dataesr/scientific-paragraphs-categorization huggingface dataset:
Decreasing Re and   has almost no effects on the final center of gravity position. This database parametric study focuses on the start of the trajectory. To give an order of magnitude, in typical ice trajectory experiments  [29] , the ice piece travels a dimensionless horizontal distance of 4. × 10 -3 , three times the horizontal distance in this database. The major limitation of this study is the 2D computational domain that precludes any comparisons against 3D experimental results. The computed 2D results should be equivalent to 3D results with a plate of infinite span and different from the results obtained with a plate of short span extent. Short span plates are typical of most engineering applications and experiments. Another limitation is the small number of runs used for the statistical analysis.
Modelling and Simulation in Engineering The numerical errors on aerodynamic coefficients observed during the oscillating airfoil validation could also have an impact on the database results. The aerodynamic moment coefficient oscillates typically between -0. 6 and 0. 6 during one trajectory calculation. The number of oscillation cycles is usually above three. Assuming the airfoil validation errors apply for the moving plate, the moment coefficient maximum values are underestimated by 20% and the minimum values overestimated by 20%. For one cycle, the numerical errors have probably neutral effects. Also, it is worth mentioning that, for Lagrangian methods, often used for trajectory calculations, 10% of errors on static aerodynamic coefficients are usually assumed, together with an unknown uncertainty on the damping function used to model the dynamic caused by rotational velocity  [5].
Conclusions A database of 64 trajectories for a 2D plate released in a moderate Reynolds number flow has been built using CFD. Apart from the plate starting angles and launch times, four parameters influence the trajectories: the Reynolds number, the Froude number, the Tachikawa number, and the dimensionless mass moment of inertia parameter. Both the Froude and the Tachikawa numbers have significant effects on the trajectories. It appears that increasing the Froude number increases the dispersion of the trajectories in a vertical plan. Lowering the Tachikawa numbers decreases the median value of the dimensionless vertical position. Launch times change significantly the trajectory, as indicated by scatter plots of the dimensionless vertical position as a function of horizontal position, due to the unsteady separated flow around the static plate.
The CFD tool used to build the database solves the penalized Navier-Stokes equations. The numerical method for the flow is based on a VIC scheme. The aerodynamic forces and moments are computed by integrating the penalty term and are used to compute the velocity from the solid motion equation. The velocity is imposed on the solid area of the flow, identified by a level set function. The forces and moments are validated against literature results for an inline oscillating cylinder and a flapping airfoil. The mean trajectory with a confidence interval gives a representative idea of the plate motion as long as the two starting angles are processed separately. The histograms of passage probabilities at a vertical position show the strong effect of the starting plate angle. The box plot representation allows a comparison of the effects of the four parameters on a single graph.
Acknowledgments (i) Numerical experiments presented in this paper were carried out using  MCIA  ( Mésocentre de Calcul Intensif Aquitain ) facilities, the  PLaFRIM  experimental testbed, and the supercomputer Mammouth Parallèle 2 from  Sherbrooke University , managed by  Calcul Québec and Compute Canada. The operation of this supercomputer is funded by the  Canada Foundation for Innovation (CFI) , the  ministère de l' economie, de la science et de l'innovation du Québec (MESI) , and the  Fonds de recherche du Québec -nature et technologies (FRQ-NT). PLaFRIM cluster is being developed under the  Inria PlaFRIM development action  with support from  LABRI  and  IMB  and other entities:  Conseil Régional d Aquitaine ,  FeDER ,  Université de Bordeaux , and CNRS; see https://www. plafrim. fr/fr/accueil/.
They are calculated according to the autocorrelation function of the sampled surface, AACF, which describes the dependence of the height data at one position on the values at another position of the same dataset. S al represents the shortest distance in which the value of the AACF has decayed under a threshold, normally 0. 2. Large values of this parameter represent surfaces dominated by low frequency components, that is, the surface features are repeated along longer distances. On the other hand, S tr allows to identify the existence of a preferential texture pattern. This ratio ranges between 0 and 1, being 0 for surfaces who show strong anisotropy, that is, with an outstanding preferential direction in the texture. ▪ Hybrid parameters. They combine spatial and amplitude features of the surface, by evaluating the degree of heterogeneity of its average slope.
