dataesr/scientific-paragraphs-categorization huggingface dataset:
For each cell line at each time point, the significance of differences between Wnt3a-treated and control cells was determined and a correction for multiple testing was applied with Benjamini & Hochberg's methodology  [52]. Only genes with significantly different expression (P < 0. 05), with a log2 fold change superior to 0. 3785 (i. e. fold change > 1. 3) or inferior to -0. 3785 (i. e. fold change < 1. 3), were selected. Pathway enrichment analysis was carried out with GeneTrail  [53]. A hypergeometric test, corrected for multiple testing with the Benjamini-Hochberg method, was used to assess the significance of the over-representation of biological annotations among gene lists. The transcriptomic data of Wnt3a-treated cells are available in Gene Expression Omnibus (GEO) (accession number: GSE65238).
4 software (Applied Biosystems) with the following settings: 5 min at 42°C (RT step), 5 min incubation at 95°C followed by a three-step cycling program with 40 cycles of 15 sec at 95°C, 30 sec at 60°C and 30 sec at 72°C (PCR step). A post-PCR dissociation analysis step was included according to instrument guidelines to distinguish specific from non-specific amplification products. All data were normalized to endogenous actin expression.
Tissue samples and microarray data The human samples used in this study have been previously described  [41, 42]. RNA microarray (Affymetrix U133 Plus 2. 0) performed on 41 TNBC, 30 HER2 + , 30 luminal B (LB), 29 luminal A (LA) and 11 healthy tissue breast samples, have also been previously described  [41, 42]  (GEO accession number: GSE65216).
Analysis of the enrichment of the up-and down-regulated Wnt target genes in human breast tumor samples We analyzed whether the Wnt target genes we identified were enriched in human tumor samples. We compared the gene expression data obtained in TNBC cell lines stimulated with Wnt3a with that obtained in our cohort of 130 human breast cancer samples  [41, 42]. The two experiments were not done with the same arrays: the cell line (Gene st 1. 1, Affymetrix) and the tumor (U133 Plus 2. 0, Affymetrix) experiments contained 19738 and 11543 genes, respectively. We restricted our study to the 11262 genes present on both arrays. Of note, AXIN2 was not found in our tumor dataset. We applied a FDR (False Discovery Rate) cut-off of 0. 05 and a log-2 fold change threshold of 0. We used the Fisher exact test to assess the significance of the intersection between the genes that were up-or down-regulated upon the stimulation of cell lines with Wnt3a, and those that were more strongly or more poorly expressed in tumors (TNBC, HER2+, LB) than in LA samples.
Several gene sets were considered. For the cell line experiment, we considered the up-or down-regulated genes, the different time points (6, 12 and 24 hours) and the two TNBC cell lines. For the breast tumors, we considered the genes that were more strongly or more poorly expressed in the three different subgroups of tumors (TNBC, HER2+ and LB) than in LA samples. Each subgroup was considered separately and all comparisons between the different breast cancer subtypes were performed. The comparisons between tumors and cell lines were performed with data obtained in HCC38 and MDA-MB-468 cell lines. However, there was no significant overlap between the MDA-MB-468 and the tumor datasets, possibly because of the very low number of genes found to be differentially expressed in Wnt3a-stimulated MDA-MB-468 cells.
In HCC38 cells, more genes were up-regulated at 12h and 24h after stimulation with Wnt3a than at 6h (Fig 2B, S1 Dataset). These genes may be "direct" or "secondary" Wnt target genes. However, one can postulate that the gene list found at the earliest time point (6h) is likely more enriched in "direct" Wnt target genes than those at later time points (12h, 24h). Therefore, we next focused on the genes up-regulated at 6h. Half of the target genes (30 out of 59) identified in MDA-MB-468 cells incubated for 6h with Wnt3a were also up-regulated in HCC38 cells after the same duration of treatment (Fig 2C, S2 Dataset). Among the 30 common genes, 23 were previously reported as Wnt target genes (Fig 2C  , Table 1 ). As expected, the "universal" Wnt-induced genes AXIN2 and NKD1  [12]  were up-regulated in both TNBC cell lines (Fig 2C, S2 Dataset).
