dataesr/scientific-paragraphs-categorization huggingface dataset:
The river forcing is generated using a hydrological modeltotal runoff integrating pathways (TRIP)  (Oki &   Sud, 1998). The river outflow calculated by TRIP was combined with data from Global Nutrient Export from Watersheds [GlobalNEWS  (Beusen et al. , 2009; Seitzinger et al. 2005) ] and used as nutrient river input to the model. Nutrients and oxygen are relaxed to climatology at the lateral boundaries. For initialization of NORWECOM, climatological values of nutrients and oxygen were used; all other variables were set to a constant low value. The capacity to assimilate ocean colour data has been developed using a version of the Ensemble Kalman Filter that includes both a Gaussian anamorphosis in order to handle the non-Gaussian model variables  (Simon & Bertino, 2009 ) and a state augmentation in order to estimate biological model parameters together with the model variables  (Simon & Bertino, 2012).
The horizontal spatial resolution is 1/16°. The physical forcing for biogeochemical processes is pre-computed at the same resolution by the ocean general circulation model MFS based on NEMO3. 4. MFS supplies the daily averaged fields of horizontal and vertical current velocities, vertical eddy diffusivity, potential temperature and salinity, in addition to surface data for solar shortwave irradiance and wind stress. The Med-MFC operational chain runs unattended twice a week in an offline mode. It is composed of four steps: (1) downloading of forcing and daily MODIS chlorophyll data and pre-processing, (2) 3D-var satellite chlorophyll assimilation, (3) 7 day forecast of the biogeochemical state of the sea and (4) MyOcean catalogue update. The biogeochemical run is composed of (1) 7 days of hindcast (the first day: analysis with data assimilation on Wednesday run only) and (2) 10 days of forecast for the Wednesday run.
esa-oceancolour-cci. org). A 12-year (1999-2010) reanalysis of Mediterranean Sea biogeochemical tracer fields at 1/8°has been made available as part of the recent MyOcean release V3. 1. It provides monthly 3D mean fields of chlorophyll, phosphate, nitrate, dissolved oxygen, NPP and phytoplankton biomass. A database of satellite derived (SeaWIFS) surface chlorophyll concentrations delivered by the MyOcean Ocean Colour Thematic Assembly Center was used for data assimilation. Data assimilation of surface chlorophyll concentration is performed weekly through a 3D variational scheme (3D-var) following  Dobricic and Pinardi (2008)  and modified as in  Teruzzi et al. (2014a). To statistically project surface information to a fully 3D chlorophyll field, EOF analysis has been applied to a multi-annual (1998-2004) OPATM-BFM run  (Lazzari et al.
2012 ) identifying 12 months and nine sub-regions. The efficiency of the procedure was assessed by visual inspection (see Figure  3 ), as well as by comparing predicted surface chlorophyll fields to satellite-derived observations and in situ data  (Teruzzi et al. 2014a). For the purpose of model validation and assessment of the impact of monovariate chlorophyll assimilation on the overall model prediction skill, an extensive dataset was collated for dissolved oxygen, nitrate, phosphate, dissolved silica and NPP  (Lazzari et al. 2012). It combines biogeochemical measurements available at the OGS National Oceanographic Data Centre with additional recent cruises. The metrics introduced are based on nearestpoint statistics. Model bias and correlation were calculated according to  Allen et al. (2007)  and summarized in Table  3  for chlorophyll, dissolved oxygen and nutrient data for the reanalysis and the corresponding free simulation (hindcast).
Impacts were also seen in the dissolved inorganic carbon and alkalinity fields. Where systematic pCO 2 errors were not reduced, it was concluded that this was due to sparse observational coverage in those regions. However, the assimilation had a long memory, with information from the increments retained by the model for several months. This demonstrates that even a limited number of observations can have a substantial and lasting impact on the model results. In future, this method can be applied to larger pCO 2 observation databases such as the Surface Ocean Carbon Atlas (SOCAT)  (Bakker et al. 2014)  in order to produce long-term reanalyses. A development could be to assimilate physical, chlorophyll and pCO 2 data simultaneously, with cross-covariances or balance relationships between the physical and biogeochemical variables, thereby making maximum use of available observations.
