dataesr/scientific-paragraphs-categorization huggingface dataset:
that rank keyphrases according to the sum of the weights of their component words  (Hasan and Ng, 2014; Boudin, 2015). This is indeed the case for the second and third baselines, and we partially address this issue by normalizing candidate scores by their length, as proposed in  (Boudin, 2013). We use the parameters suggested by the authors for each model, and estimate LDA topic distributions on the training set of each dataset. Our model introduces one parameter, namely α, that controls the strength of the graph weight adjustment. This parameter is tuned on the training set of the SemEval-2010 dataset, and set to α = 1. 1 for all our experiments. For a fair and meaningful comparison, we use the same candidate selection heuristic ( §2) across models.
Results Results for the baselines and the proposed model are detailed in Table  1. Overall we observe that our model achieves the best results and significantly outperforms the baselines on most metrics. Relative improvements are smaller on the  Hulth-2003 and Marujo-2012  datasets because they are composed of short documents, yielding a much smaller search space (Hasan and Ng, 2014). TopicRank obtains the highest precision among the baselines, suggesting that its -one keyphrase per topic-policy succeeds in filtering out topic-redundant candidates. On the other hand, TopicRank is directly affected by topic clustering errors as indicated by the lowest MAP scores, which supports the argument in favour of enforcing topical diversity implicitly. In terms of MAP, the best performing baseline is PositionRank, highlighting the positive effect of leveraging multiple features.
Additionally, we report the performance of our model without applying the weight adjustment mechanism. Results are higher or on-par with baselines that use topic information, and show that our model makes good use of the reinforcing relations between topics and the candidates they instantiate. We note that the drop-off in performance is more severe for F1@5 on the Semeval-2010 dataset, going from best to worst performance. Although further investigation is needed, we hypothesise that our model struggles with selecting the most representative candidate from each topic using TextRank as a unique feature. We also computed the topic coverage of the sets of keyphrases extracted by our model. With over 92% of the top-10 keyphrases assigned to different topics, our model successfully promotes diversity without the need of hard constraints.
A manual inspection of the topic-redundant keyphrases reveals that a good portion of these are in fact clustering errors, that is, they have been wrongly assigned to the same topic (e. g. 'students' and 'student attitudes'). Some exhibit a hypernym-hyponym relation while both being in the gold references (e. g. 'model' and 'bayesian hierarch model' for document H-7 from the Semeval-2010 dataset), thus indicating inconsistencies in the gold data.
MuSeeQ's bioimage informatics: the computer-aided image analysis tool Introduction to MuSeeQ's bioimage informatics The image-processing program ImageJ [56] was chosen as a platform to host MuSeeQ because it is in the public domain and it is commonly used by the biologist community; ImageJ is presented as the world's fastest image processing program written in pure Java (https ://image j. nih. gov/ij/featu res. html). It supports and deals with a wide range of file formats and works on many operating systems. Moreover, the open source platform Fiji (Fiji Is Just ImageJ)  [57]  provides many powerful image analysis solutions and a curated selection of plugins. MuSeeQ was thus written in the ImageJ's Java-like macro language to be easily implemented in Fiji as a module. An additional movie file shows how to install MuSeeQ on Fiji [see Additional file 1].
Among CNNs, we find Region-based CNNs (R-CNNs)  [78]  which classifies regions using deep CNN. Nevertheless, this method is very slow to apply. Fast R-CNN was developed to overcome this limitation but the latter is based on a selective search algorithm. Faster R-CNN  [79]  and R-FCN  [80]  were also developed to improve the speed and accuracy of the process. While the first method is time-consuming when a large number of proposals is used in its first-stage, R-FCN might be too local to be discriminative enough. Deep machine learning methods have proved nevertheless its performance in image-based plant phenotyping for fully automated quantitative trait identification and localization  [75]  as well as plant disease detection and diagnosis  [80]. Despite the unrivaled accuracy of these techniques, they often require hundreds, sometimes thousands of images and diverse datasets to be trained  [81].
Moreover, differences between the image format, size and depth of the dataset used for the training of the network and that used for the analysis can also lead to a misclassification and segmentation troubles for non experts, which led us to avoid CNNs. In our study, there is no necessity for the power of a top-down thresholding approach or machine learning methods. The algorithm used by MuSeeQ was thought as the combination of the global threshold-based and dynamic segmentation principles. We first modified the G. Landini Colour Threshold function based on an algorithm derived from the iterative intermeans method (IsoData algorithm;  [82] ). Then, we manually trained our threshold algorithm to a set of digital images. This allowed us to detect peaks in the hue, saturation and brightness value histograms describing as much as possible pixels corresponding to the soluble mucilages and seeds (Additional file 3).
