dataesr/scientific-paragraphs-categorization huggingface dataset:
Protein similarity assessment To set up an useful comparative framework based on updated and common descriptions of gene functions, a sequence similarity search versus the Swiss-Prot sequence database of the complete mRNA collections of S. lycopersicum and V. vinifera was performed using the tBLASTn program  [21] , setting an e-value cut-off at e -3 and the "max_target_seqs" parameter to 500. Only the alignments with a minimum of 90% identities and 90% coverage were retained for subsequent analyses. Among the accepted results, the tomato or grapevine mRNAs that matched a Swiss-Prot protein associated with an Enzyme Commission number (EC number) were also identified. In order to confirm the orthology relationships predicted by a BBH approach, we implemented a dedicated Python (v3. 3. 3) software to compare and match the strings of the updated functional descriptions of each ortholog couple.
Expression data analysis Processed RNA-Seq libraries (Accession SRP010775) from tomato were downloaded from the NexGenEx-Tom database  [74]. The grapevine RNA-Seq collections (Accession SRA009962) from Zenoni et al. [75]  were preprocessed with the same methodology as in  [74]. The two collections include three stages of S. lycopersicum cv. Heinz (2 cm fruit, breaker and mature fruit) and grapevine V. vinifera cv. Corvina (post-setting, veraison, mature berry) fruit in physiological conditions. The gene expression levels were defined based on the mRNA loci both for tomato  [76]  and grapevine (12X v1) annotations (this last by CRIBI  [68] ). Read counts were normalized by Reads Per Kilobases per Million (RPKM) for each tissue/stage. Genes with expression level below 1 RPKM were considered as not expressed.
Due to the differences in ranges of expression, and in order to have a better visualization of the profiling, the log2 transformation was applied for cluster analyses. The identification of the expression profiles was performed by the K-means clustering method  [77]. The Pearson correlation coefficient for pairs of gene expression profiles was used as the distance metric. The Figure of Merit (FOM) plot  [53]  was used to define the most appropriate number of clusters to be used for the K-means clustering. For each cluster a Hierarchical sub-clustering (HCL)  [78]  was performed using the Euclidean distance on the basis of expression levels. Both the clustering methods are implemented in the MultiExperiment Viewer (MeV) software  [79]. A Gene Ontology enrichment analysis of the BBH subset was conducted using the online software AGRIGO 1.
Availability of data and materials All the data presented in this work are publicly available and exploitable at the website http://biosrv. cab. unina. it/comparalogs/gene/search About this supplement This article has been published as part of BMC Bioinformatics Volume 19 Supplement 15, 2018: Proceedings of the 12th International BBCC conference. The full contents of the supplement are available online at https://bmcbioinformatics. biomedcentral. com/articles/supplements/volume-19-supplement-15.
Analysis of in vitro single molecule tracking data (from TIRFM of supported OMP-containing bilayers; see ref. 8  for details) allows the diffusional behaviour of individual OMP molecules to be classified as Brownian, confined, or mixed (Fig. 3a, b ). These diffusion modes fitted well with the different mobility behaviours that proteins displayed in the outer membrane in vivo  8. We therefore analysed the simulated OMP trajectories using the same analysis software (PaTrack 39 ) as for the experimental data. This enables us to classify the modes of diffusion seen in the simulations in the same manner as for the experimental single tracking data, using a back-propagation neural network algorithm, trained and tested on synthetic and experimental data. Analysis of the experimental data demonstrated that when OMPs (either BtuB or BtuB + OmpF) are present at high densities in the in vitro membranes the dynamic behaviour (of BtuB) is predominantly 'confined' or 'mixed' rather than Brownian (see Fig.
To estimate the fraction of the membrane surface area occupied by protein we first measured the average intensity of single BtuB-ColE9-TMR (i. e. one step photobleaching) when incorporated into a PSM at very low dilution. We then summed the intensities of pixels within several regions of interest (ROI) for a PSM incorporating BtuB at high concentration. Knowing the surface of each ROI, the intensity value corresponding to a single BtuB, and the transversal surface of a BtuB molecule, we can therefore calculate the surface coverage of BtuB over the PSM, yielding a figure from 20 to 40% from one experiment/ROI to another, with an average close to 25%. TIRFM videos and image sequences from MD simulations were analysed using custom software (PaTrack)  39. The centre of each fluorescence peak was determined with sub-pixel resolution by fitting a two-dimensional elliptical Gaussian function.