These two genes showed the highest fold change in HCC38 cells incubated with Wnt3a: 10. 2 for NKD1 (P = 2. 2x10 -23 ) and 9. 86 for AXIN2 (P = 6. 4x10 -19 ) (S2 Dataset). In addition to AXIN2  [35, 37, [56] [57] [58] [59] [60] [61] [62] [63]  and NKD1  [58, 59, [68] [69] [70] , the 21 other previously reported Wnt target genes (Fig 2C  , Table 1 ) that were up-regulated in both cell lines were: APCDD1  [36, 40, [54] [55] [56] [57] [58] , BMP4  [37, 38, 57, 64] , BMP7  [57] , C13orf15  [58] , C21orf63  [58] , CYP24A1  [38] , FAM171B  [58] , KRTAP3-1  [57] , LBH  [66] , MFSD2A  [58] , PPFIBP1  [58] , PTGER2  [71] , RNF43  [37, 57, 58, 72, 73] , ROR1  [57] , SEMA3A  [35, 37, 56, [59] [60] [61] [62] [63] , SLC39A10  [37] , SNCAIP  [58] , TMEM64  [58] , XKRX  [57] , ZNF703  [37, 58]  and ZNRF3  [37, 57, 58, 73].
Therefore, we report seven novel Wnt target genes among the genes that were up-regulated in both cell lines: B3GNT2, C2orf54, DDIT4L, IGFL4, LPAR1, SNTB1 and TFPI2 (Table  1 , Fig  2C ). In addition to the 30 genes common to both cell lines at the 6h time point, which could be considered as specific to breast cancer cells,  29   Altogether, this analysis revealed 270 genes that are potentially activated by Wnt3a at 6h; 133 (49%) were previously described as Wnt-induced genes and 137 are putative novel Wnt target genes (S3 Dataset).
stanford. edu/group/nusselab/cgi-bin/ wnt/target_genes). In agreement with these reports, we found that Wnt3a down-regulated the expression of several genes in TNBC cell lines. The incubation of cells with Wnt3a for 6h, 12h, or 24h downregulated (fold change < 1. [40, 58] , none of the genes down-regulated in both TNBC cell lines have been previously identified as Wnt target genes (Table  1 ). In addition to these genes, 12 and 160 Wnt target genes were specifically down-regulated at 6h in MDA-MB-468 and in HCC38 cells, respectively (Fig 3  , S1 Dataset ). Altogether, 178 potential targets were retrieved from our analysis; only 74 of them (42%) including FBXO32 have been previously described as Wnt target genes (S3 Dataset). Therefore, our study revealed 104 putative novel target genes that are down-regulated in TNBC cells stimulated with Wnt3a (S3 Dataset).
Validation of Wnt target genes in HCC38 and MDA-MB-468 triplenegative breast cancer cell lines We sought to confirm that the Wnt targets identified by microarray analysis were deregulated in TNBC cells stimulated with Wnt3a; therefore, we examined the expression level of several  genes by qRT-PCR. We selected early Wnt target genes (6h) found in both cell lines ("up": APCDD1, AXIN2, NKD1 and DDIT4L; "down": PCDH8 and PPP1R3C) (Fig 2C, Fig 3C, S2 Dataset). The magnitude of change in gene expression following Wnt3a treatment differed between qRT-PCR and microarray analysis, but the overall pattern was similar (Table  2  and Table  3 , S2 and S3 Figs). The expression of APCDD1, AXIN2, DDIT4L and NKD1 was up-regulated in MDA-MB-468 cells within the first 3-6h of Wnt3a treatment, and was subsequently down-regulated later on (Table  2 ).