Extraction of the parameters of interest The measurements performed by MuSeeQ are extracted from input images and exported in two tables of results, either for the soluble mucilage-or seed-related parameters (Fig. 2 ). MuSeeQ computes eleven mathematical shape descriptors. Five of the most relevant, both for the seed and the soluble mucilage, are summarized in Table  1  with their respective unit, description and abbreviation. Regarding the mucilage, the most important one corresponds to the area of the surface of released soluble mucilage in 2D (AreaM). MuSeeQ also computes the area of the seeds surface projected in 2D (AreaS), a classical way to access the seed size  [44, 47]. Interest in computing our algorithm under the Java-like macro language resides in its flexibility, which implies that the user can easily increase the number of parameters to analyse by simply adding them into the script (Additional file 6).
9970; P < 0. 0001), AreaS ( r2 = 0. 9743; P < 0. 0001) (Additional file 7), and the ratio (AreaM/AreaS) (r 2 = 0. 9962; P < 0. 0001) (Fig. 4d ). It is currently not possible to statistically compare our results obtained on the area of the soluble mucilage with other image-based analysis methods since they do not exist yet. However, SmartGrain is an image analysis software dedicated to the quantification of the seed shape parameters  [45]. To compare the results obtained with MuSeeQ versus SmartGrain, we applied a Bland-Altman comparison method on the most important seed shape parameter, i. e. AreaS, which confirms the precision of the measurements using MuSeeQ (Fig. 4f  ). The measurements performed with MuSeeQ slightly exceed those obtained with SmartGrain. This bias is explained by the scale setting step which can slighly vary between the two softwares.
Looking at the correlations matrix between seed shape parameters and other traits, the solidity of the seed (SolS), which indicates how curved the borders of the seeds are (Table  1 ), was found to positively correlate with AreaM and the ratio (AreaM/AreaS) (Additional files 8, 9). It suggests that the more flaxseed release soluble mucilage, the less flaxseed present curvarures on both sides. Among FAs, C18:0, C18:2, Fig. 6  Correlation matrix between traits linked to the soluble mucilage, seed shape and fatty acids. Only pairwise correlations between the most relevant parameters were displayed in the matrix. Correlations between other traits can be found in Additional file 3. The datasets used to build the matrix were obtained using MuSeeQ for the seed shape-and the soluble mucilage-releated traits (10 biological replicates) and from a GC-FID analysis for the FA content (2 biological replicates), both performed on the same recombinant inbred lines population in generation F8.
Analysis of the mucilage release for the RILs 84, 44 and 62 was performed by placing seeds in water containing 0. 01% toluidine blue O (Merk Millipore) without shaking. After 1 h seeds were observed using a light microscope (Axioplan 2, Zeiss, http://www. zeiss. fr/). For the soluble mucilages phenotyping of the RIL population (Fig. 6 , Additional files 8, 9), we analysed five seeds per RIL and six RILs per assay. We performed two technical replicates and the results were combined together. Finally, the dataset used in Fig. 7  is from experiments performed with MuSeeQ on one biochemical assay per species, except for flaxseeds where we combined the analyses of three different biochemical assays to increase the size of the dataset (Additional files  11, 12).
A gradient of 10 °C min -1 was used to increase the temperature to 180 °C and the temperature maintained at 180 °C for 30 s, ramped at 1. 5 °C min -1 to 220 °C, 30 s at 220 °C, followed by 30 °C min -1 to reach 250 °C and maintained at 250 °C for 5 min (run time: 38 min). Each mass spectrum was recorded with a scanning range of 50 to 800 m/z. X Calibur software (Thermo Scientific) was used for manual peak integration and analysis. Each FA species was identified according to its retention time by comparing it with those of pure standards (Fame mix C14-C22 Supelco-18917-1AMP). FA and standards were analysed under the same conditions. Pentadecane was applied as the internal standard. FA species concentrations for each sample were normalized against the internal control and expressed as a pourcentage of the total FA content.