5 µs. Structures of BtuB and OmpF (PDB ID: 2YSU [dx. doi. org/10. 2210/pdb2YSU/ pdb] and 2OMF [dx. doi. org/10. 2210/pdb2OMF/pdb]) were converted to a CG representation using the martinize. py script (version 2. 4) downloaded from the MARTINI website (http://md. chem. rug. nl/index. php/tools2/proteins-and-bilayers) with the MARTINI2. 2 force field  34, 55  and the ElNeDyn elastic network model  56. The BtuB structure, 2YSU, had missing loop residues, which were modelled using Modeller  57. A grid of randomly rotated proteins was embedded into a pre-equilibrated phosphatidyl choline (PC) bilayer using g_membed  58. For simulations including BtuB or a mix of BtuB and OmpF, this initial grid contained 6 × 6 proteins; for simulations containing OmpF the initial grid contained 5 × 5 OmpF trimers, to take account of the difference in size between BtuB and the OmpF trimer.
To extrapolate values for translation and rotation to clusters containing more than 20 proteins the standard deviation of the rotations and one-dimensional translation vs. cluster size data was fitted using a power law function, y = Ax -b , where x is the cluster size and y is the rotation or translation as appropriate. Residual values for the fitting are shown in Supplementary Table  1  and show that the power law fitting was appropriate. The mesoscale simulations were performed using scripts written in Python, which made use of the NumPy 63 , MDAnalysis  64  and NetworkX  65  Python libraries. Analysis of simulation data. Analysis of CG-MD clustering and protein-protein interactions: Clustering analysis was performed using in-house Python scripts, making use of the NumPy, MDAnalysis and NetworkX Python libraries.
In the clustering algorithm, proteins were considered to be interacting when the centroids were within 6 nm of one another (for BtuB-BtuB pairs), 8. 8 nm (for pairs of OmpF trimes) and 7. 3 nm (for a BtuB-OmpF pair). Protein-protein interactions were identified using in-house clustering scripts, again making use of the NumPy and MDAnalysis modules. Residues of neighbouring proteins were considered to be interacting when residue centroids were within 0. 7 nm of one another. Analysis of CG-MD and mesoscale simulations using PaTrack: In order to analyse the simulations using the single molecule tracking analysis package, PaTrack  39  , CG-MD and mesoscale simulations were first converted to videos with an in-house Python script that made use of the libraries NumPy and SciPy. The videos were converted to spe format using ImageJ 66 (https://imagej.
nih. gov/ij/ index. html) and then analysed as for conventional single molecular tracking data using PaTrack. Visualisation: Systems were visualised using VMD  67  and graphs plotted using matplotlib  68  (https://matplotlib. org). Data availability. Data supporting the findings of this manuscript are available from the corresponding authors upon reasonable request. The code used to run mesoscale simulations can be found at https://github. com/annaduncan/ meso_model.
Acknowledgements This work was supported by grants to C. K. ( BBSRC LoLa  grant  BB/G02067/2  and  European Research Council Advanced  grant  742555-OMPorg ),  D. B. ( EPSRC Fellowship   EP/M002896/1 ),  J. P. (Deutsche Forschungsgemeinschaft   SFB 944 P8/Z ) and M. S. P. S. ( BBSRC BB/L002558/1 ,  Wellcome Trust   WT092970MA ,  Leverhulme Trust  and EPSRC). M. C. is funded by  CNRS "Mission pour l'interdisciplinarité" PEPS MPI 2017. P. R. was supported by an  Oxford University Goodger and Schorstein scholarship. T. R. acknowledges the  Canadian Institutes of Health Research (CIHR)  and  Somerville College ,  Oxford (Fulford Junior Research Fellowship ). Supercomputer time was provided via  EPSRC (ARCHER)  and  PRACE (MareNostrum, Barcelona ).
