbit antibodies conjugated with Alexa Fluor 647 (both from Life Technologies) were used as secondary antibodies.
For experiments shown in Fig. 7d ,e, TOFA and pimozide were dissolved in DMSO (independently or in combination) to a final concentration of 3. 64 and 4. 55 mg ml À 1 , respectively, and dosed at 5. 5 ml g À 1. Littermate controls treated with appropriate vehicles were used for all experiments. CHIKV titrations from mouse explants were performed using the standard TCID50 dilution assay on Vero cells. Animal tissues were homogenized in DMEM (high glucose, GlutaMAX, pyruvate) at the final concentration of 0. 1 g ml À 1 using a Precellys 24 homogenizer (Bertin Technologies). In Fig. 7e , joint swelling was measured 4 days p. i. using a Schnelltaster caliper. Data analysis and reproducibility. For identification of primary hits, mainly the inhibitory effect on CHIKV, replication was taken into consideration.
To reduce possible off-target or other unspecific effects and to maximize the robustness of the hit selection, two additional parameters were analysed: (i) non-expressed genes were excluded by determining constitutive or inducible expression via microarray profiling of non-infected and infected HEK-293 cells (7,561 genes were filtered out). (ii) Using the microscopic assay applied throughout the primary screen, toxic siRNAs, which reduced total cell numbers (o1,000 cells per well) on transfection were excluded. In this way, 3,274 siRNAs (targeting 2,635 different genes) were excluded from further analysis. The statistical analysis of the revised raw data was performed using cellHTS2 (ref. 15), an R-implemented software package for the analysis of cell-based high-throughput RNAi screen data.
Raw data were normalized and centred by Z-score transformation. The medians of the centred and scaled values of at least three independent replicates were used for RSA analysis  16. Primary hits were selected by using the cutoff criterion log Pr2. The validation was performed based on the primary hit list, thereby focusing not only on proviral but also on antiviral factors. Although the validation was experimentally identical to the primary screening, the hit selection varied slightly: after filtering out 133 toxic siRNAs, the Z-scores were again determined using the cellHT2 software package. However, instead of using the RSA analysis, which has limitations in analysing data sets consisting of siRNAs that should theoretically all influence CHIKV replication, we computed the cSSMD based on the normalized per cent inhibition of individual siRNAs, by assessing the collective activity of multiple siRNAs relative to the control  17.
Enriched molecular functions were selected on the basis of their statistical significance (Pr0. 01 was used as threshold). Statistically enriched gene ontology terms associated to less than two genes were removed. Co-localization analysis shown in Fig. 3e  was performed on threedimensional cell stacks using the intensity correlation analysis plugin for the WCIF Image J program. Statistical analyses in Figs 1, 3, 5, 6 and 7, Supplementary Fig. 1  and Supplementary Fig. 5  were performed using GraphPad prism. Networks in Fig. 1c  and Supplementary Fig. 1k  were drawn using the Cytoscape software. The STATA software (StataCorp) was used to calculate the D values for interaction effect on the geometric means of data shown in Fig. 7  following the two-factor design method  50. No statistical method was used for the sample size determination and experiments were performed without randomization or blinding strategy.
Acknowledgements We thank  Dr Hans-Joachim Mollenkopf  for assistance with the microarray analysis,  Dr Jens von Kries  and his screening unit,  FMP, Berlin, Germany , for providing the enzymatic CLK1 screening data,  Isabella Gravenstein ,  David Manntz ,  Jo ¨rg Angermann ,  Christiane Dimmler  and  Marina Drabkina  for excellent technical assistance and contributions to the RNAi screen,  Dr Camille Bleriot  for technical assistance in flow cytometry,  Dr Rike Zietlow  for editing the manuscript and  Dr Felix Rey  for critical reading. The plasmids lentiGuide-Puro and lentiCas9-Blast were a kind gift from  Dr F. Zhang , Cambridge, MA,  USA , and CHIKV-GFP a kind gift from  Dr Steve Higgs ,  Kansas State University, KS, USA. pcDNA3. 1-VEGFR-3and pcDNA3. 1-VEGFR-3-G857Rwere kindly provided by  Dr.