Equipment and sampling strategy To calculate the specified parameters, the height distribution of every patch derived from all burnishing tests were acquired by an ALI-CONA Infinite Focus Microscope equipped with a 10× objective. In order to discard edge effects, only the central area of each burnishing patch was acquired. The system functions based on the focus variation technology, that is, by the acquisition of the height of all points composing the target surface by an optical head that takes images along a vertical 100-nm resolution. A 0. 1-μm sampling interval was used both in x and y directions. The size of each patch is described in the following subsection, as it varies for each of the three initial tested areas. The filtering of the dataset and the calculation of parameters were performed by the ALICONA IF MeasureSuite software.
, 2013). A number of hypotheses have been put forward to explain insular woodiness, such as (1) increased competition hypothesis (taxon-cycling hypothesis;  Darwin, 1859; Givnish, 1998) , (2) greater longevity hypothesis (promotion-of-outcrossing hypothesis;  Böhle, Hilger, & Martin, 1996; Wallace, 1878) , (3) moderate climate hypothesis  (Carlquist, 1974)  and (4) reduced herbivore hypothesis  (Carlquist, 1974). However, experimental data for these hypotheses are non-existing or based on only a few, small-scale examples. A recent review on insular woodiness of the Canary Islands showed that a majority of the insular woody species grow in dry coastal regions  (Lens, Davin et al. , 2013) , and an ongoing global derived woodiness database at the flowering plant level reveals a strong drought signal (F.
| Statistics To test the difference between P 50 , P 12 (pressure inducing 12% loss of hydraulic conductivity referring to initial air-entry pressure), P 88 (pressure inducing 88% loss of hydraulic conductivity referring to irreversible death-inducing xylem pressure in angiosperms;  Barigah et al. , 2013; Urli et al. , 2013)  and S (slope of vulnerability curve at inflexion point, an indicator for the speed at which embolisms affect the stem) with life form (woodiness vs. herbaceousness), we used generalized least squares (GLS). To deal with heteroscedasticity, we included a varIdent weights function  (Zuur, Ieno, Walker, Saveliev, & Smith, 2009). Statistical analyses were carried out using the gls function from the nlme package  (Pinheiro, Bates, DebRoy, & Sarkar, 2016)  in the R software (R Core Team 2016).
The regression or differences was considered to be significant if p ≤. 05. Further, we calculated the hierarchical partitioning  (Chevan & Sutherland, 1991)  for the significant variables retained in the model in order to assess their relative importance to explain the P 50. We performed simple linear regression between thickness of intervessel pit membrane (T PM ) and P 50 to assess a potential correlation. To deal with heteroscedasticity, we included a varFixed weights function  (Zuur et al. , 2009). We calculate the R 2 values based on the method of Nakagawa and Schielzeth (2013), using the function rsquared in the package piecewiseSEM  (Lefcheck, 2015). Furthermore, the regression was applied between P 50 and the proportion of lignified area per total stem area (P LIG ) for eight individuals measured of Cladanthus mixtus due to the high intraspecific variation in the degree of woodiness for this species.
To assess the correlation between the predictive variables and P 50 in the general dataset (woody + herbaceous species studied), as well as in the woody and herbaceous dataset separately, we performed Pearson's or Spearman's correlation analyses depending on the normality of the variable's distribution. P LIG in the general dataset was log-transformed to match the normality. Finally, we performed a Student's t test to assess the difference in pit membrane thickness between the insular woody and herbaceous group.
| Relationship between embolism resistance and wood anatomy A combined backward and forward multiple regression analysis shows that the thickness of intervessel pit membrane (T PM ), the proportion of xylem fibre wall per fibre (P FW F) and the hydraulically weighted diameter of vessels (D HV ) best explain the variation in P 50 (p =. 0018, R 2 =. 8578; Table  2 ). However, only T PM and P FW F are significant, with T PM explaining 70% and P FW F 30% of the variation in P 50 (Table  2 ). In the general dataset, the thickness of intervessel pit membrane (T PM ) correlates with embolism resistance (p =. 0021, R 2 =. 4425;.
| 1473 Functional Ecology DÓRIA et Al. 0. 21 to 0. 43; t test, p =. 0017; Figures  2e,f  and 3b ). T PM is also correlated with vessel grouping index in the general dataset and in the woody dataset (p =. 0448, r =. 6440; p =. 0221, r =. 9297), respectively. Aspirated intervessel pit membranes were very scarce and ignored in our measurements. In addition to the thickness of intervessel pit membrane (Figure  3a ), the following anatomical variables in the general dataset (herbaceous and woody species combined; Table  S2 ) are significantly correlated with P 50 : density of vessels (p =. 0028; r = -. 8317), vessel grouping index (p =. 0040; r = -. 8151) and the proportion of lignified area per total stem area (p =. 0060; r = -. 7952; Figure  2c, d ). However, when we analyse the woody species separately (Table  S2 ), only vessel grouping index (p =.