Context. Membership analyses of the DANCe and Tycho+DANCe data sets provide the largest and least contaminated sample of Pleiades candidate members to date. Aims. We aim at reassessing the different proposals for the number surface density of the Pleiades in the light of the new and most complete list of candidate members, and inferring the parameters of the most adequate model. Methods. We compute the Bayesian evidence and Bayes Factors for variations of the classical radial models. These include elliptical symmetry, and luminosity segregation. As a by-product of the model comparison, we obtain posterior distributions for each set of model parameters. Results. We find that the model comparison results depend on the spatial extent of the region used for the analysis. For a circle of 11. 5 parsecs around the cluster centre (the most homogeneous and complete region), we find no compelling reason to abandon King's model, although the Generalised King model introduced here has slightly better fitting properties.
Furthermore, we find strong evidence against radially symmetric models when compared to the elliptic extensions. Finally, we find that including mass segregation in the form of luminosity segregation in the J band is strongly supported in all our models. Conclusions. We have put the question of the projected spatial distribution of the Pleiades cluster on a solid probabilistic framework, and inferred its properties using the most exhaustive and least contaminated list of Pleiades candidate members available to date. Our results suggest however that this sample may still lack about 20% of the expected number of cluster members. Therefore, this study should be revised when the completeness and homogeneity of the data can be extended beyond the 11. 5 parsecs limit. Such a study will allow for more precise determination of the Pleiades spatial distribution, its tidal radius, ellipticity, number of objects and total mass.
In the case of the Pleiades, cross-matching the Hipparcos catalogue  (Perryman et al. 1997)  with the 2109 candidate mem-bers of  Bouy et al. (2015) , shows that only 70 of them have parallax measurements. This figure has roughly doubled with the first Gaia data release DR1  (Gaia Collaboration et al. 2016) , and is expected to improve based on the longer time baselines and hence more accurate measurements of subsequent Gaia releases. In preparation for the analysis of these upcoming data sets and to narrow down the set of models that will be tested in the context of 3D studies, we have initiated a re-examination of the current analytical alternatives to describe the PSD of the Pleiades cluster. bins centred at 5. 2, 1. 65, 0. 83 and 0. 3 M. Using tidal forces 1  , they iteratively constrained the tidal radius to a value of 13.
(2017). We avoid the binning biases by using Bayesian inference methods applied to continuous and thus non-binned distributions. In addition, these Bayesian methods allow a quantitative comparison of the competing models, including those with and without mass segregation. This will allow us to establish on firm grounds the analytical expression of the Pleiades PSD, and its potential dependence on stellar mass. In Section 2 we briefly describe the data set that forms the basis of our analysis. In Section 3 we present the set of radially symmetric analytical models we used, as well as their extension to biaxially symmetric (elliptical) profiles. We also include a luminosity dependence of the core radius (as a proxy to the investigation of mass segregation). In Section 4 we describe the foundations of model selection in the Bayesian framework.
Completeness of the sample To properly establish the probabilistic framework, it is necessary to take into account the observational constraints of the data. The Pleiades DANCe catalogue is constrained by its sky coverage and the different degrees of completeness (see  Bouy et al. 2013 Bouy et al. , 2015, for details), for details). Although the data set extends up to a radius of 6. 5 • ,  Bouy et al. (2015)  conservatively assume that the census is homogeneous in coverage and limiting magnitude only in the central 3 • radius area. Here, we estimate the completeness of the whole of the joint Tycho+DANCe survey in terms of the J band luminosity and spatial coverage, which also applies to our list of candidate members. In Fig. 1  we show the distributions of the number of sources in the combined DANCe+Tycho catalogue as a function of the radial position for different limiting magnitudes and bins in the J band.