Serum Serum was collected by cardiac puncture from mice under isofluorane anesthesia. Whole blood was added to non-heparinized collection tubes and spun to obtain the serum fraction for use in ascorbate and blood chemistry assays. Serum was also snap-frozen in microfuge tubes in liquid nitrogen and stored at -70°C until being assayed. All samples were subjected to a single freeze-thaw cycle. Mouse clinical blood chemistries were analyzed using a Beckman Coulter DXC 600. Data was tested for significance using a one-way ANOVA with Tukey's posthoc comparison. The analysis included 11 wild-type mice, pooled as littermates from each mutant genotype, five Gars C201R/+ , eight Gars Nmf249/+ , and six Agrn nmf380/nmf380 mice. Agrn mutant mice ranged in age from postnatal day 17 to 44. These mice were compared to age matched littermate control animals, and independently to the wild-type control animals from the Gars Nmf249 and Gars C201R litters with similar results.
Ascorbate assay Serum ascorbic acid levels were assayed using a colormetric detection system according to the manufacturers instructions (Abcam, 65656). A 96well plate format was used and results were read on a photospectrometer plate reader. Fifty microliters of serum was used in each assay to be well within the sensitivity of the assay and the linear range of the standard curve. Assay attempts on tissue such as kidney, liver, and spinal cord, failed due to precipitate formation that prevented accurate photospectroscopy readings. Data was tested for significance using a Student's t-test. Analysis was performed on four Gars Nmf249/+ , three Gars C201R/+ , and six wild-type mice pooled as littermates of the mutants.
1% formic acid, while the basic extracts, which also used water/methanol, contained 6. 5 mM ammonium bicarbonate. The MS analysis alternated between MS and data-dependent MS2 scans using dynamic exclusion  (Evans et al. , 2009). Raw data files are archived and extracted as described below. Gas chromatography/mass spectroscopy (GC/MS) The samples destined for GC/MS analysis were re-dried under vacuum desiccation for a minimum of 24 h prior to being derivatized under dried nitrogen using bistrimethyl-silyl-triflouroacetamide (BSTFA). The GC column was 5% phenyl and the temperature ramp was from 40°to 300°C in a 16-min period. Samples were analyzed on a Thermo-Finnigan Trace DSQ fast-scanning single-quadrupole mass spectrometer using electron impact ionization. The instrument was tuned and calibrated for mass resolution and mass accuracy on a daily basis.
Data extraction and compound identification Raw data was extracted, peak-identified and QC processed using Metabolon's hardware and software. These systems are built on a webservice platform utilizing Microsoft's. NET technologies, which run on high-performance application servers and fiber-channel storage arrays in clusters to provide active failover and load-balancing  (Dehaven et al. , 2010). Compounds were identified by comparison to library entries of purified standards or recurrent unknown entities. Metabolon maintains a library based on authenticated standards that contains the retention time/index (RI), mass to charge ratio (m/z), and chromatographic data (including MS/MS spectral data) on all molecules present in the library. Furthermore, biochemical identifications are based on three criteria: retention index within a narrow RI window of the proposed identification, nominal mass match to the library ±0.
Statistical analysis of metabolomics data A total of 20 samples (12 control and eight mutant samples) with a total of 289 metabolites were analyzed with the null hypothesis of no difference between the mutant and control samples. Various different exploratory, univariate and multivariate and machine learning methods were used in the data analysis from publically available software packages Metaboanalyst  (Xia et al. , 2012 (Xia et al. , , 2009)). Chemical structures were known for 228 of the 289 metabolites detected. Seventeen metabolites (16 with known structure, one unknown structure, Table  S2 ) had 50% or more values missing in either mutant or control or both samples and were discarded from further analysis. The final data matrix contained 20 samples and 272 metabolites. Missing values were imputed using the k-nearest neighbors (KNN) method  (Stacklies et al.