0055; r = -. 9722) and proportion of ray area per wood area (p =. 04997; r = -. 8784) are significantly correlated with P 50 , whereas all the significant correlations disappear in the herbaceous dataset probably due to the limited variation in P 50 amongst the herbaceous species studied (Tables  S1  and S2 ). The axial parenchyma patterns are very similar between the most resistant and most vulnerable Argyranthemum species (scanty paratracheal according to  IAWA Committee, 1989). The simple linear regression for C. mixtus individuals shows that the proportion of lignified area per total stem area is highly linked with P 50 for this population (p =. 0008; R 2 =. 84; Figure  4e ), which scales with the large intraspecific variation in the degree of woodiness in the stem (Figure  4a-d ).
| Stems of insular woody daisies are more embolism resistant than those of their herbaceous relatives Our xylem physiological embolism resistance data show one major outcome: stems of the insular woody Argyranthemum are more resistant to drought-induced hydraulic failure than those of their herbaceous Anthemideae relatives. Additionally, the difference in slopes of the vulnerability curves between woody and herbaceous species demonstrates that embolism formation occurs slower in the former (Figure  1 ; Table  S1 ). The positive link between increased wood formation and embolism resistance within the daisy lineage matches the observation that insular woody species native to the Canary Islands are often distributed in the dry coastal areas  (Lens, Davin et al. , 2013)  and agrees with an ongoing global derived woodiness database at the flowering plant level, comprising more than 6,000 species of which most of them are native to regions with a marked drought period such as (semi-)deserts, savannas, steppes and Mediterranean-type habitats (F.
| The relationship between increased embolism resistance and increased wood formation/lignification is strong but indirect In addition to the observed correlation between the proportion of lignified area per total stem area (P LIG ) and embolism resistance (Table  S2 ), we have also found another lignification link in our dataset: the proportion of xylem fibre wall per fibre (P FW F), which is a measure for fibre wall thickness in the xylem, explains 30% of the variation in P 50 (Table  2 ). As lignification in angiosperm shrubs and trees is mainly defined by wood fibres  (Zieminska, Butler, Gleason, Wright, & Westoby, 2013; Zieminska, Westoby, & Wright, 2015) , and as wood lignin content is positively linked to embolism resistance in a global dataset  (Pereira, Domingues-Junior, Jansen, Choat, & Mazzafera, 2017) , our observed (indirect) correlation between the proportion of xylem fibre wall per fibre and P 50 could be expected.
| Relationship between embolism resistance vs. vessel grouping index, vessel density and ray abundance Our general dataset shows that more embolism-resistant species have more vessels per xylem surface area (higher density of vessels-DE V ) compared to more vulnerable species. In addition, the general dataset and the woody dataset show that these vessels are grouped in larger multiples (higher vessel grouping index-G V ; Tables  S2  and S3 ). Higher vessel grouping patterns allow the continuity of 3D water transport pathway in case one or several vessels in a vessel multiple become embolized  (Carlquist, 1984; Lens et al. , 2011). On the other hand, increased vessel-vessel contact areas facilitate the potential spread of air bubbles from one embolized vessel towards an adjacent functional one via air-seeding  (Zimmermann, 1983).