The expression of PPP1R3C was down-regulated at 3h of Wnt3a treatment and that of PCDH8 was down-regulated at 6h of Wnt3a treatment (Table  2 ). We also carried out similar time-course experiments with Wnt3a in HCC38 cells (Table  3 ). The expression of APCDD1, AXIN2, DDIT4L and NKD1 was up-regulated at 3h and reached a maximum level at 6h (Table  3 ). By contrast, the levels of PPP1R3C and PCDH8 transcripts were lower in cells treated with Wnt3a for 3h than in resting cells, and reached their lowest levels at 6h (Table  3 ). The expression of PPP1R3C and PCDH8 was more strongly down-regulated in HCC38 cells than in MDA-MB-468 cells, consistent with the microarray data (S2 Dataset, S2 and S3 Figs). Overall, qRT-PCR experiments on a selection of genes validated the microarray data and both analyses revealed a time-course of target gene expression similar to that reported by others  [32, 78].
Analysis of pathways deregulated in HCC38 and MDA-MB-468 triplenegative breast cancer cell lines upon Wnt3a treatment The Wnt/β-catenin pathway activates but also represses the transcription of genes encoding products that may positively or negatively regulate the canonical Wnt pathway. Indeed, 34 out of 270 genes that were up-regulated in cells stimulated with Wnt3a for 6h, and 12 out of 178 genes that were down-regulated under the same conditions, encode known activators or inhibitors of this signaling pathway (S3 Dataset). Expression changes of these Wnt regulators are necessary to maintain Wnt signaling and to avoid the uncontrolled activation of the canonical pathway. Although most Wnt target genes identified in both TNBC cell lines (24 out of 36, Table  1 ) have been previously described in other studies, their role in the Wnt/β-catenin pathway needs to be established.
The exceptions to this are APCDD1, AXIN2, BMP7, NKD1, RNF43 and ZNRF3, which are already known to regulate negatively the canonical pathway (S3 Dataset). Among the 12 potential novel Wnt target genes identified in HCC38 and MDA-MB-468 cells (Table  1,   REDD2 (product of the gene DDIT4L) activates the TSC (tuberous sclerosis complex) complex and negatively regulates mTOR signaling  [79, 84]. However, Wnt has also been shown to promote mTOR activity by preventing the activation of the TSC complex through two mechanisms: the inhibition of GSK3β, which is a known activator of the TSC complex, and the activation of the small GTPase Rac1  [80]. Additional experiments would be required to examine whether REDD2 induced by Wnt3a in TNBC cells attenuates mTOR activity that is initially stimulated by Wnt signaling (negative feedback loop).
In colon cancer cells, the binding of lysophosphatidic acid (LPA) to its receptors LPA 2 and LPA 3 but not to LPA 1 (product of the gene LPAR1) activates the Wnt/β-catenin pathway  [81]. Thus, it is possible that LPA 1 plays a role in the aberrant activation of β-catenin signaling in TNBC cells. Further analyses are needed to confirm this hypothesis. Only one paper mentions a link between Wnt and TLR1, reporting that Wnt3a suppresses pro-inflammatory responses to TLR1/2 ligands in dendritic cells  [82]. We used KEGG analysis to examine enriched pathways among the genes positively or negatively regulated by Wnt3a in each cell line to explore the effect of Wnt signaling on cellular functions (S4 Dataset). We focused mainly on HCC38 cells because the number of deregulated genes was very different between the two TNBC cell lines and higher in HCC38 cells (Fig  2  and Fig 3).
Wnt target genes that were up-regulated in HCC38 cells stimulated for 6h with Wnt3a were associated with the TGFβ, Wnt, p53 and Hedgehog signaling pathways, whereas the down-regulated Wnt target genes were associated with calcium and mTOR signaling pathways (S4 Dataset). Some of these pathways (e. g. , TGFβ and p53 pathways) were still deregulated even after 12h or 24h of treatment. Many Wnt target genes play a role in Wnt regulation (S3 Dataset); therefore, we expected to find the Wnt signaling pathway among the affected pathways. Interestingly, the pathways affected in both HCC38 cells and MDA-MB-468 cells (S4 Dataset) control proliferation (e. g. , Hedgehog, Jak-STAT, p53 and Wnt signaling), apoptosis/autophagy (e. g. , p53 and mTOR signaling) and inflammation/immune responses (e. g. , TGFβ and toll-like receptor signaling, and cytokine-cytokine receptor interactions).
