dataesr/scientific-paragraphs-categorization huggingface dataset:
Evolutionary timescales and taxonomic distance We found that shorter taxonomic distances (domesticated and intraspecific entries) are associated with a higher proportion of protein coding changes than interspecific changes. These results should be taken with caution as the sparsity of interspecific studies indicates comparatively little investigation of pigmentation traits across larger evolutionary timescales. Interestingly, the majority (8 out of 14) of interspecific entries were identified in teleost species, despite their overall underrepresentation in the dataset. Further, 6 of these interspecific entries were found in East African cichlids. The current overrepresentation of shorter evolutionary timescales may introduce bias in the types of mutation that can be identified. Shorter evolutionary timescales may allow for pleiotropic mutations, as well as mutations that confer an advantage only in a specific environmental context.
Potential biases introduced by study systems Mammalian and avian systems dominate the majority of the entries. These only possess 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. Those few studies in the Gephebase dataset that focus on clades with distinct chromatophore classes often found novel pigmentation phenotypes associated with a wider range of genes. This is also corroborated by the greater numbers of shared hotspot genes between clades with similar pigmentation systems -the genes identified in teleost and squamate studies showed little overlap with those found in mammals and birds.
For example, the study of variation in yellow pigmentation in budgerigar parrots led to the identification of a coding mutation in a previously uncharacterised polyketide synthase that is involved in the synthesis of yellow polyene pigments called psittacofulvins  (Cooke et al. , 2017). We found only one entry in the dataset relating to cellular structural colouration, an interspecific study investigating pigment pattern differences between two Danio fish species. D. nigrofasciatus has a disrupted stripe pattern in comparison to D. rerio, which is associated with a cis-regulatory mutation in the Endothelin-3 (Edn3) signaling peptide  (Spiewak et al. , 2018). Edn3 promotes iridophore proliferation, this cis-mutation leads to decreased Edn3 expression and correspondingly lower iridophore complement in D.
Sexually selected traits are also underrepresented in the database, making up only 5. 5 % 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 cisregulatory 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 et al. , 2009). The importance of pigmentation patterns in mate choice can lead to such conflicts, and the ways in which they are resolved can present fascinating case studies.
Sexually selected traits also present a conundrum in that it is unclear why and how trait variation is maintained in natural populations despite apparent directional selection due female choice or male-male competition  (Chenoweth and McGuigan, 2010). Identifying genes and genomic regions contributing to trait variation and studying its adaptive significance in wild populations creates the opportunity to understand this paradox  (Johnston et al. , 2013). Finally, the Gephebase dataset is currently biased towards large-effect loci, as linkage mapping is limited in its statistical power to detect small effect mutations and polygenic architectures  (Rockman, 2012; Slate, 2013). Association studies conducted on thousands of samples are now reaching a point where small-effect loci are detectable, under the conditions that these variants are common : for instance, several GWAS studies of skin pigmentation levels have uncovered an amino-acid polymorphism of MFSD12 as a determinant of colour variation across several continents  (Adhikari et al.
Assignment of new parameters Clade: Each vertebrate represented in the dataset unambiguously belonged to one of five clades with distinct pigmentation biology -amphibians, teleosts, squamates, birds and mammals. As such, we divided entries into these clades to compare trends in their different pigmentation systems. Pigment Type: In total, five pigment types were represented in the dataset -biliverdin, carotenoids, pteridines, melanins, and psittacofulvins. Additionally, one entry referred to structural coloration. Each entry could be unambiguously assigned to one or more of these categories. In total there were 10 entries in which a single genetic variant was associated with changes to multiple pigments. These entries were assigned to each relevant pigment type category. Phenotype Category: We empirically assigned to each entry one of seven phenotype categories in order to group similar organismal phenotypes, and analyse trends in how they arise.
We additionally assigned each category to either colour shift or pattern alteration, on the basis of whether the phenotype affected colour or spatial pattern boundaries. Each entry was considered independently, and these assignments were considered separately from the previous phenotype categories. Phenotypes involving a loss of patterning (for example whole body albinism resulting in no visible pattern boundaries) were considered colour shifts. Ambiguous entries were not assigned a category -a total of 76 entries. This left a total of 287 assignments, of which 263 were cis-regulatory or coding. This total (263) was used for the 'total' bars in Figures  4A, S3 , and S4. Cellular Function Category: In order to analyse genes associated with different developmental and cellular roles, we assigned each gene in the dataset a functional category, reflecting its role in the development of the phenotype (Supplementary File 1).