Therefore, the competitive advantage of having higher vessel grouping index may only be valid when the thickness of intervessel pit membrane is large enough to prevent air-seeding within the vessel multiple, which is statistically supported by a tight correlation between the thickness of intervessel pit membrane and vessel grouping index in the general and woody datasets, while the same correlation is not found for the herbaceous dataset with their thinner intervessel pit membranes. Amongst the insular woody Argyranthemum species, there is a weakly significant, positive correlation between the proportion of ray area per wood area (P R , as seen in tangential sections) and embolism resistance (p =. 04997; r = -. 8784; Table  S2 ; Figure  S3 ). We observed in the field that all the leaves of the embolism-resistant Argyranthemum species native to the drier regions were functionally dead at the end of the dry summer, while new green leaves were starting to flush after the first rains set in September 2013 (Figure  S3b ).
| CON CLUS IONS We find that stems of the insular woody species of Argyranthemum are more resistant to drought-induced hydraulic failure than those of their herbaceous relatives native to the European main- We show that intervessel pit membrane thickness best predicts the variation in embolism resistance amongst the daisy species studied and functionally explains P 50 via its role in air-seeding. Moreover, the thickness of intervessel pit membrane is the essential missing link to understand the indirect correlation between embolism resistance and increased lignification, a correlation that has also been demonstrated in larger datasets  (Hacke, Sperry et al. , 2001; Lens et al. , 2016; Pereira et al. , 2017)  as well as within species  (Lens, Tixier al. , 2013; this study). Therefore, we argue that lignification characters do not have a direct impact on embolism resistance, but they co-evolve with other anatomical features that are more directly influencing P 50 in water-conducting cells  (Lachenbruch & McCulloh, 2014; Rosner, 2017).
land. Although this experimental result agrees with a marked drought signal in the ongoing global derived woodiness dataset including over 6000 derived woody flowering plant species representing several hundreds of transitions towards derived woodiness (F. Lens, unpublished data), this does not necessarily mean that drought has triggered wood formation in the common ancestor of Argyranthemum after arrival on the Canary Islands. Dated molecular phylogenies estimating the palaeoclimate in which the woody daisies have originated combined with a thorough niche modelling study including additional environmental variables (temperature, precipitation, aridity, potential evapotranspiration and soil) are likely to shed more light into this fascinating island phenomenon.
Acknowledgements We are most appreciative of ongoing help from  Kelly Rogers  and  Lachlan Whitehead  ( WEHI ) for helping with image acquisition and analysis. We would like to thank  Giel van Dooren  ( Australian National University ) and  Dominique Soldati-Favre  (The  University of Geneva ) for plasmids and antibodies. We would also like to acknowledge  Vern Carruthers  ( University of Michigan ) and  David Sibley  ( Washington University ) for providing us with antibodies. We are also indebted to  Cody Allison  ( WEHI ) for providing us with immortalised tdtomato: ROSA26   MEFs. This work was supported by  NHMRC  project grants  APP1025598  (CJT),  APP1047806  (CJT) and an  ARC   Future Fellowship   FT120100164  awarded to CJT CYB and NJK are supported by  Agence Nationale de la Recherche  ( LABEX PARAFRAP ANR-11-LABX-0024 ) and  CNRS-INSERM-Fondation FINOVI  ( Atip-Avenir Program Project Apicolipid ).
The in silico analysis of microarray datasets also suggested that the abundance of VAV2 and VAV3 transcripts is associated with low and high levels of CDH1 and NR2F1 transcripts in human breast cancer cell lines, respectively (Fig. 5c ). In line with this, we found that the knockdown of VAV2 and VAV3 transcripts in the epithelial T47D cell line (Fig. S4A ) leads to the downregulation and upregulation of CDH1 and NR2F1 mRNAs, respectively (Fig. S4B ). The fold change obtained in both cases was smaller than that found in 4T1 cells (compare Fig. 5  and S4B ), probably because the shRNA-mediated knockdown of VAV family transcripts was less efficient in the human than in the mouse cell line. We also observed that the transient expression of Nr2f1 results in the reduction of CDH1 mRNA levels in three independent human breast cancer cell lines that display an epithelial phenotype (T47D, MCF7, BT474) (Fig.
a Sequence logos of the transcription factor binding sites enriched in the promoter region of Vav-regulated microRNAs. b Changes in expression of the indicated transcripts in KD 2/3 versus parental 4T1 cells according to microarray analyses  [43]. c Abundance of indicated transcripts (top) in the Cancer Cell Line Encyclopedia collection of human breast cancer cell lines (left)  [65]. In red, we show cell lines used in subsequent analyses (left). Changes in abundance are plotted as in Fig. 3a. d Expression correlation matrix of indicated transcripts in the human breast tumor samples present in the GSE65194 microarray dataset. Positive and negative correlation is shown in red and blue, respectively. The size and color intensity of circles are proportional to the Pearson correlation coefficient found for each transcript pair.
