dataesr/scientific-paragraphs-categorization huggingface dataset:
II. METHODS (1) Literature curation in Gephebase Gephebase compiles pairs of alleles in association with pairs of phenotypic states (a genetic variation causing a phenotypic variation is called a 'gephe' for brevity). A full description of the database is provided in  Courtier-Orgogozo et al. (2020). Data currently cover all eukaryotes with relevant publications, with a focus on traits of evolutionary rather than clinical relevance. This includes variations that have been artificially selected by breeders ('Domesticated' data set), or subject to experimental evolution under laboratory-controlled selective pressures. Data are manually curated by a small team of researchers, using key words as well as Pubmed/Google Scholar citations to identify newly published studies. The current triage system gives priority to gephes identified by forward geneticsquantitative trait locus (QTL) mapping, and genome-wide association studies (GWAS) with single-gene resolution and reasonable supporting evidence for causality (if not for the variant, at least for the gene).
Gephes identified by candidate gene approaches without mapping are also included when there is additional functional evidence for the causality of the mutation. Gephebase is up to date and gathers all relevant papers published until 2017. Since 2017, data curation efforts have mainly focused on colour variation in vertebrates (trait category = 'Coloration'). The download of Gephebase data on 15 August 2022 (see online supporting information, File S1, tab: All Vertebrates), which was used for the present study, represents a compilation of the genes and mutations contributing to vertebrate colouration up to August 2022. (2) The vertebrate pigmentation data set To examine trends in colour pattern evolution, we utilised Gephebase as a resource for exploring genotype-phenotype relationships. We formed a working data set by selecting every Gephebase entry pertaining to the trait category 'Coloration', and further filtered those entries by removing all entries pertaining to non-vertebrate species  (accessed 15.
08. 2022). In total we retrieved 389 entries, with each entry representing a mutation at a single locus. Each mutation was present in only one gene for the organism in which it was identified, and there were a total of 68 unique genes identified across 103 vertebrate species. For each entry, we examined five parameters defined by Gephebase: Gene ID, Taxonomic status, Study methodology, Aberration type, and Molecular type  (Courtier-Orgogozo et al. , 2020). We further defined five additional parameters detailed below: Clade, Pigment type, Phenotype category, Cellular function category, and Molecular function category (supporting information, File S1, tabs: All Vertebrates, GO Terms by Gene). (3) Assignment of new parameters (a) Clade Each vertebrate represented in the data set belonged unambiguously to one of five clades with distinct pigmentation biology: amphibians, teleosts, squamates, birds and mammals (Table  1 ).
(b) Pigment type In total, five pigment types were represented in the data set: biliverdin, carotenoids, pteridines, melanins, and psittacofulvins. Additionally, four entries referred to structural colouration. This is a short list of pigments, but few studies identified the specific pigment molecules associated with a phenotype. For example, a phenotype may clearly implicate a change in the synthesis or distribution of melanin pigments, without distinguishing between pheomelanin and eumelanin. Hence, these two types of pigment are grouped in the melanin category. Finally, there were 13 entries in which a single genetic variant was associated with changes to multiple pigments. These entries were assigned to each relevant pigment type category.
(e) Molecular function category We assigned protein molecular function categories nonempirically by using the gene ontology (GO) terms associated with each gene in the data set. We employed the EBI QuickGO Mouse Slimmer in order to identify parent GO terms associated with the sets of child terms associated with each gene. This slimmer was selected as being the most representative of the distribution of GO terms within vertebrate clades. We then narrowed the set of GO terms to include only those associated with the 'Molecular function' GO tree (see supporting information, File S1, tab: GO Assignments). We additionally manually combined a number of closely related GO terms in order to reduce the number of potential.
Vertebrate pigmentation analysis categories (see Table  4 ; supporting information, File S1, tab: Molecular Function Summary). In total there were 89 unique assignments of parent GO categories across the data set. All bar two of the 68 unique genes in the data set generated at least one assignment. The only two exceptions were goldentouch, a previously undescribed gene identified in Midas cichlids with no known molecular function, and mlana, a gene implicated in melanosome biogenesis. Only one molecular function assignment (GO:0005515 protein binding) is assigned to mlana, which is not included in the QuickGO Mouse Slimmer. After combining closely related terms, there were 10 parent GO terms assigned. Then, we removed all categories with fewer than five unique gene assignments, of which there were three: RNA binding, enzyme regulator activity, and lipid binding.