These results are not surprising given the well documented crosstalk between Wnt signaling and these pathways  [80, [85] [86] [87] [88] [89] [90] [91] [92]. Moreover, Hedgehog, TGFβ, Jak-STAT and toll-like receptor signaling pathways are deregulated in breast cancer and play a role in its development  [93] [94] [95] [96]. Altogether, these data may provide insight into the effect of aberrant Wnt/β-catenin signaling on biological processes involved in the development of TNBC/BLBC. Comparison of our list of Wnt target genes identified in HCC38 and MDA-MB-468 triple-negative breast cancer cell lines with previously published lists We compared our list of Wnt target genes with previously published lists (activated and repressed genes were compared separately) (S5 Dataset)  [34, 35, [37] [38] [39] [40] [56] [57] [58] [59] [60] [61] [62] [63] 97, 98]  to determine whether the Wnt target genes that we identified were specific to breast cancer cells.
Interestingly, IRF7 was the only common gene that responded to both Wnt3a and Wnt1 in these cells (see list of "down" genes)  [34, 35]. We found that EGR1, FAS and SLC7A2 were up-regulated and DIO2, ETS1 and VEGFA, were down-regulated in TNBC in response to Wnt3a, consistent with reports in Wnt1-treated C57MG cells  [34, 35]. Among the 248 target genes identified in normal murine mammary gland epithelial cells treated with Wnt3a, seven were common to our study ("up": AHR, AXIN2, CCND1, GADD45G and HS3ST1; "down": MMP13 and TNFAIP2)  [98]. Overall, these observations show that very few Wnt target genes identified in breast cancer cells were common to other tissues; thus, only a handful can be considered as 'universal' target genes e. g. APCDD1, AXIN2 and IRS1 (S5 Dataset). Unexpectedly, NKD1, another well-known Wnt target gene, was reported in only one  [58]  out of the 13 selected studies (S5 Dataset).
Although they are derived from the same tissue, the TNBC cell lines HCC38 and MDA-MB-468 display strong differences regarding the number and the identity of Wnt target genes. Similar observations were made in the colorectal cancer lines DLD1 and SW480 depleted in β-catenin  [58]  (S5 Dataset). In addition, C57MG mammary epithelial cells treated with Wnt1  [34]  or Wnt3a  [35] , two canonical ligands from two different classes, display different gene expression profiles (S5 Dataset), suggesting that a particular Wnt ligand affects the expression of a specific set of Wnt target genes. Altogether, these data confirm that Wnt target genes are diverse, tissue-, cell line-and treatment-specific. Therefore, we believe it is necessary to perform similar analyses in many different breast cancer cell lines (triple negative/basal-like and luminal) after stimulation with various Wnt ligands to obtain a more complete picture of Wnt target genes in this tissue.
We  We assessed the significance of the overlap between the genes that were up-regulated in Wnt3a-stimulated HCC38 (6h, 12h, 24h, and those up-regulated at both 6h and 24h) and those that were strongly expressed in tumors (TNBC, HER2+, LB and LA) (gene nb). We restricted our study to the 11262 genes present on both arrays (HCC38 dataset, tumor dataset). The number of the Wnt target genes that were more strongly expressed is indicated for each tumor comparison. The associated P value is also shown underneath (in Italics). The significance of the overlap between two lists was assessed with the Fisher exact test. As an example, 194 of the 419 Wnt target genes up-regulated in Wnt3a-stimulated HCC38 cells (6h) were more strongly expressed in TNBC than in LA samples (194 of the 3497 genes more strongly expressed genes in TNBC than in LA samples) (P value = 3.
