Human skin color | EPFL Graph Search

Human skin color ranges from the darkest brown to the lightest hues. Differences in skin color among individuals is caused by variation in pigmentation, which is the result of genetics (inherited from one's biological parents and or individual gene alleles), exposure to the sun, natural and sexual selection, or all of these. Differences across populations evolved through natural selection or sexual selection, because of social norms and differences in environment, as well as regulations of the biochemical effects of ultraviolet radiation penetrating the skin. The actual skin color of different humans is affected by many substances, although the single most important substance is the pigment melanin. Melanin is produced within the skin in cells called melanocytes and it is the main determinant of the skin color of darker-skin humans. The skin color of people with light skin is determined mainly by the bluish-white connective tissue under the dermis and by the hemoglobin circulating i

Maternal allocation of carotenoids increases tolerance to bacterial infection in brown trout

Matay Hobil, Laure Menin

Life-history theory predicts that iteroparous females allocate their resources differently among different breeding seasons depending on their residual reproductive value. In iteroparous salmonids there is typically much variation in egg size, egg number, and in the compounds that females allocate to their clutch. These compounds include various carotenoids whose functions are not sufficiently understood yet. We sampled 37 female and 35 male brown trout from natural streams, collected their gametes for in vitro fertilizations, experimentally produced 185 families in 7 full-factorial breeding blocks, raised the developing embryos singly (n = 2960), and either sham-treated or infected them with Pseudomonas fluorescens. We used female redness (as a measure of carotenoids stored in the skin) and their allocation of carotenoids to clutches to infer maternal strategies. Astaxanthin contents largely determined egg colour. Neither egg weight nor female size was correlated with the content of this carotenoid. However, astaxanthin content was positively correlated with larval growth and with tolerance against P. fluorescens. There was a negative correlation between female skin redness and the carotenoid content of their eggs. Although higher astaxanthin contents in the eggs were associated with an improvement of early fitness-related traits, some females appeared not to maximally support their current offspring as revealed by the negative correlation between female red skin colouration and egg carotenoid content. This correlation was not explained by female size and supports the prediction of a maternal trade-off between current and future reproduction.

Springer2017

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

The focus of the work presented in this thesis is the exploration of the genetic architecture of complex human traits - at the dawn of genomic medicine. The underlying mechanisms explaining the enormously polygenic nature of most human complex traits are still unknown. The first chapter explores a possible explanatory model in which variant effects are due to an indirect mechanism, namely competition among genes for shared intracellular resources such as ribosomes. Our findings show that under most reasonable assumptions, resource competition should not be expected to have much impact on either protein expression levels of individual genes or on complex trait outcomes. The prediction accuracy of polygenic scores (PGS) remains relatively modest compared to what is expected given the estimated heritability of traits. Traditionally, the construction of PGS uses a large number of genetic variations, most of which have weak additive effects. Recent machine learning methods could improve PGS by also aggregating epistatic effects. To evaluate these different methods, we conducted an experiment based on an innovative concept of crowdsourcing, detailed in the second chapter. We collaborated with opensnp.org, an open repository where people share their genotyping data and phenotypic information, and with crowdai.org, a platform that allowed us to create a public competition for the genomic prediction of height. The challenge lasted three months and attracted 138 participants. This was the first crowd-sourcing challenge based on publicly available genome-wide genotyping data. Due to the enormous number of potential combinations of variants, it is difficult to integrate epistatic effects into PGS. In the third chapter, we present a method where we limit the possible combinations to the boundaries of each topologically associated domain (TAD) independently. With the UK Biobank, for the height phenotype, we included 17,560 variants in an artificial neural network (ANN) and compared the variance explained (

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

for n=20). We concluded that it should be possible to build better PGS using ANNs and epistasis in TADs. The effect of genetic ancestry on phenotypes is not taken into account in PGS-based risk estimates. Doing so could accelerate the adoption of genomic medicine for underrepresented populations and mixed-race individuals. The fourth chapter presents a method for its integration through a secondary score derived from genome-wide genotyping data, the PC score (PCS). We compared two models, one using only the PGS and the other using both the PGS and the PCS. Using the UK Biobank, we found an improvement in genetic prediction for all phenotypes tested:

EPFL2021

Multi-Modal Recurrent Attention Networks for Facial Expression Recognition

Seungryong Kim, Jiyoung Lee

Recent deep neural networks based methods have achieved state-of-the-art performance on various facial expression recognition tasks. Despite such progress, previous researches for facial expression recognition have mainly focused on analyzing color recording videos only. However, the complex emotions expressed by people with different skin colors under different lighting conditions through dynamic facial expressions can be fully understandable by integrating information from multi-modal videos. We present a novel method to estimate dimensional emotion states, where color, depth, and thermal recording videos are used as a multi-modal input. Our networks, called multi-modal recurrent attention networks (MRAN), learn spatiotemporal attention volumes to robustly recognize the facial expression based on attention-boosted feature volumes. We leverage the depth and thermal sequences as guidance priors for color sequence to selectively focus on emotional discriminative regions. We also introduce a novel benchmark for multi-modal facial expression recognition, termed as multi-modal arousal-valence facial expression recognition (MAVFER), which consists of color, depth, and thermal recording videos with corresponding continuous arousal-valence scores. The experimental results show that our method can achieve the state-of-the-art results in dimensional facial expression recognition on color recording datasets including RECOLA, SEWA and AFEW, and a multi-modal recording dataset including MAVFER.

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2020

The genetic architecture of complex human traits at the dawn of genomic medicine

Olivier Noël Marie Naret

R^2

) by the PGS with or without the knowledge of the TADs. We found that it brings a significant improvement with an average

R^2

going from 0.287 to 0.293 (with a p-value

=10E-5

EPFL2021