Thus we ended up with seven molecular function categories for comparison. The DNA binding GO category was the result of combining two GO terms: DNA binding and transcription factor activity. Although there are distinct biological differences between a gene displaying DNA binding activity and one acting as a transcription factor, in the Gephebase data set there was nearly complete overlap between these terms. Only one gene was tagged as DNA binding without also being tagged for transcription factor activity: epidermal growth factor receptor egfr. All other genes were tagged either with both GO terms, or with neither. (4) Genetic hotspots We investigated the overlap of genes between pigmentation systems by examining the number of entries corresponding to shared genes. For each clade (excluding amphibians due to lack of data), we listed all the genes identified in that clade, and then calculated the total number of entries corresponding to those same genes in each of the other three clades.
5) Statistical analysis For each analysis the cis-regulatory proportion was fitted as a binomial logistic regression model and post hoc pairwise comparisons were performed with Tukey correction using the glht function in the multcomp package for R. Direct comparisons between categories or groups of categories were performed using Fisher's exact test, again at P < 0. 05. (6) Correction for overrepresented genes For each analysis we compared the total numbers of cisregulatory and coding mutations in each category. However, some particularly well-characterised genes contribute a Table  4. The 10 molecular function (parent gene ontology, GO) categories assigned to data set entries, the total number of genes to which each GO category was assigned and corresponding total number of entries, the number of cis-regulatory entries to which each was assigned and corresponding cis-regulatory proportion, and the child GO terms that were combined into the category where relevant.
III. RESULTS (1) Information on genetic variants associated with pigmentation is asymmetrically distributed across vertebrate clades To examine the representation of different pigmentation systems across vertebrate evolutionary studies, we looked at the number of entries in Gephebase across five clades: teleost fishes, amphibians, squamates, birds and mammals. All 389 entries belonged to one of these clades. The significant majority (68. 3%, N = 266) of entries were for mammals, followed by birds (21. 1%, N = 82), teleosts (7. 5%, N = 29), squamates (2. 6%, N = 10) and amphibians (0. 5%, N = 2). Clades in which multiple distinct chromatophore classes are found thus represent only 10% of our data set, with the majority of data from clades with only melanocytes and no other pigment cell classes.
The third most represented gene is asip, which codes for agouti signalling protein (ASIP). This protein acts as an antagonist to MC1R; in the absence of ASIP, MC1R initiates a signalling cascade that results in eumelanin production, while when ASIP is present the melanocyte reverts to pheomelanin synthesis (García-Borr on et al. , 2005). There are 43 asip entries in our data set (11. 1%). The overrepresentation of these three genes in Gephebase reflects their relevance in mammalian and other vertebrate pigmentation systems, and particularly their frequent selection as candidate genes for further study. To remove any potential bias resulting from a focus on these genes, we reanalysed the data with mc1r, kit, and asip removed. With these entries removed from the data set, the overall proportion of cis-regulatory mutations increased from 21.
8% to 28. 6%, due to the prevalence of coding mutations in mc1r entries in particular (Fig. 2E ). Similarly, the proportions of cisregulatory mutations in all three methodology categories increased (Fig. 2E ). Coding mutations remain the most reported for candidate gene and linkage mapping methodologies, but for the association mapping category there is now an approximately equal number of cis-regulatory and coding mutations (N = 21 and 20, respectively). However, the difference between the proportion of cis-regulatory mutations before and after removing the overrepresented genes was not significant (Fisher's exact test, P = 0. 089). We performed an additional analysis in which we normalised the data set to weight each individual gene equally regardless of its number of entries in the database (see Section II.