Statistical analyses and data evaluation In order to validate flaxseeds segmentation using MuSeeQ, we used SmartGrain software  [45]. Linear regression analyses, scatter dot plots, Box and Whiskers plots and the Bland-Altman plot were carried out using GraphPad Prism (Version 5. 0, GraphPad Sofware, Inc. ). For linear regression analyses, the standard deviation of the residuals was computed to estimate the goodness-of-fit as: Frequency histograms were made using Matlab software (2014b, The Matworks Inc, Natick, Massachusetts, USA). After having checked that the variables were normaly distributed, Pearson's correlation analyses were performed using Statistica software v. 9. 1  (StatSoft, Inc. , 1984 -2010). The Pearson's correlation matrix and the corresponding likelihood matrix were created using Statistica (Additional files 8, 9) or after importation of the data into Matlab software (Fig.
6 ). For all statistical analyses, the normality and the homocedasticity of the datasets were checked using D'Agostino & Pearson omnibus normality and Bartlett's tests, respectively. Statistical differences between samples were tested using either a one-way ANOVA followed by a Tukey's HSD (honest significant difference) post hoc test (Fig. 4, 5 ) or a Kruskal-Wallis non-parametric test followed by a Dunn's multiple comparison post hoc test (Fig. 7 ), with *p < 0. 05; **p < 0. 01; ***p < 0. 001.
Funding This work was performed, in partnership with the SAS PIVERT, within the frame of the  French Institute for the Energy Transition (Institut pour la Transition Energétique (ITE) P. I. V. E. R. T. (www. institut-piver t. com) selected as an  Investment for the Future ("Investissements d' Avenir "). This work was supported, as part of the Investments for the Future, by the  French Government  under the reference  ANR-001. FMi, KP, and FMe wish to thank COST action  FA1006 Plant Metabolic Engineering for High Value Products (PlantEngine). The equipement utilized in this study were co-funded by the  Regional Council of Picardie And European Union  within CPER 2007-2020.
n, number of cells used to calculate averaged intensity profiles from three independent experiments; "0" on X-axis corresponds to nucleus center. c MDA-MB-231 cells embedded in 20 °C polymerized gel analyzed as in a. d Morphological analysis of DAPI-stained nuclei (see Supplementary Fig. 1a  for nucleus shape scoring criteria) in MDA-MB-231 cells in 3D collagen matrix under indicated experimental conditions. Data are mean % ± SEM from three independent experiments (except Ctrl at [37 °C], N = 2 and siNT, N = 6); (n), number of cells analyzed. P-values of Kruskal-Wallis test as compared to control condition in each dataset. e MDA-MB-231 cells expressing H2B GFP were embedded in 3D 37 °C or 20 °C polymerized gels. Cells were treated with ethanol (Ctrl) or GM as indicated. Nuclei were automatically tracked from time-lapse sequences obtained from three independent experiments and plot shows the distribution of nuclei speed.
An intriguing possibility is whether the nuclear piston mechanism may contribute to nucleo-anterior polarization of MT1-MMP compartments. More work will be needed to determine the contribution and integration of these mechanisms to the metastatic program to tumor cell plasticity. Future studies should also unravel how these mechanisms are integrated with and possibly control invadopodia formation and targeted delivery of MT1-MMP to the cell surface for dissolution of confining ECM fibrils.
Live-cell imaging in 3D type I collagen. For Inter-fibril distance estimation, a 50 µl drop of 2. 0 mg/ml fluorescently-labeled Collagen I was allowed to polymerize for 2 h at 37 °C or 20 °C as described above. Distances between collagen fibrils were measured from stacks of 30 optical sections acquired at 0. 5 μm-interval with a SP8 Leica laser confocal microscope in the xy, xz, and yz planes (15 µm depth) using a 63 × 1. 4NA oil objective, 4 detection channels (2 PMTs and 2 hybrid Detectors) and 405, 488, 561 and 633 nm laser lines. The system was steered by Leica Application Suite (LAS-X) software. For live cell imaging, glass bottom dishes (MatTek Corporation) were layered with 10 µl of a solution of 5 mg/ml unlabeled type I collagen mixed with 1/20-40 volume of Alexa Fluor 647-labeled collagen.