M. G. , P. B. , C. P. , Y. D. and P. L. acknowledge support by the French research programme LEFE-GMMC under the grant agreement no. 452 PPR-GREEN-MERCATOR-II. R. B. and D. F. would like to thank ACRI for providing the GlobColour chlorophyll data and acknowledge funding support from the UK Ministry of Defence and from the European Space Agency for funding the Climate Modelling User Group (Contract 4000100222/10/I-AM) as part of its Climate Change Initiative. The European Community's Seventh Framework Programme FP7 (2007-2013) provided support under grant agreement no. 283367 (MyOcean2) to P. B. , R. B. , D. F. , L. B. , A. S. , C. P. , Y. D. and A. C. , and under grant agreement no. 264879 (CarboChange) to L. B. , E. S. and M. G.
In the gait literature, the harmonic ratio has been used as a measure of smoothness  [24] [25] [26] 29]. The harmonic ratio (HR) is defined as the ratio of the sum of the magnitudes of the even harmonics to that of the odd harmonics of the trunk acceleration over a single stride  [29]. The smoothness of the entire gait data is analysed by segmenting the data into individual strides. HR exploits the inherent periodicity of gait and is primarily a measure of gait symmetry of the two legs  [29]. Although symmetry may be an important component of how one would visually judge the overall smoothness of a person's gait, it is a very different factor from that of smoothness. For instance, one may walk with perfectly symmetric but unsmooth (very intermittent) gait pattern. Therefore HR does not appear to be an appropriate measure of smoothness.
A measure must have good sensitivity, particularly in the physiological range that covers the entire spectrum of healthy and un-healthy movements. Reliability is directly related to the robustness of the measure to measurement noise 1 , i. e. the amount of variability introduced in the measure by a given level of measurement noise. Practicality refers to issues related to the practical implementation of the measure, which needs to consider factors such as computational complexity of the measure (e. g. how does the computational time increase with increase in data size). For movement smoothness, a valid measure must be independent of the movement amplitude and duration, i. e. dimensionless. Smoothness is measured through the amount of movement intermittency, which is directly related to the movement's temporal organization or coordination.
In this paper, we introduce a slightly modified version of the original SAL, which we call SPARC for SPectral ARC length, by setting ω c min ω max c , min ω , V (r) < V ∀ r > ω (5) The SPARC extends SAL in that ω c is adaptively selected based on a given threshold V and is upper-bound by ω max c. In contrast to SAL, SPARC is independent of temporal movement scaling, and retains the good sensitivity and reliability of the SAL. A detailed explanation of the SPARC measure and the rationale for its development is given in Appendix A of the Additional file 2. The entire analysis presented in the paper was carried out using iPython  [41]. The entire analysis can be found online at https://github. com/siva82kb/SPARC.
Same noise results in less smoothness for slow movements Smoothness measures are affected by noise, and changes in SNR in movement data can affect smoothness estimates differently (Figure  S4  and Figure  S5  in Additional file 2); the bias and variability in a smoothness estimate increases with decrease in SNR. The SNR of a given movement data is determined by the amount of signal power and the amount of noise power. In any given movement recording set-up, the measurement noise can be assumed to have a constant power. Under such a condition, slower movements would have a lower SNR than faster movements, which could mean that the smoothness of slower movements might be underestimated compared to that of the faster movements, even when both the (uncorrupted) movements' speed profiles have the same shape, except for the scaling of the amplitude and duration.
Acknowledgements This work was funded in part by the  COST Action TD1006 "European Network on Robotics for NeuroRehabilitation " [1], by the  European projects CONTEST  ( FP7-PEOPLE-ITN-317488 ) and COGIMON ( ICT-644727 ) and the  UKIERI  project ( UKUTP201100135 ). We would also like to thank  Dr Sadhana Yadav ,  Mr Harish Kumar , and  Mr Aravind Nehru  for proof reading the manuscript.
[ 21 ] The evolution of 3D microstructures of electrode materials has mostly been quantifi ed previously via X-ray CT by comparing before and after morphological evolution. [ 22, 23 ] 27] In this study, we capture the evolution and quantify the strain in the bulk architecture of the MnO 2 electrode material in a commercial primary Li/MnO 2 lithium battery during a high rate, constant resistance discharge. As Li/MnO 2 cells have a high discharge rate capability (they are often used for high drain applications), we use the high speed imaging capability of beamline ID15A  [ 28 ]  at ESRF-The European Synchrotron. Furthermore, we use two different DVC software packages developed by Hall et al. [ 25, 26 ]  and Bay et al. [ 24 ]  to track the transient local displacements of the MnO 2 electrode in 3D as lithiation-induced dilation occurs.