Molecular Function Category: We assigned protein categories non-empirically by using the gene ontology (GO) terms associated with each gene in the dataset. 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 for being best 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. We additionally manually combined a number of closely related GO terms in order to reduce the number of potential categories (Supplementary Table  S1 ). In total there were 83 unique assignments of parent GO categories across the dataset. All of the 61 unique genes in the dataset generated at least one assignment, with the exception of mlana, a gene implicated in melanosome biogenesis.
Mlana has only one molecular function assignment (GO:0005515 protein binding), 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 GO categories for comparison (Supplementary Table  S1 ). The most commonly assigned 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 acting as a transcription factor, in the case of the Gephebase dataset there was nearly complete overlap between these terms.
Only one gene was tagged as DNA binding without being tagged for transcription factor activity, namely egfr -epidermal growth factor receptor. All other genes were tagged with both GO terms, or neither. Evolutionary Hotspots: We investigated the overlap of genes between pigmentation systems by examining the number of entries corresponding to shared genes. For each clade, we looked at all the genes identified in that clade, and then calculated the total number of entries corresponding to those genes in each of the other three clades. We then normalised each of these three figures by dividing by the total number of entries in that clade, in order to account for the disparity in entries between clades. S1  shows the 10 GO categories assigned to dataset 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.
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). In short, 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" dataset), or subject to experimental evolution under lab-controlled selective pressures. Data is manually curated by a team of less than a dozen researchers, using keywords as well as Pubmed/Google Scholar citations to identify newly published studies. Our current triage system gives priority to gephes identified by forward genetics (QTL mapping, GWAS) with a 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. Past 2017, data curation efforts have mainly focused on colour variation in vertebrates (Gephebase "Trait" category = "Coloration"). The download of Gephebase data on 28 October 2021 (Supplementary File 1), which was used for the present study, can be considered as a compilation of the genes and mutations contributing to Vertebrate coloration up to and including 2019.
Meta-analyses To examine trends in colour pattern evolution, we utilised Gephebase as a resource for exploring genotype-phenotype relationships. We formed a working dataset by selecting every Gephebase entry pertaining to the trait category 'colouration', and further filtered those entries by removing all entries pertaining to non-vertebrate species (accessed 28/10/2021). In total we retrieved 363 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 61 unique genes identified across 89 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.
Statistical analysis were performed with a concordance Cohen's kappa test with linear weighting (κPL) and quadratic weighting (κPQ). The kappa values interpretation results was based on the de nitions provided by Altman and Viera. (0-0. 2 poor, 0. 2-0. 4 passable, 0. 4-0. 6 moderate, 0. 6-0. 8 good, 0. 8-1 very good)  [15, 16]. Their comparison was accomplished by a bilateral Student test. The analysis was made at the Unit of Biostatistics and Clinical Research of the Caen University College Hospital with the IBM SPSS and R software programs. Statistical signi cance was de ned as p < 0. 05.
In fact, FRENCH scale leave less freedom to the nurses compared to the ESI. Our study compares two cultures and two ways of thinking. The ESI leaves more freedom for the nurses judgment coupled with an assessment of care needed. FRENCH headed scale approaches an e-sorting scale. A recent paper evaluates an electronic triage system (e-triage) based on machine learning that predicts likelihood of acute outcomes enabling improved patient differentiation  [20]. As the FRENCH scale, e-triage is composed of a random model applied to triage data (vital signs, chief complaint) to determine a triage score and seems to improve ESI sub-sorting. In both cases the nal adjustment of the triage score, depending on the clinical context and the patient's medical history, is based on the evaluator's experience: triage nurse for FRENCH and big data in esorting.
ACKNOWLEDGEMENTS Surveys -triage tool implementation handbook : The clinical cases tested were taken from the o cial training guides for each of the sorting tools: -the  AHRQ (American Agency for Health Research and Quality) ESI  guide. https://www. ena. org/docs/default-source/education-document-library/triage/esi-implementation-handbook-2020. pdf?sfvrsn=fdc327df_4 -the French triage training guide developed by the  French Society of Emergency Medicine  based on the work of  Taboulet et al. Taboulet P, Moreira V, Haas L, Porcher R, Braganca A, Fontaine J-P , et al. Triage with the  French Emergency Nurses Classi cation in Hospital  scale: reliability and validity. Eur J Emerg Med Off J Eur Soc Emerg Med. 2009; 16:61-7  Triage des patients à l'accueil d'une structure d'urgences. Présentation de l'échelle de tri élaborée par la Société française de médecine d'urgence : la FRench Emergency Nurses Classi cation in Hospital (FRENCH ),  P.