For example, in deer mice (Peromyscus spp. ) alternative isoforms of asip are differentially expressed in different body regions, so that cis-regulatory mutations can drive spatially localised pattern changes by altering relative isoform expression  (Mallarino et al. , 2017). Additionally, it has been shown that functional residues within regions of protein interaction are more highly conserved than residues outside of interaction regions, thus it may be the case that only the ligand-binding domains of receptor proteins are highly constrained by specificity, whereas other domains are more tolerant of coding mutations  (Worth, Gong & Blundell, 2009). Alternatively, ligand genes, which typically encode a smaller protein, may be expected to have a greater proportion of their coding sequence functionally constrained when compared with the corresponding receptor genes.
IV. DISCUSSION Gephebase is a powerful tool that provides an accessible compilation of data on phenotype-genotype associations to allow investigations of general patterns regarding the mechanisms underlying evolutionary change. Importantly, this data set can be used to identify gaps in the literature, and to identify novel directions and study systems for future research. Here, we analysed a large data set of 389 Gephebase entries, each representing a pair of alleles linked to phenotypic variation in vertebrate pigmentation. Of the three forms of empirical evidence accepted by Gephebase, it was clear that the study methodology with the highest ascertainment biasthe candidate gene approachidentified more coding sequence mutations than the other methods. Irrespective of this bias, protein coding changes were still the majority of mutations identified in all analyses, representing 75% of entries for linkage mapping studies and 50% for association mapping studies.
In birds,  Toews et al. (2016)  used an association mapping approach to examine pigmentation evolution in two naturally hybridising species of warbler with highly divergent pigment patterns. Diversification of model systems in the future may facilitate a greater understanding of long-term pigment evolution in birds and mammals. (3) Potential biases introduced by study systems Mammalian and avian systems dominate our data set. These taxa possess only melanocytes as pigment cells, and pigmentation differences primarily result from spatial and temporal differences in pigment production and deposition. In other vertebrates, pigmentation patterns result from the presence and differential arrangement of different classes of pigment cells that share a developmental origin. The few studies in the Gephebase database that focus on clades with distinct chromatophore classes often found novel pigmentation phenotypes associated with a wider range of genes.
Sexually selected traits are also underrepresented in the database, making up only 5. 4% of the case studies. Understanding the interplay between natural and sexual selection on colour traits is important since they may often act in opposite directions. For example, in several species of Lake Malawi cichlids a well-camouflaged colour morph is associated with a cis-regulatory mutation in pax7. However, this mutation also has a deleterious effect in that it disrupts male nuptial colouration. This conflict has been resolved by the invasion of a novel sex determination locus in tight linkage with the pax7 allele  (Roberts, Ser & Kocher, 2009). The importance of pigmentation patterns in mate choice can lead to such conflicts, and the ways in which they are resolved could provide fascinating case studies.
As shown in The XANES (X-ray Absorption Near Edge Spectroscopy) region of Cu(I)-Aβ1-16 (left panel of Fig. 2 ),shows a peak, located in the pre-edge region at about 8982. 6 eV, that is known  [14, 29, 31, 32]  to be the ngerprint of Cu(I) presence. Quantitative structural information can be obtained from a t of the EXAFS (Extended X-ray Absorption Fine Structure) region of the spectrum. We report in Table  2  the values of the best-t parameters, including the quality factor R 1 , that provides a quantitative measure of the goodness of the t. The best t of the Cu(I)-Aβ1-16 complex EXAFS spectrum is obtained by assuming that only two ligands, namely two nitrogen atoms from two imidazole rings of two His residues, are coordinated to Cu(I). The t to the Cu(II)-Aβ1-16 EXAFS data shows instead that the Cu(II) coordination mode involves four ligands, 1 The quality factor R is dened as R-factor= i |χ exp (k i )-χ(k i ) f it | i |χ exp (k i )| namely two nitrogen atoms from two His residues plus one nitrogen and one oxygen from the Asp residue.
This radius was determined as the average of 20 measurements performed with a calliper for both vegetables. For Brussels sprouts, α = 12. 5 mm, while for peas, α = 2. 5 mm. As degradation and diffusion happen at the same time, we solved a diffusion equation with first-order degradation: ∂C ∂t = -D(T) ∂C 2 ∂r 2 -k(T)C (Eq. 5) Similar to  Crank (1975) , the solution is given by the following equation: C t = exp(-k(T)t) * [C eq + ( C 0 -C eq ) 6 π 2 ∑ ∞ n=1 1 n 2 exp ( - D(T)π 2 n 2 t a 2 )] (Eq. 6) The sum in Equation (  6 ) was developed up to n = 100, and the equation contains both the rate constant k(T) and the diffusion constant D(T). Fitting both constants simultaneously is not feasible due to their high correlation (linked to the acceleration of degradation and diffusion with increased temperature) and the limited number of data points.