The Duracell CR2 battery was imaged in a 76 keV monochromatic X-ray beam with a fi eld of view (FOV) of 8. 6 mm × 8. 7 mm and a pixel size of 10. 87 µm (794 × 800 pixels), corresponding to half of the cell (see Figure  1  ). The sample stage rotation axis was placed at the edge of the FOV, such that by a 360° rotation it was possible to image the entire sample. [ 30 ] Each tomogram was composed of 2 × 2000 half projections. Tomograms were captured with angular increments of 0. 09° between images. A high speed camera, PCO Dimax (PCO AG, Germany), with an exposure time of 0. 7 ms was used during imaging. Consequently, the acquisition time of each tomogram was 2. 8 s which was suffi ciently fast to avoid image artifacts caused by sample motion during acquisition. During continuous X-ray CT, tomograms were captured every 40 s which included image acquisition time and data transfer to the ESRF central servers.
Continuum DVC and Strain Field Evolution In this study, the CR2 cell is discharged from its fully charged state (3 V) to below 2 V, as shown in Figure  2. The MnO 2 is reduced from tetravalent to trivalent state to form Li x MnO 2 (where 0 ≤ x ≤ 1) as the Li + ion inserts into the MnO 2 lattice. [ 32, 33 ] D displacement measurements of textured materials can be performed by comparing X-ray CT images via DVC. [ 24, 26, 34 ] The continuum DVC in this work uses TomoWarp2 code developed between the Universities of Lund and Grenoble building on the previously developed code, TomoWarp of Hall. [ 25, 26, 35 ] In the current context, continuum DVC refers to the displacement calculation over a 3D grid of node points that are then used to determine 3D tensor strain fi elds following continuum mechanics.
The DVC approach can be described briefl y as having the following main steps: • A regular 3D grid of nodes was defi ned over the CT image volume, and a "correlation window" was centered on each node for the correlation analysis. In the current study, the grid spacing of the nodes was 5 voxels in each direction and the correlation window had a volume of 15 × 15 × 15 voxels. • The displacement for each node was derived by identifying the 3D translation that maximizes the cross-correlation of the gray-level data within the correlation window in the nondeformed image and the translated correlation window in the deformed image. The displacement fi eld so determined had a precision of 1 voxel in each direction. • Subvoxel resolved translation vectors were determined based on the maximum of the interpolated correlation coeffi cient for a range of displacements around the initial integer voxel value.
Conclusions DVC software has been applied to high speed operando X-ray CT of commercial primary lithium batteries during discharge. By quantifying the movement of discrete sections of the electrode material between sequential images during discharge, a spatial and temporal map of material activity and strain profi les was generated. The progression of the lithiation front has been tracked by measuring the local displacement of electrode material during dilation for the fi rst time which has provided valuable insights into the transient structural mechanics that take place within commercial cells during operation. For example, relatively inactive regions were identifi ed and large concentration gradients within the electrode layers could be deduced from the evolving strain patterns, giving the capability to pinpoint ineffi ciencies and identify means through which the performance of the cell could be improved.
Acknowledgements The authors gratefully acknowledge funding from the  Engineering and Physical Sciences Research Council (EPSRC) , the  Royal Academy of Engineering  and the  National Physical Laboratory (NPL). The authors are grateful to the  Science and Technology Facilities Council (STFC)  for travel funding through the  Global Challenge Network in Batteries and Electrochemical Energy Devices. These experiments were performed on the ID15A beamline at the  ESRF, Grenoble, France. The authors are grateful to  Marco Di Michiel  and  Mario Scheel  at  ESRF  for providing assistance in using beam-line ID15A.
Below we discuss how the available data on the origins of different genes of RNA viruses bear on these distinct origin scenarios. Positive-strand RNA viruses: Assembly from diverse prokaryotic progenitors and gene exchanges leading to enormous diversification Large-scale phylogenomic analysis of the þRNA viruses of eukaryotes was initiated over two decades ago and yielded conclusions that withstood the test of time remarkably well  (Goldbach and Wellink, 1988; Koonin, 1991b; Koonin and Dolja, 1993). These studies have identified three major evolutionary lineages that collectively encompass the vast majority of the þRNA viruses infecting eukaryotes: picornavirus-like, alphavirus-like and flavivirus-like superfamilies (Fig. 2 ). This classification is based on a combination of evidence from the RdRp phylogeny with signature genes and gene arrangements that have been identified for the picornavirus-like and alphavirus-like superfamilies (see below).