, 2007). Foldchange analysis was performed to compare absolute group means between the two groups and was thus performed before any normalization step. The data was then log2 transformed and autoscaled  (Dieterle et al. , 2006; van den Berg et al. , 2006 ). Student's t-test, hierarchical clustering analysis using Euclidian distance and Ward methods, heatmap, and principal component analysis (PCA) were performed as exploratory data analysis methods. A machine learning model was developed using support vector machine (SVM) algorithm with linear kernel method to classify the mutant and control samples and rank the importance of metabolites in discriminating the two types of samples. R package classification and regression training 'caret' version 5. 17-7 was used in SVM model generation  (Kuhn, 2008).
Receiver operating characteristic curve (ROC) analysis was performed using the R package ROCR version 1. 0-5  (Sing et al. , 2005)  to evaluate the sensitivity/specificity of the classification of the samples as mutant or control based on the metabolite signatures. A nested cross validation (CV) approach was used in which 50 sets were created by randomly dividing the 20 samples into two parts, training and test. In each of these 50 sets, the SVM model was trained on 70% of the samples, and was tested on the remaining 30% of the samples. The results from the 50 test samples were used to calculate the model performance and create an importance score index to rank the contribution of each of the 272 metabolites in classification of mutant from control samples. Finally, the same analysis of 50 resamples was performed on the dataset with randomly shuffled class labels to compare and contrast the performance of SVM model on a true dataset with a random dataset with no biological signal.
Funding This work is funded by  National Institutes of Health -National Institute of Neurological Disorders and Stroke  [grants  RO1 NS054154  to R. W. B. ,  R21 NS082666  to R. W. B. and C. J. B. ] and, in part, by grants from the  Research Fund of the Universiteit Antwerpen (University of Antwerp)  [ TOP-BOF 29069  to A. J. ], the  Fonds Wetenschappelijk Onderzoek (Fund for Scientific Research)  -Flanders [ G078414N  to A. J. ] and the  French Association for Neuromuscular Disorders  [ 16971  to A. J. ]. M. C. was supported by  National Institutes of Health  [grant  R25 NS078795 ]. The scientific services at  The Jackson Laboratory  are supported in part by the  National Cancer Center  [ CA034196 ].
The b bp (700) coefficient was obtained through the formula b bp (700) = 2π ˆ1. 1 ˆβp117 (700)  [29] , with β p117 (700) = β 117 (700) -β w117 (700) ~β117 (700), as the water light backscaterring (β w117 (700) ~10 ´5) is negligible compared to particulate light backscattering signal (β p117 (700) ~10 ´2) in turbid waters. The β 117 (700) values were here not corrected for absorption losses (except for pure water absorption) as no absorption measurement was available for this dataset. However at 700 nm these absorption corrections typically correspond to less than 5% of the measured signal in the Rhône River plume  [8]. The low overall Chl-a concentrations (<2 mg¨m ´3) during the study period (Figure  2 ) suggest a total suspended particle matter dominated by mineral rather than organic matter.
Consequently, the Chl-a concentration will not be considered in this study. The Rhône River freshwater discharge was measured at a hydrometric station located in Beaucaire-Tarascon, about 65 km upstream the Rhône River mouth. Daily averaged data were exported from the Hydrologic data bank (http://www. hydro. eaufrance. fr/) for the period corresponding to the Mesurho data acquisition. Hourly (10 min burst average) wind direction and speed were measured at an altitude of 10 m at a meteorological station located on the coast at Cap-Couronne about 20 km east of the river mouth.
The irradiance sensor was hooked to a vertical mast at the front of the boat pointing the zenith and away from ship-induced shadow effects. At each station, measurements were made during and after collection of a surface water sample. Radiometric measurements last 2 to 12 min, depending on the allocated time, taking simultaneous scans of the three instruments every 15 s. The sensors spanned the 350-950 nm spectral range with a spectral resolution of 3. 3 nm. Data were recorded using a TriOS GmbH MSDA software using the most recent nominal calibration coefficients (set in 10/2014). Calibrated data for Ed, Lt and Ls were then interpolated to 1 nm intervals before processing. The Rrs signal was computed from each simultaneous above water measurements of irradiance, Ed, total upwelling radiance, Lt, and sky radiance, Ls, as  [32] : R rs " L t ´ρL s E d ( 1 ) where ρ is the air-water interface reflection coefficient.