Accounting for completeness and the previous truncation in the data set is essential to avoid possible bias in the inferred parameters (see Appendix A). Nevertheless, we remind the reader that the inhomogeneities (e. g. spatial resolutions, gaps in luminosity) of the DANCe+Tycho data set are so complex (and some of them only partially understood) that they can indeed bias the sample of candidate members in unknown ways. For example, the gap in luminosity coverage between the faint end of the Tycho-2 catalogue and the bright end of the DANCe survey (see Fig. 8  of  Bouy et al. 2015)  may result in undetected sources, therefore unmeasured proper motions and, finally, an incomplete list of candidate members. Another important constraint is the number of cluster stars observed within the survey area coverage.
Bayesian analysis As mentioned in Section 2, our data set may be contaminated. Thus, in an effort to minimise the possible impact that these contaminants may have on our inference, we also model their spatial distribution. Hence, our model of the spatial distribution of stars not only includes the model of the Pleiades cluster, but also a field component which is modelled by a uniform spatial distribution U within the maximum radius R max. The measured properties of each of star in our data set can be assumed to be unaffected by the measured properties of any other star in the data set (this assumption is called statistical independence). Under this assumption, the probability that the data set was generated by the mixture of cluster and field is the product of the probabilities that each of the stars was generated by this mixture.
Total mass and number of members In this Section we use the inferred values of the parameters in King's family of models to derive simple estimates of the total number of members and mass of the Pleiades cluster. For each model and extension within the King's family, we estimate the total number of cluster members by integrating the surface density profile until the tidal radii inferred for the model. This is done for each set of parameters returned by PyMultiNest. The resulting distributions of the total numbers fore each model and extension in the King's familly are shown in Fig. 2. Additionally, Table  6  shows the mode of these distributions. As can be seen from this Table, our current data set (with 1954 members), although twice as large as previous studies in the literature, still lacks almost one fifth of the predicted number of objects in the cluster.
The results of this work suggest that, although the Pleiades cluster is one of the most studied in the literature, the daughters of Atlas still keep many of their secrets within the oceans of the sky; probably awaiting the arrival of the final Gaia's data. Appendix A: Effects of truncation on King's profile. Statistical truncation occurs when an unknown number of sources lay beyond a threshold value. This threshold value can originate in the measuring process or in the post-processing of the data. The resulting data set does not contain any information about objects beyond the threshold. Performing inference on truncated data can bias the recovered parameters if the truncation mechanism is not included in the analysis. Nevertheless, bias can still appear if poor statistics are used to summarise the results.
Mean Relative Error For the biaxial and luminosity segregated models we also give the ellipticity distributions computed a posteriori from the core and tidal (when available) semi-major and semi-minor axes resulting from the PyMultiNest samples. Finally, this Appendix also contains the distributions of the total mass of the cluster derived from the radial and biaxial models in the King's family. Table  B. 12: Covariance matrix of the biaxially symmetric RGDP model. Table  B. 14: Covariance matrix of the luminosity segregated GDP model. The data are binned in three bins of the J band: J < 12, 12 J 15, and 15 < J (with colours green, cyan and magenta, respectively). The MAP is shown by means of three coloured solid lines, the colours correspond to those of the J band bins. In these MAPs, the core radius is increased accordingly to Eq.
Acknowledgements. We express our gratitude to the referee  Anthony G. A. Brown  for his kind and assertive comments which greatly improved the quality of this work. This research has received funding from the  European Research Council (ERC)  under the European Union's  Horizon 2020 research and innovation programme  (grant agreement No  682903 ,  P. I. H. Bouy ), and from the  French State  in the framework of the  "Investments for the future" Program ,  IdEx Bordeaux , reference  ANR-10-IDEX-03-02. D. Barrado  acknowledges the support by the  Spanish  project  ESP2015-65712-C5-1-R. E. Moraux  acknowledges financial support from the " StarFormMapper " project funded by the  European Union 's  Horizon 2020 Research and Innovation Action (RIA) programme  under grant agreement number  687528.