3d ; Po0. 05, t-test) and Black NHK (38 ± 4%; Fig. 3e ; Po0. 05, t-test) when compared with the control epidermis (4±12%). Exosomes carry miRNA that regulate pigmentation. The observations that miRNAs are possible modulators of pigmentation in melanocytes  [27] [28] [29]  and that EVs sequester miRNAs  18  led us to investigate whether the observed effects on melanin content and gene expression could be mediated by miRNAs contained within an exosomal vehicle as suggested above  30. As a result, we analysed the miRNA composition of Black, Caucasian and irradiated Caucasian NHK exosomes (Supplementary Data 3). Of note, the miRNA profile of keratinocyte exosomes is modified after ultraviolet B exposure  31. Interestingly, only one miRNA, hsa-miR-3196 for which a precise target has not been reported in the literature, was differentially expressed between irradiated and non-irradiated Caucasian NHK exosomes (Fig.
Target ions already selected for MS/MS were dynamically excluded for 180 s. Processing of MS data: Data were acquired using the Xcalibur software (version 2. 0. 7) and the resulting spectra were then analysed via the MascotTM Software created with Proteome Discoverer (version 1. 4, Thermo Scientific) using the SwissProt Homo sapiens (human) Protein Database. Carbamidomethylation of cysteines, oxidation of methionine and protein N-terminal acetylation were set as variable modifications for all Mascot searches. Specificity of trypsin digestion was set and two missed cleavage sites were allowed. The mass tolerances in MS and MS/MS were set to 2 p. p. m. and 0. 8 Da, respectively. The estimated false discovery rate of all peptide and protein identifications was less than 1%, by automatically filtering on peptide length, mass error and Mascot score of all peptide identifications.
Interaction of exosomes with melanocytes. FITC-labelled exosomes were used to characterize the binding and/or the internalization of exosomes from keratinocytes to melanocytes. The Fluoreporter FITC protein labelling kit (F-6434, Molecular Probes) was used to label exosomes, according to the manufacturer's instructions. Exosomes were labelled and the excess of FITC-reactive solution was removed by rinsing in 1 ml PBS. The final washing supernatant was used as the negative control in the flow cytometry analysis. Data were acquired on a BD Accuri C6 flow cytometer and analysed in FlowJo. Purified exosomes resuspended in PBS were labelled with PKH67 (Sigma), as control PBS without exosomes was used. The uptake was performed at 37 °C for 24 h by incubating melanocytes with the same number of NHK from which exosomes were recovered.
4 numerical aperture CFI (chrome-free infinity)). Images are maximum-intensity z projections of three-dimensional image stacks acquired every 0. 2 mm using the Metamorph software (MDS Analytical Technologies, Sunnyvale, CA). Electron microscopy. For ultrathin cryosectioning and immunogold labelling, the cells were fixed with 2% PFA or with a mixture of 2% PFA and 0. 2% glutaraldehyde in 0. 1 M phosphate buffer, pH 7. 4. Cells were processed for ultracryomicrotomy (70 nm of thickness), immunolabelled with anti-CD63 and immunogold-labelled using PAG10 as reported  38. For EM of the isolated exosomes, a drop of exosomes suspended in PBS was deposited on Formvar-carbon-coated electron microscopy grids, fixed as above, immunolabelled and stained using the method described for ultrathin cryosections  38.
All samples were analysed using a FEI CM120 electron microscope (FEI Company), and digital acquisitions were made with a numeric camera (Keen View; Soft Imaging System, SIS, Germany). Image analysis and quantification. Quantification of exosome size and number, and MVB number was determined using the iTEM software (Soft Imaging System). miRNA array profiling. All experiments were conducted at Exiqon Services, Denmark. The quality of the total RNA was verified by an Agilent 2100 Bioanalyzer profile. Total RNA (140 ng) from sample and reference was labelled with Hy3 and Hy5 fluorescent label, respectively, using the miRCURY LNA microRNA Hi-Power Labeling Kit, Hy3/Hy5 (Exiqon Services) following the procedure described by the manufacturer. The Hy3-labelled samples and a Hy5-labelled reference RNA sample were mixed pair wise and hybridized to the miRCURY LNA microRNA Array 7th (Exiqon Services), which contains capture probes targeting all miRNAs for human, mouse or rat registered in the miRBASE 18.