Satellite Dataset The ocean color remote sensing dataset is composed of MODIS-A, MODIS-T, SEVIRI and OLI images acquired during the second TUCPA field campaign period  (17 to 20 February 2014). Over this period, we focus only on observations acquired during the 17 and 20 February which are cloud-free days. As OLI data are only available on 23 February, MODIS and SEVIRI observations for this day are also included for comparison. Two MODIS observations per day are available, one in the morning between 09:50 and 10:55 UTC (MODIS-T), and one at noon between 12:10 and 13:10 UTC (MODIS-A). SEVIRI images every 15 min are available for the three days, from 08:00 to 16:00 UTC. Some SEVIRI images show clouds that mask the Rhône River plume and were manually excluded from the dataset. The OLI image was recorded on 23 February at 10:24 UTC.
Satellite Data Processing Level 1A MODIS-A and MODIS-T data were downloaded from the oceancolor. gsfc. gov website then processed using the SeaDAS (version 7. 0) software (seadas. gsfc. nasa. gov) to generate geo-location and Level 1B files. Rrs(555), Rrs(645) and Rrs(859) Level 2 products were generated using the l2gen function. Four atmospheric corrections were tested to determine the most suitable for the moderately turbid waters of the Rhône River plume: (1) the MUMM  [37] ; (2) the NIR  [38] ; (3) the SWIR  [39, 40]  and (4) the NIR-SWIR  [36]  atmospheric correction algorithms. The MUMM atmospheric correction algorithm is based on the spatial homogeneity of the water-leaving radiances and aerosols ratios (α and ε respectively, see Table  1 ) between two NIR bands (748 and 869 nm for MODIS sensors)  [37].
54 for SPM < 30 g•m -3 ) then tends to saturate (Figure  4 ), precluding its use to estimate high SPM concentration values. Based on these results, a simple linear relationship using the red band of each satellite sensor appears as the most robust and accurate option to invert Rrs into SPM concentration in the moderately turbid waters of the Rhône River plume. These results are supported by previous studies Orthorectified and terrain corrected Level 1T OLI data was obtained from the Landsat-8 portal on ESA website (https://landsat8portal. eo. esa. int/portal/) then processed using the ACOLITE software (http://odnature. naturalsciences. be/remsem/acolite-forum/) [13, 18]  to obtain water-leaving reflectances. Two atmospheric correction algorithms are proposed in ACOLITE: the NIR algorithm  [13]  based on the MUMM approach and using the red (655 nm) and NIR (865 nm) bands, and the SWIR algorithm  [18]  using the high quality SWIR bands of OLI.
Comparisons with SEVIRI are made over the whole study area (cf. Figure  1 ). To minimize differences due to the natural dynamics of the plume between SEVIRI and other data acquisition times, SEVIRI Rrs products preceding and following the MODIS-A, MODIS-T and OLI acquisition times are weight-averaged before comparison. The time difference between OLI and MODIS-T on 23 February is 35 min but is higher than 2 h with MODIS-A. Because of this large time difference, MODIS-A and OLI Rrs products were compared considering only a polygon inside the river plume, where Rrs variations are less important than in the plume boundaries regions. For a better comparison, the same polygon is used for the comparison between Rrs derived from MODIS-T and OLI. Nevertheless, we can notice that scatter-plots on Figure  6  show a scatter that increases with the time difference between sensor observations.
In situ hyperspectral Rrs measurements from the TUCPA campaign dataset have been weighted by the spectral sensitivity of sensor spectral bands to obtain the equivalent Rrs values in the green, red and NIR spectral bands of the OLI, MODIS and SEVIRI satellite sensors (or only red for SEVIRI). The central wavelengths of the selected spectral bands are: 560 nm, 655 nm and 864 nm for OLI, 555 nm, 645 nm and 859 nm for MODIS, and 635 nm for SEVIRI. Empirical relationships were then established between Rrs in each sensor spectral bands and the corresponding in situ SPM concentration (Figure  4 ). The resulting relationships between Rrs and SPM are quite similar for the three sensors. Within the SPM concentration range sampled during the TUCPA field campaign (3-60 g¨m ´3), the Rrs signal increases linearly with increasing SPM concentration in the red and NIR bands (Figure  4 ).