Therefore, our approximation is quite correct since any of the used transports requires at least a few seconds to carry out a significant change in cytosolic concentrations. In order to numerically integrate the differential system that we derived, we designed a home-made C++ software, based on and adaptive Runge-Kutta solver which we modified with 1) the effect of the chemical, algebraic constraints and 2) our Newton + algorithm, to ensure that every trial set of concentrations respects the set of equilibria. Such a simplified model is able to fully recapitulate intracellular pH regulation  (Bouret et al. , 2014; Counillon et al. , 2016). This model predicts a large and robust area of intracellular pH in the range of biological values and a powerful ability to compensate for transient or sustained perturbations.
Funding This work was funded by  Université de Bourgogne  ( Dijon, France), Université Tunis El Manar (Tunis, Tunisia ),  Université de Monastir  ( Monastir, Tunisia ), and  Université Hassan 1 er (Settat, Morocco). This work was supported by  PHC   UTIQUE 2021-2022  ( Dr Taoufik Ghrairi ,  Tunisia  and  Dr Gérard Lizard , France; code CMCU:  22G0809/Code Campus France: 47608VJ ). Imen Ghzaiel also received financial support from  ABASIM (Association Bourguignonne pour les Applications des Sciences de l'Information en Médecine ; Dijon, France).
Study Design This analysis was conducted as part of Intra Individual Evaluation of Uremic Toxin Concentration (EVITUPH) an open-label monocentric prospective study that included 75 HD patients between 07/2015 and 09/2015 in Nephrology Service in Marseilles, France, with a one-year follow-up (registered at ClinicalTrials. gov NCT02480699). The initial study was performed to explore the intra-individual variability of uremic toxins over one year.
If the patient had not completed or completed partially the questionnaire, a new one was offered, and renewed explanations were provided to the patient. After two unsuccessful attempts, the patient was considered non-contributory and was not included in the present study. This method is considered appropriate for capturing the usual intake over a one-month period. Dietary data were to be entered using the Nutrilog ® software, Marans, France (version 3. 2). If necessary, the database was updated with patient recipes. Nutritional variables: energy (kcal), protein (g), fat (g), carbohydrates (g), protein of animal or vegetable origin (g), sugars (total, g), saturated fatty acids (%), and polyunsaturated fatty acids (%) were extracted from each questionnaire. The protein/fiber index was calculated by dividing the total protein intake (g) by the total fiber intake (g).
Statistical analysis The metabolomics data matrix was first pre-processed to remove metabolites with more than 20% missing values in all the study groups, and the remaining missing values were imputed using the kNN algorithm (k = 10). Data normality was checked by inspecting probability plots. Afterwards, data were log transformed and Pareto scaled, and then subjected to statistical analysis using the R/Bioconductor POMA package. Linear models were built using the limma package to look for metabolite differences between the study groups, either by considering the whole study population (i. e. , CD vs. CTL) as well as after stratifying according to the ApoE-ε4 genotype (i. e. , CD ε4+ , CD ε4-, CTL ε4+ , CTL ε4-) and sex (i. e. , CD F , CD M , CTL F , CTL M ). In addition to the matching variables (i.
Funding This work was accomplished as a part of the  D-CogPlast project ("Identification of dietary modulators of cognitive ageing and brain plasticity and proof of concept of efficacy for preventing/reversing cognitive decline ") supported within the  European Joint Programming Initiative "A Healthy Diet for a Healthy Life" (JPI HDHL , http:// www. healt hydie tforh ealth ylife. eu/), granted by  MINECO (Spain ,  PCIN-2015-229 ),  ANR  ( France ,  ANR-15-HDHL-0002-05 ).
The staining was revealed using 0. 6% diaminobenzidine (DAB) with a nickel-enhanced glucose oxidase method  (Shu et al. , 1988). The sections were washed, dehydrated in increasing concentrations of ethanol, mounted on slides and coverslipped using Depex mounting medium (BDH, 361254D). Sections (n = 4-6/group) containing the corpus callosum (body; -0. 18 to -2. 55 to Bregma) were imaged at 20X using a Nanozoomer slide scanner (Hamamatsu Photonics), extracted with the NDP. view 2 freeware (Hamamatsu Photonics) and analyzed with ImageJ software. The corpus callosum area traced was calculated and the density of Iba1 + cells was determined per mm 2. The analysis was conducted blind to the experimental conditions. Brain sections were obtained from 6 P0 litters (for a total of 4 n-3 sufficient = mice and 5 n-3 deficient mice), 5 P5 litters (n-3 sufficient = 4 mice, n-3 deficient = 4 mice), 5 P10 litters (n-3 sufficient = 4 mice, n-3 deficient = 5 mice), 4 P15 litters (n-3 sufficient = 6 mice, n-3 deficient = 5 mice).