VAV family and ZEB2 mRNA levels are inversely correlated in human breast tumors To assess whether the specific correlation of the expression of Vav, Nr2F1 and Zeb2 proteins found in 4T1 cells could be conserved in human tumors, we performed further coexpression matrix analyses using a large microarray dataset that contained samples from normal tissue as well as HER2 + , luminal A, luminal B, and basal breast tumors (n = 1097 samples). To further strengthen these analyses, we also included in the study the correlation of the foregoing mRNAs with transcripts known to be repressed by Zeb2 and/or deregulated during EMT processes (CDH1, PKP2, GJB2, GJB3, VIM). This approach revealed that the combined abundance of VAV family (VAV2+VAV3) transcripts does show an inverse correlation with the expression levels of both NR2F1 (Fig.
4  and 5 ). The direct correlation between ZEB2 and NR2F1 transcripts is recurrently detected in the luminal tumor samples from all the microarray datasets interrogated in this study (Fig. 7a, b ) whereas it shows a less consistent pattern in the case of samples from basal subtype tumors (Fig. 7b , brown bars). By contrast, the inverse correlation observed between the ZEB2 and CDH1 mRNAs is maintained in all the datasets surveyed regardless of the tumor subtype involved (Fig. 7b , blue bars). This suggest that the regulation of Zeb2 expression is probably subjected to different mechanisms in these two tumor subtypes. We found no statistically significant correlations of VAV, NR2F1 or ZEB2 transcripts with other putative Zeb2-regulated (GJB3, PKP2) and EMT-associated (VIM) transcripts (Fig.
7  Prognostic value of Vav EMT-and lung metastasis-associated gene signatures. a Expression correlation matrix of indicated transcripts in human luminal breast tumor samples present in the GSE65194 microarray dataset (luminal tumors)  [66]. In these analyses, the abundance of VAV2 and VAV3 mRNAs (VAV, red lettering) was pooled together to calculate the expression correlation values. The graphic representation was made as indicated in Fig. 5d. b Graph representing the Pearson correlation coefficient of the ZEB2 mRNA with indicated transcripts, tumor subtypes, and microarray datasets 1 (GSE65194)  [66] , 2 (GSE25066)  [67, 68]  and 3 (GSE78958)  [69]. The gray areas depict the nonstatistically significant values according to statistical threshold used in the analyses (P ≤ 0. 05). c, d Patients were stratified into high (red lanes) and low (blue lanes) groups according to their similarity to the miR-200c-Vav2;Vav3-associated EMT (c, top panels; d, left panel), the Vav2;Vav3-dependent prometastatic (c and d, middle panels), and the MammaPrint (c, bottom panels; d, right panel) signatures and curves plotted according to either distant recurrence-free survival (c) in the indicated patient groups (top, gray boxes) or lung metastasis-free survival (d).
Datasets used in these analyses were GSE25066 (for the survival analyses)  [67, 68] , GSE2603 (for the survival analyses)  [70] , and GSE2603 (metastasis free-survival)  [70]  are directly correlated with the capacity of 4T1 cells to form lung metastases  [26]. As comparative control, we utilized the MammaPrint gene signature currently used in the clinic  [42]. These signatures were tested in microarray-derived datasets of breast cancer patients to assess the ability to stratify patients according to overall patient survival, chemoresistance-related disease outcome, and lung metastasis-free survival. We found that the prometastatic Signature B is clearly the most robust, since it can stratify patients according to all the clinical parameters chosen in these analyses (Fig. 7c, d ). In fact, its prognostic power is even better than the MammaPrint signature in terms of overall survival (P = 0.
Genome-wide mRNA and microRNA expression profiling The Vav2;Vav3-dependent coding transcriptome of 4T1 cells was determined using Affymetrix microarrays (GEO reference, GSE33348)  [26]. To characterize the Vav2;Vav3dependent microRNAome, triplicate samples of total micro-RNA from control, KD 2/3 (A) , KD 2/3 (B) , KD 2/3 +V 2 , KD 2/3 +V 3 , and KD 2/3 +V 2/3 (Table  S1 ) were analyzed using the GeneChip miRNA 1. 0 array (Affymetrix). Data from these arrays have been deposited in the GEO database (GSE97385).