4NA Plan-Apo objective lens and a dual-output laser launch, which included 491 nm and 561 nm 50 mW DPSS lasers (Roper Scientific). Images were acquired with a CoolSNAP HQ 2 CCD camera (Roper Scientific). The system was steered by Metamorph 7 software. Automated tracking of endosome angular distribution. A homemade Matlab program (available on demand) was developed to track nuclei based on nuclear staining and create a velocity-dependent coordinate system to analyze MT1-MMP endosomes relative to the direction of displacement of the nucleus. Nuclei were automatically segmented from maximal z-stack projection of sequential time frames (see previous section) based on smoothing and thresholding and then were tracked based on the distance from their previous position. From the trajectory of each nucleus, speed (μm/min) and directionality (persistence) were computed for each consecutive pair of frames.
Averaged Col1-3/4 C intensity profiles. Col1-3/4 C neoepitope (collagen degradation) and DAPI (nucleus) intensity profiles were obtained using the line-scan function (maximal intensity) of Metamorph analyzing the back-to-front cell region including the nucleus for both signals (see Fig. 1a, c ). Then, we used a homemade Matlab program (available on demand) to normalize line-scans to correct for linescan length difference. Briefly, three reference points were manually defined based on the DAPI signal profile; the nucleus center and the back and front of the cell, respectively. These reference points were used to define a linear transformation such that they became 0, -1, and 1 coordinates, respectively on the normalized curves, allowing direct comparison of different profiles. Centrosome-nucleus distance measurement.
Cells were embedded in 2. 0 mg/ml type I collagen polymerized at 37 °C as above. After 16 h, cultures were fixed with 4% paraformaldehyde and stained using polyclonal rabbit anti-pericentrin antibodies. Detection was performed with fluorescently-labeled anti-rabbit antibody. DNA was stained with DAPI. Centrosome-to-nucleus distance was determined by overlaying pericentrin and DAPI images and extending a line from the centrosome (center of pericentrin staining) to the nearest point of the nucleus rim; length of this line was measured using ImageJ software tools. Microfabrication of microchannels. Micro-channels were prepared as previously described  69. Briefly, polydimethylsiloxane (PDMS) (GE Silicones, 10/1 w/w PDMS A/crosslinker B) was used to prepare 7 µm-wide micro-channels with 2. 5 µm constrictions from a self-made mold.
The centrosome displacement was measured before and after laser ablation over 25 s periods of time and scored as positive in the direction of cell movement or negative otherwise. Pre-and post-ablation displacements are measured in the same cell and paired in the statistical analysis. Statistics and reproducibility. All results are presented as the mean ± SEM of three independent experiments except for collagen degradation in 20 °C polymerized gel Fig. 1f  (N = 2), centrosome-nucleus distance analysis in siLMNAtreated cells in Fig. 3e  (N = 2) and Tks5 recruitment analysis in Fig. 5h  (N = 2). GraphPad Prism (GraphPad Software) was used for statistical analysis. Statistical significance was defined as *P < 0. 05; **P < 0. 01; ***P < 0. 001; ****P < 0. 0001; ns, not significant. Data were tested for normal distribution using the D'Agostino-Pearson normality test and nonparametric tests were applied otherwise.
Acknowledgements We thank the  Nikon Imaging Centre @ Institut Curie-CNRS and Cell and Tissue Imaging Facility of Institut Curie , member of the  France Bio Imaging national research infrastructure  ( ANR-10-INBS-04 ) for help with image acquisition,  Drs M. Bornens ,  S. Courtneidge ,  S. Etienne-Manneville ,  A-M Lennon-Duménil ,  F. Perez ,  M. Mercola ,  T. Mistelli ,  Q. Zhang  and  R. Vallee  for providing reagents for this study and  Dr I. Brito  for help with statistical analysis. E. I. was supported by a postdoctoral fellowship from  Ligue Nationale contre le Cancer , P. M. by a fellowship from  Fondation ARC pour la Recherche contre le Cancer , R. F. by a fellowship from  Ministère de l'Education Nationale, de l'Enseignement supérieur et de la Recherche , A. C. by a grant from  Worldwide Cancer Research  (Grant  16-1235  to P.