Statistical Analyses All statistical analyses were performed by using Prism 9 (GraphPad software). The results are expressed as mean ± standard error of the mean (SEM), except Table  3  which presents medians. Statistical significance was set to p < 0. 05. Normality and homoscedasticity were verified using the Shapiro-Wilk test and Brown-Forsythe test, respectively. Non-parametric unpaired two-tailed Student's t-tests were applied for all analyses that did not pass normality and/or homoscedasticity. None of the microglial ultrastructure parameters presented in Table  3  pass normality using Shapiro-Wilk test. They were analyzed by non-parametric two-tailed Student's unpaired t-tests. The analysis of microglial properties was usually conducted using n = cell considering the biological unit as the microglia, and not the animal.
0227). Moreover, 29% of microglia contacted myelinated axons in the n-3 sufficient condition vs 14% in n-3 deficient mice (data not shown). We also assessed the number of myelin fragments per microglial cell body and found significantly less myelin debris in microglia from n-3 deficient mice (Figures  2A, C  and Table  3 ) (n-3 sufficient vs n-3 deficient: Mann-Whitney test * * p = 0. 009). These data indicate that not only are there fewer microglia, but low n-3 PUFA intake affected the interaction of microglia with myelin in the corpus callosum.
, 2021b). We focused on one part of the corpus callosum, the body that unites the motor, somatosensory and parietal regions  (Wahl et al. , 2007; Park et al. , 2008; Putnam et al. , 2010; Ku and Torii, 2020). More experiments are needed to fully unravel to what extent n-3 PUFA deficiency affects microglial morphology, lipid composition and functions in the corpus callosum and how this would relate to behavior abnormalities later in life. Our data reveal that n-3 deficient mice did not display the peak in microglial density classically observed at P10. Indeed, according to the literature, microglia invade the corpus callosum of mice, a phenomenon called the "fountain of microglia"  (Del Rio-Hortega and Penfield, 1932; Imamoto and Leblond, 1978; Ling, 1979) , with a peak around that age, to promote adequate induction of oligodendrocytes  (Hagemeyer et al.
In conclusion, we uncovered a novel effect of maternal n-3 PUFA dietary deficiency on white matter microglia in the developing brain. Further studies are needed to (1) understand the mechanism by which n-3 PUFA affect white matter microglia during neurodevelopment, and (2) test for the causal link between white matter microglia alterations and the myelination deficits that are observed in n-3 deficient mice. M-KS-P was supported by a doctoral training award from the Canadian Institutes of Health Research (CIHR) and a Fond de Recherche du Québec-Santé (FRQS) doctoral award. Bordeaux Imaging Center a service unit of the CNRS-INSERM and Bordeaux University, member of the national infrastructure France BioImaging, are supported by the French National Research Agency (ANR-10-INBS-04).
ACKNOWLEDGMENTS We would like to thank  Patrick Nahirney  and  Brent Gowen  for the access to the transmission electron microscope. We would also like to thank  Christel Poujol ,  Sébastien Marais ,  Fabrice Cordelières ,  Jérémie Teillon ,  Magali Mondin ,  Monica Fernandez-Monreal  from the  Bordeaux Imaging Center (Bordeaux, France)  for their help with microscopy experiments and image analysis. We like to thank  Maude Bordeleau  for her help with image analyses. We finally thank the animal facility of the  NutriNeuro lab  for taking care of and providing the animals that were necessary to perform the experiments. We acknowledge and respect the l k w n people on whose traditional territory the  University of Victoria  stands and the Songhees, Esquimalt and SÁNE Ć peoples whose historical relationships with the land continue to this day.
FUNDING This research was funded by the  Institut National pour la Recherche Agronomique, l'Alimentation et l'Environnement (INRAE) , the  Bordeaux Univ  and the  International Laboratory OptiNutriBrain. AN was supported by the  Region Ile de France (PICRI , the  Cerebral Palsy Foundation  # 13020605 ) and by the FRM. M-ET holds a Canada Research Chair (Tier 2) in Neurobiology of Aging and Cognition. AN and M-ET were supported by the  Fonds de Recherche du Québec-Nature et Technologie (FRQNT). SL was supported by the  Foundation for Medical Research (FRM ,  DEQ20170336724 ), the  French Foundation  (FDF, # 00070700 ),  Fondation pour la Recherche sur le Cerveau (FRC) ,  Fondation Carrefour  and  Région Nouvelle Aquitaine. FD was funded by  Idex/University of Bordeaux  and benefited from the  Bordeaux Neurocampus Graduate Program  ( EUR 17-EURE-0028  project).