Accumulative cAMP Assay The cAMP assay was performed as previously described  [31]. Briefly, HEK293 cells transiently transfected with empty vector (mock) or stably expressing human MT 1 or MT 2 were dispensed into a 384-well plate (4000 cells per well) and stimulated with 1µM forskolin alone or in the presence of increasing concentrations of MLT or extracts, incubated for 30 min at room temperature in stimulation buffer supplemented with 1 mM IBMX (Sigma-Aldrich, St Quentin, France). Cells were then lysed and, cAMP content was determined following the supplier's instructions (PerkinElmer CisBio cAMP-G i kit, Codolet, France). The plate was read using the Infinite F500 Tecan microplate reader. Data were fitted via non-linear regression to determine EC 50 and E max values using GraphPad Prism software (La Jolla, CA, USA).
ERK1/2 Activation Assay A quantitative analysis of ERK1/2 activation in non-transfected HEK293 cells (mock) or stably expressing human MT 1 or MT 2 was performed using the p-ERK1/2 AlphaLISA Sure-fire ultra-Assay kit (PerkinElmer, Waltham, MA, USA) as described  [32]. Briefly, cells were starved overnight before being stimulated with a saturating or increasing concentrations of melatonin or plant extracts for 5 min at 37 • C 5% CO 2 , collected in lysis buffer, and 4 µL of each sample was used for the assay according to the supplier's instructions. The plate was read using the Infinite M1000 Tecan microplate reader. Data were analysed using GraphPad Prism software (La Jolla, CA, USA) and fitted via the non-linear regression to determine the EC 50 values.
Net BRET signals were determined by subtracting the BRET signal obtained with cells that only expressed Rluc-MT 1 receptor from BRET signals obtained with cells that co-expressed each receptor and YFP-tagged β-arrestin-2 (this plasmid construct have never been described before). Curves were fitted using a nonlinear regression and log (agonist) vs. response fit equation using GraphPad Prism 9 (GraphPad Software, La Jolla, CA, USA).
MTT Assay Cell viability was measured using the colorimetric 3-(4,5)dimethylthiazol-2-yl)-2,5 diphenyltetrazolium bromide (MTT) assay kit (Millipore Corporation, Billerica, MA, USA). Living cells reduce MTT to formazan which was quantified by measuring the absorbance at 570 nm. HEK293 cells expressing MT 1 receptors were grown to confluence in 96-well plates and incubated with 100 µL of medium, DMSO or fixed concentration of extracts for 3 or 24 h at 37 • C 5% CO 2. At the end of the incubation time, 20 µL MTT was added to each well and incubate for another 4 h at 37 • C 5% CO 2. Formazan production was expressed as a percentage of the values obtained from control cells. The plate was read using an Infinite M1000 Tecan microplate reader. Data were analysed using GraphPad Prism software (La Jolla, CA, USA).
Some extracts increased cell viability at 3 h but this effect was not seen after 24 h of incubation. Afterwards, the effect of the selected extracts was examined on mock-transfected HEK293 cells. As expected, melatonin did not show any effect in these cells. Most of the extracts had a tendency in mock cells to reduce forskolin (FSK)-stimulated cAMP levels, with Ex9, 10 and 25 reaching statistical significance (Figure  2A ). This effect was not due to the presence of maltodextrin as ruled out in control experiments (Supplementary Figure  S2C ). Then, the same experiment was repeated in HEK293 cells expressing either MT 1 or MT 2 receptors (Figure  2B, C ). The data presented are normalized to mock cell responses to focus on melatonin receptor-dependent effects. Melatonin (1 µM) decreased FSK-stimulated cAMP production as expected.