However, it is well established that the activation of the Wnt/β-catenin pathway induces cell proliferation, in agreement with our KEGG pathway analysis (S4 Dataset). Therefore, long exposure (24h) of cells to Wnt3a will also lead to the activation or repression of genes linked to proliferation. Breast cancer subtypes proliferate at different rates: TNBC are the most proliferative tumors, followed by HER2+, LB and LA tumors (for an example, see the expression profile of several markers of proliferation in tumors, S5 Fig). Thus, it is possible that in breast cancer subtypes, the enrichment of Wnt target genes identified in cell lines stimulated with Wnt3a for 12h or 24h reflects proliferation, which may, or may not, be related to Wnt activation. However, it is impossible to distinguish Wnt activation from proliferation, because both are closely linked.
These Wnt target genes were also enriched in TNBC vs. HER2+ (P = 1. 7x10 -8 ) or LB tumors (P = 4. 0x10 -10 ) (Table  5 ). Interestingly, these genes were also significantly enriched in HER2+ tumors (P = 0. 033), albeit to a lesser extent than in TNBC, but were not enriched in LB tumors (P = 0. 15) (Table  5 ). The high expression of these 72 genes may reflect an activation of the Wnt pathway in TNBC samples. When we restricted our analysis to the most strongly deregulated genes (with a fold change > 1. 3), 17 genes out of 72 genes were retained (Fig 5 ), including 11 genes previously identified as Wnt target genes: SLC25A19, CDC6, CDC25A, C1orf109, C1orf135, PLEKHO1, PAX6, PSTPIP2, MFSD2A, FZD7, ARL4C (S3 Dataset). Among those genes, we identified the Frizzled 7 receptor (FZD7) (Fig  5 ).
3) the expression of 18, 16, and 10 genes, respectively in MDA-MB-468 cells (Fig 3A, S1 Dataset) and 166, 306 and 250 genes, respectively in HCC38 cells (Fig 3B, S1 Dataset). Wnt3a down-regulated the expression of more genes in HCC38 cells than in MDA-MB-468 cells, consistent with our previous observation that more genes are affected by Wnt3a in HCC38 cells than in MDA-MB-468 cells (Fig 2). One third (six out of 18) of the transcripts down-regulated in MDA-MB-468 cells incubated with Wnt3a for 6h were also down-regulated in HCC38 cells after the same treatment: C10orf81, CLDN8, FBXO32, PCDH8, PPP1R3C and TLR1 (Fig 3C, S2 Dataset). Among them, C10orf81, FBXO32 and PCDH8 were also down-regulated in both cell lines after 12h of Wnt3a treatment (Fig 3C, S2 Dataset). The expression of FBXO32 in stimulated cells after 24h of treatment was still lower than in resting control cells (Fig 3C, S2 Dataset).
Acknowledgments We are indebted to  Caroline Hego  and  Emilie Henry  for generating the microarray data. We also thank  Dr Céline Baldeyron ,  Amélie Brisson ,  Dr François Lallemand  and  Bérengère Marty-Prouvost  ( Institut Curie ) for helpful discussions. We acknowledge  Amélie Brisson ,  Bérengère Marty-Prouvost  and  Dr David Silvestre  for their critical reading of the manuscript.
The present study investigates whether the relationship between sensory singularities, focal colors, and unique hues depends on the characteristics of the illuminants, the reflectances, or the sensors. For this purpose, we examine this relationship (a) using different kinds of artificial illuminants, (b) using sensors with spectral sensitivities other than the human photopigments, and (c) with different sets of reflectance spectra that still result in colors similar to Munsell chips under neutral illumination. Preliminary results of these investigations were previously presented  (Witzel, Cinotti, & O'Regan, 2014). In the supplementary materials, we also provide a set of MatLab functions for computing sensory singularities. These functions are slightly different from those used in previous publications in a way that allows for more robust calculations across the different conditions investigated in the present article.