C. ) and S. A. G. by a grant provided by the program «Investissements d'Avenir» launched by the  French Government  and implemented by  Agence Nationale pour la Recherche (ANR)  with the reference  ANR-10-LBX-0038  ( Labex CelTisPhyBio ) part of the  IDEX PSL  ( ANR-10-IDEX-0001-02 PSL ). Funding for this work was provided by the program «Investissements d'Avenir» launched by the  French Government  and implemented by  Agence Nationale pour la Recherche (ANR)  with the reference  ANR-10-LBX-0038  ( Labex CelTisPhyBio ) part of the  IDEX PSL  ( ANR-10-IDEX-0001-02 PSL ) and by grants from  Ligue Nationale contre le Cancer  (Equipe labellisée 2015) and from  Worldwide Cancer Research  (Grant  16-1235 ) to P. C. and by core funding from  Institut Curie and Centre National pour la Recherche Scientifique (CNRS).
Evaluations of nuclear reaction data for the major uranium isotopes 238 U and 235 U were performed within the scope of the CIELO Project on the initiative of the OECD/NEA Data Bank under Working Party on Evaluation Co-operation (WPEC) Subgroup 40 coordinated by the IAEA Nuclear Data Section. Both the mean values and covariances are evaluated from 10 -5 eV up to 30 MeV. The resonance parameters of 238 U and 235 U were re-evaluated with the addition of newly available data to the existing experimental database. The evaluations in the fast neutron range are based on nuclear model calculations with the code EMPIRE-3. 2 Malta above the resonance range up to 30 MeV. 235 U(n,f), 238 U(n,f), and 238 U(n,γ) cross sections and 235 U(n th ,f) prompt fission neutron spectrum (PFNS) were evaluated within the Neutron Standards project and are representative of the experimental state-of-the-art measurements.
An iterative procedure involving differential data calcu-lated with the advanced reaction models was implemented in the evaluation system EMPIRE-3. 2 [15, 16] , and modelling of selected integral benchmarks such as the collection from the Handbook of Criticality Safety Benchmark Experiments  [17]  was followed to perform the present evaluation. Processing with the NJOY code system  [18]  was done to prepare the corresponding ACE libraries for use with the MCNP Monte Carlo transport code  [19]  in benchmark calculations. Differences in integral benchmark performance of a few selected benchmarks depending on nuclear data were used as guidance to improve the reaction models and to finetune the reaction model parameters iteratively. Following this procedure, accurate model-based evaluations which reproduce both differential data and selected integral measurements have been obtained, which are used as a prior information in a subsequent Bayesian update.
g. , fluctuations around 1 MeV in 235 U(n,f) cross sections and fluctuations from 2 keV up to 200 keV in 235 U(n,γ) cross sections). The non-physical (scaling) parameters could be interpreted as a compensation of the model defects. The MC calculated covariance prior was then fed to the GANDR system  [20]  which is the generalised least-squares code capable of adjusting the prior and constraining the covariances using the experimental data from the EXFOR database to produce the final evaluation. The same procedure was applied earlier in the evaluations of thorium, manganese, and tungsten as explained in Refs. [22, 23] , where further details can be found.
A. Resonance Range in n+ 235 U The computer code system SAMMY  [24]  was used to analyze experimental data in the resolved resonance region (RRR) up to 2. 25 keV using the multilevel multi channel R-matrix representation of the neutron cross sections defined according to the Reich-Moore (RM) approximation. Experimental conditions such as resolution function, finite size sample, nonuniform thickness, and nuclide abundances of sample, multiple scattering, self-shielding, normalization, background, and Doppler broadening were taken into account in performing the fitting analysis  [25]. Due to the small average level spacings of the 235 U nucleus, the entire resonance evaluation up to 2. 25 keV includes a large number of s-wave resonances with spin and parity, 3 -and 4 -, as displayed in Table  I.
The level densities, for both equilibrium deformation and saddle points, are described with the Enhanced Generalized Superfluid Model (EGSM)  [15, 16, 53]  with parameters appropriate for each deformation. The order of symmetry of the nuclear shape at saddles was incorporated by multiplying the level density with specific enhancement factors as given in Refs. [70, 71]. The full γ-cascade in the compound and residual nuclei included both continuum and discrete transitions, with a maximum multipolarity equal to 2. Gamma-ray transmission coefficients were obtained from γ-ray strength functions computed using the Modified Lorentzian model (MLO1 closed form) as detailed in Ref. [53] , renormalized to 2πΓ γ /D 0 determined from experimentally deduced val-ues of Γ γ and D 0  [53]. The module PCROSS in EMPIRE was used to calculate pre-equilibrium emission, incorporating a one-component exciton model with gamma, nucleon and cluster emissions  [72].