Kernel) without additives, such as maltodextrin, the amount per extract matches the amount per dried kernel. Importantly, the melatonin present in Ex18 is of natural origin excluding presence of any fossil fuel/synthetic melatonin based on a radiocarbon analysis performed by an independent analytic testing laboratory on the request of the supplier (personal communication). Collectively, these data confirm the high levels and the natural origin of melatonin in Ex18. Given the high variability of melatonin levels in Pistacia species reported in the literature, our data cannot be necessarily extrapolated to other Pistacia species and extracts. A larger follow-up study with a number of different Pistacia extracts will be necessary to determine the range of melatonin content in Pistacia extracts with standardized up-to-date analytical methods.
Acknowledgments: We thank  Julie Zumsteg  ( IBMP, Strasbourg, France ) for melatonin measurements by HPLC/MS. This work was supported by  Sanofi-Aventis Groupe, Gentilly, France , the  Institut National de la Santé et de la Recherche Médicale (INSERM)  and the  Centre National de la Recherche Scientifique (CNRS). R. J. was supported by the  Fondation pour la Recherche Médicale  (Equipe FRM  DEQ20130326503 ) and  La Ligue Contre le Cancer N/Ref :  RS19/75-127. N. L. holds a doctoral fellowship from  China Scholarship Council, Huazhong University of Science and Technology, Wuhan, China.
Thermogravimetric Analysis Mass losses were measured with a TGA Netzsch TG 209 F3 piloted by Proteus 6 version. Sample weights varied from 9 to 15 mg. The heating rate was fixed at 5 • C/min from ambient to 550 • C under nitrogen atmosphere. Thermograms (TG) and first derivative (dTG) data were exported in Excel format for analysis. The methodology of mass loss analysis used a fitting procedure  [51] , where TG traces were approximated by a sum of sigmoid curves, according to Equation (1): ∆m = m u -m l *   1 1 + exp T-θ 1 θ 2   (1) where m u and m l are, respectively, the upper and lower limits of mass percentage of the applied sigmoid. T is the temperature and θ 1 and θ 2 are parameters analogous to temperature. In an ideal situation, where mass losses are well separated, θ 1 is related to the temperature corresponding to the peak of the dTG curve.
Fourier Transform Infrared Spectroscopy (FTIR) A Shimadzu IR Affinity-1S equipped with a Miracle™ single attenuated total reflection (ATR) cell from Pike Technologies was used to sample the absorbance of TPS, EMAA, ionomer and blends. A diamond/ZnSe cell was mounted on the ATR. For each spectrum, 32 or 64 scans were performed at a resolution of 4 cm -1 from 650 cm -1 to 4000 cm -1. Multiple point baseline correction was applied to all samples to minimize baseline drift. Several spectral band resolutions were obtained thanks to Fityk 1. 3. 1, a peak fitting software. The shapes of individual bands were modelled by Gaussian distributions, the sum of bands being used for calculation of the nonlinear fit. The convergence of the calculation was based on minimizing the weighted sum of squared residuals and was ensured by the iterative Levenberg-Marquardt algorithm.
Morphometric Data Acquisition and Treatment Xylem G-layer thickness was measured for the tension side and the opposite side, excluding the xylem SCW. A total of 387 measures (211 and 176 respectively for tension wood and opposite wood) were acquired from the same 3 cross sections/stem. Acquisitions were carried out with FIJI software (https://imagej. net/software/fiji/ accessed on 20 December 2022). A bilateral t-test was used to test for differences in G-layer thickness after having checked for homoscedasticity using Bartlett's test.
Unsupervised Exploratory Analysis: Principal Component Analysis and Hierarchical Clustering of Spectra Principal component analysis (PCA) was used to identify differences among spectra and to locate peaks contributing most to among-sample variation. PCA is a non-supervised method that helps visualize the structure of the variability in highly dimensioned data. Spectra were pre-processed prior to PCA as follows: (i) peaks < 300 cm -1 and >1750 cm -1 were suppressed for all spectra, (ii) the baseline was corrected using polynomial fitting (degree 6), (iii) spectra were normalized by equalizing the area under each spectrum. To determine whether spectra differed between the three categories, they were classified using hierarchical cluster analysis (HCA), a non-supervised method that recursively partitions a dataset according to dissimilarities.