0. The hybridization was performed according to the miRCURY LNA microRNA Array Instruction manual using a Tecan HS4800 hybridization station (Tecan, Austria). After hybridization the microarray slides were scanned and stored in an ozone-free environment (ozone level below 2. 0 p. p. b. ) in order to prevent potential bleaching of the fluorescent dyes. The miRCURY LNA microRNA Array slides were scanned using the Agilent G2565BA Microarray Scanner System (Agilent Technologies Inc. , USA) and the image analysis was carried out using the ImaGene 9. 0 software (BioDiscovery Inc. , USA). The quantified signals were background-corrected (Normexp with offset value 10, and normalized using the quantile normalization method, which we have found produces the best between-slide normalization to minimize the intensitydependent differences between the samples.
Acknowledgements We are grateful to  Eric Gooris  for insightful discussions and support during the course of this work. We are grateful to  Allison de Horsey  ( St George's School, Newport, RI, USA ) for critical reading and editing of the manuscript. We are grateful to  Matteo Gentili  for help with FACS analysis and to  Joanna Kowal  for the exchange of expertise on techniques. We thank  Lucie Sengmanivong  and  Vincent Fraisier  from the  PICT-IBiSA Lhomond UMR144 Imaging facility of Institut Curie  and the  Nikon Imaging Centre at Institut Curie-CNRS , members of the  France-BioImaging  national research infrastructure. We acknowledge  France-BioImaging  infrastructure supported by the  French National Research Agency  ( ANR-10-INSB-04 , )Investments for the future*). This work was supported by  Institut Curie ,  CNRS  and Clarins.
NMR titrations DNA and RNA probes used for titration experiments were chemically synthetized by IDT (Supplementary Table  S3 ). Protein samples were prepared in NMR buffer (see above) and titrated with the nucleic acid probes reaching 1:1 and 1:2 excess of DNA/RNA (100-200 M). 1 H-15 N HSQC spectra were acquired for each titration point and the chemical shift perturbation (CSP) was calculated for each amide signal using the following formula: δ av = ( δ 1H ) 2 + δ 15N 5 2 • 0. 5 CSP results were mapped into the calculated structures/models using PyMOL v0. 98 software.
Fluorescence anisotropy binding titrations Measurements were performed in a BMG Polarstar Galaxy plate reader using 96-well black plates (Corning) regulated at 26 • C, with 485 and 520 nm excitation and emission filters, respectively. The concentration of the fluorescein labelled oligonucleotides (IBA GmbH) was 40 nM and the buffer was 20 mM Tris-HCl, 150 mM NaCl, pH 8. 0. A 1:1 binding model compatible with the experimental data was fitted to the isotherms using BIOEQS software  (26) , to obtain the free energy of formation of complexes from their individual elements employing a numerical solver engine. Errors in the fitting parameters were obtained by confidence limit testing, using the same software, at the 67% confidence level. No changes in the fluorescence emitted by the fluorescein dye were observed upon binding of any of the tested proteins.
Gbp2/THO interaction is not probably just driven by shape complementarity recognition. Our data reveals that the tyrosine hydroxyl groups are not structurally important for Gbp2 RRM3 fold (Supplementary Figure  S18A ), but are crucial for THO recognition (Tyr to Phe mutation reduces binding dramatically) very likely by participating to a complex network of hydrogen bond interactions. The human proteome has around ∼1600 RRM annotated. We found that only SRSF1 RRM1 and SRSF9 RRM1 have a YxYGG motif in loop 5. Moreover, the structure of SRSF1 RRM1 shows remarkable similarities with Gbp2/Hrb1 RRM3 in the region around this loop (Supplementary Figure  S17B ), which leads us to predict that SRSF1 and SRSF9 might be recruited to the human THO in a similar manner than Gbp2. To our understanding this is a novel hypothesis and might serve to discover new functional links in the SRSF field.
ACCESSION NUMBERS Atomic coordinates for the NMR structure ensembles have been deposited on the PDB: Hrb1 RRM1 (2MZR), Hrb1 RRM2 (2MZS), Hrb1 RRM3 (2MZT) and Gbp2 RRM3 (2MZQ). NMR data are available from the Biological Magnetic Resonance Data Bank (accession numbers: 25497 for Hrb1 RRM1, 25498 for Hrb1 RRM2, 25499 for Hrb1 RRM3 and 25496 for Gbp2 RRM3).