Within hilly areas, these relationships are likely to change with wind direction and vegetation development because of changes in airflow inclination  (Zitouna-Chebbi et al. , 2012 , 2015) , and therefore changes in aerodynamic properties  (Blyth, 1999; Rana et al. , 2007). In the context of obtaining continuous time series of evapotranspiration from EC measurements of LE, the current study aimed to examine and compare LE gap-filling methods over hilly crop fields. For this, we evaluated the performances of different methods before and after tailoring to the conditions of hilly crop fields. We used the following methodological framework. The experiment was set within a Tunisian agricultural hilly watershed with rainfed crops. It included the data collection and preprocessing, the analysis of the experimental conditions, and the analysis of the dataset to be filled.
We considered several gap-filling methods that differ in the use of ancillary information, either micrometeorological data or energy flux data other than LE. Given the possible influence of airflow inclination, the gap-filling methods were tailored by splitting the dataset on the basis of airflow inclination as driven by wind direction. We assessed the performances of the gap-filling methods by addressing (1) filling rate as compared to missing data after preprocessing, (2) retrieval accuracy on filled data, and (3) quality of gap-filled time series through energy balance closure. 2 The experiment: study site and materials.
Instrumental equipment and data acquisition On fields A, B, and C, each flux station collected measurements of the land surface energy fluxes: net radiation (Rn), soil heat flux (G), and sensible (H ) and latent heat (LE) fluxes. Table  2  displays the type of instruments used for each flux station along with acquisition and storage frequencies, and sensor accuracies according to manufacturers. The sonic anemometers, krypton hygrometers, and air temperature and humidity probes were installed at constant heights above ground level: 1. 98 m for field A, 2. 0 m for field B, and 2. 2 m for field C. The verticality of the sonic anemometers was carefully checked during the experiment with a spirit level, as described by  Zitouna-Chebbi et al. (2012). The latter reported a 1 • accuracy on sonic anemometer verticality, according to the experimental protocol and to the analysis of airflow inclination.
Data processing: calculation of net radiation and soil heat flux On fields A and B, the measurements of Rn were corrected for the effect of slope following the procedure proposed by  Holst et al. (2005). Details are given in  Zitouna-Chebbi et al. (2012 , 2015). Only direct solar irradiance was corrected by accounting for the angle between solar direction and the normal to local topography. Direct solar irradiance was empirically derived from total solar irradiance measured at the flux station. We characterized local topography with slope (topographical zenith with nadir as origin) and aspect (topographical azimuth with north as origin), both derived from a 4 m spatial resolution digital elevation model (DEM) obtained with a stereo pair of IKONOS images  (Raclot and Albergel, 2006). The correction for slope effect on Rn was about 50 W m -2 on average.
2. 6. 2). The atmospheric stability parameter ξ was negative most of the time, with few values larger than 0. 1, mainly during sunrise or sunset. The ξ median values were -0. 007, -0. 011, and -0. 010 for field A, B, and C, respectively. These values corresponded to conditions of forced convection (neutrality or low instability) induced by large wind speeds. We did not observe notable differences between northwest and south winds. Zitouna-Chebbi et al. (2012 , 2015)  obtained similar results with a dataset collected between 2003 and 2006 on different fields within the same study area. Overall, the analysis of wind direction and micrometeorological conditions indicated that the wind regime did not stem from valley wind or land-sea breeze. Indeed, the wind direction did not depict any diurnal course in relation to anabatic or katabatic flows or to land-sea temperature difference, while the ξ parameter did not correspond to conditions of atmospheric stability with free convection.
00, 0. 87, and 0. 98 m, for fields A, B, and C, respectively. Vegetation height measurements were next interpolated on a daily basis by using a logistic function. The vegetation height data indicated that the sonic anemometers and KH20 krypton hygrometers, set up around 2 m above soil surface, were located above the roughness sublayer. Indeed, the experimental conditions were typified by neutral or slightly unstable conditions that corresponded to a roughness sublayer extension from the ground up to 1. 43 × vegetation height  (Pattey et al. , 2006). Leaf area index of green leaves (GAI) was measured using a planimeter. Every 2 weeks, all leaves were collected within three 1 m long transects to derive a spatially averaged value. GAI reached its maximum on 11 April, and maximum values were 2.