As in  Philipona and O'Regan (2006) , illuminant spectra were normalized so that their maximum corresponded to 1. In order to represent the sensitivity of the human photoreceptors,  Philipona and O'Regan (2006)  applied 108 Stiles and Burch color-matching functions  (Stiles & Burch, 1959), and Vazquez-Corral et al. (2012)  used the cone fundamentals of  Smith and Pokorny (1975). We used the 28 Stockman-Sharpe cone fundamentals of  Stockman and Sharpe (2000)  for the simple reason that they directly refer to the sensitivity of the human photoreceptors and are particularly precise. In any case, the results do not depend on which measurements of cone sensitivities are used (for details, see the Sensors section). The reflectances for glossy Munsell chips were retrieved from the database of the Joensuu Color Group  (Kohonen, Parkkinen, & Jaaskelainen, 2006; Parkkinen, Hallikainen, & Jaaskelainen, 1989) , which is now available via the University of Eastern Finland (http://www.
Focal colors and unique hues Data on focal colors were obtained from the World Color Survey, a cross-cultural study on color naming  (Regier et al. , 2005). In this study, observers from 110 nonindustrialized societies were asked to indicate the typical colors of the color categories that correspond to the basic color terms of their language. They could choose among the aforementioned set of 320 chromatic and 10 achromatic (grayscale) Munsell chips. The relative frequencies of prototype choices across all observers indicate the focality of the Munsell chipsthat is, the extent to which their color is focal. The contours in Figure  4b  illustrate the relative frequency of prototype choices across the set of 320 chromatic Munsell chips; it is plotted the same way as in figure  2  of  Regier et al.
(2005)  and as in the corresponding graph in figure  3  of  Philipona and O'Regan (2006). The modes of these focal color choices (innermost contours) coincide with the prototypes of red, yellow, green, and blue in English (for details see figure  2  in  Regier et al. , 2005). They also coarsely coincide with the unique hues. According to Kuehni, Shamey, Mathews, and Keene (2010), Munsell chips 5R4/14, 5Y8/14, 2. 5G5/12, and 2. 5PB5/12 correspond to unique red, yellow, green, and blue, respectively (see table  1  in  Kuehni et al. , 2010). These Munsell chips are highlighted as black circles in Figure  4  and subsequent contour plots  (Figures 6, 7, 10, and 14). Philipona and O'Regan's (2006)  approach to determining the singularities of the reflectances consists of three steps. The MatLab functions section in the supplementary materials provides the functions we used to implement these steps in the present study.
Philipona and O'Regan (2006)  showed that the residual variation that is not explained through the linear transformation is almost zero (no more than around 0. 015 of the total variance) for each of the reflectances (cf. figure  1  in  Philipona & O'Regan, 2006). Hence, reflected signals of a given surface across a wide range of illuminations may be approximated with almost perfect accuracy by a linear transformation of the LMS signal of those illuminations. The matrix A that accomplishes the linear transformations is specific to a given surface reflectance. Our function ''A_maker'' computes the linear regression and provides the transformation matrix A. This function uses a slightly different least squares method than the original algorithm of  Philipona and O'Regan (2006). For details, see the MatLab functions section in the supplementary materials.
Our function ''ev_calculator'' calculates the eigenvalues of the transformation matrix A for each reflectance (see the MatLab functions section in the supplementary materials). The algorithm of this function is equivalent to the one of  Philipona and O'Regan (2006). Step 3: Singularity index The third step consists of the calculation of a singularity index. In order to compare singularities of the matrices corresponding to different surfaces, an index was needed to assess the singularity of a transformation matrix A with a single number. When one or two eigenvalues are zero, the transformation is unambiguously two-or one-dimensional, respectively. The major challenge consists of evaluating when an eigenvalue can be judged as being close to zero. To solve this problem,  Philipona and O'Regan (2006)  proposed the following method.