Sparsity in an artificial neural network predicts beauty: Towards a model of processing-based aesthetics

Generations of scientists have pursued the goal of defining beauty. While early scientists initially focused on objective criteria of beauty ('feature-based aesthetics'), philosophers and artists alike have since proposed that beauty arises from the interaction between the object and the individual who perceives it. The aesthetic theory of fluency formalizes this idea of interaction by proposing that beauty is determined by the efficiency of information processing in the perceiver's brain ('processing-based aesthetics'), and that efficient processing induces a positive aesthetic experience. The theory is supported by numerous psychological results, however, to date there is no quantitative predictive model to test it on a large scale. In this work, we propose to leverage the capacity of deep convolutional neural networks (DCNN) to model the processing of information in the brain by studying the link between beauty and neuronal sparsity, a measure of information processing efficiency. Whether analyzing pictures of faces, figurative or abstract art paintings, neuronal sparsity explains up to 28% of variance in beauty scores, and up to 47% when combined with a feature-based metric. However, we also found that sparsity is either positively or negatively correlated with beauty across the multiple layers of the DCNN. Our quantitative model stresses the importance of considering how information is processed, in addition to the content of that information, when predicting beauty, but also suggests an unexpectedly complex relationship between fluency and beauty.

Social and sexual consequences of facial femininity in a non-human primate

In humans, femininity shapes women's interactions with both genders, but its influence on animals remains unknown. Using 10 years of data on a wild primate, we developed an artificial intelligence-based method to estimate facial femininity from naturalistic portraits. Our method explains up to 30% of the variance in perceived femininity in humans, competing with classical methods using standardized pictures taken under laboratory conditions. We then showed that femininity estimated on 95 female mandrills significantly correlated with various socio-sexual behaviors. Unexpectedly, less feminine female mandrills were approached and aggressed more frequently by both sexes and received more male copulations, suggesting a positive valuation of masculinity attributes rather than a perception bias. This study contributes to understand the role of femininity on animal's sociality and offers a framework for non-invasive research on visual communication in behavioral ecology.

The Transcriptome Landscape of the In Vitro Human Airway Epithelium Response to SARS-CoV-2

Airway-liquid interface cultures of primary epithelial cells and of induced pluripotent stem-cell-derived airway epithelial cells (ALI and iALI, respectively) are physiologically relevant models for respiratory virus infection studies because they can mimic the in vivo human bronchial epithelium. Here, we investigated gene expression profiles in human airway cultures (ALI and iALI models), infected or not with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), using our own and publicly available bulk and single-cell transcriptome datasets. SARS-CoV-2 infection significantly increased the expression of interferon-stimulated genes (IFI44, IFIT1, IFIT3, IFI35, IRF9, MX1, OAS1, OAS3 and ISG15) and inflammatory genes (NFKBIA, CSF1, FOSL1, IL32 and CXCL10) by day 4 post-infection, indicating activation of the interferon and immune responses to the virus. Extracellular matrix genes (ITGB6, ITGB1 and GJA1) were also altered in infected cells. Single-cell RNA sequencing data revealed that SARS-CoV-2 infection damaged the respiratory epithelium, particularly mature ciliated cells. The expression of genes encoding intercellular communication and adhesion proteins was also deregulated, suggesting a mechanism to promote shedding of infected epithelial cells. These data demonstrate that ALI/iALI models help to explain the airway epithelium response to SARS-CoV-2 infection and are a key tool for developing COVID-19 treatments.

The Mandrillus Face Database: A portrait image database for individual and sex recognition, and age prediction in a non-human primate

The Mandrillus Project is a long-term field research project in ecology and evolutionary biology, monitoring, since 2012, a natural population of mandrills (Mandrillus sphinx; primate) located in Southern Gabon. The Mandrillus Face Database was launched at the beginning of the project and now contains 29,495 photographic portraits collected on 397 individuals from this population, from birth to death for some of them. Portrait images have been obtained by manually processing images taken in the field with DSLR cameras: faces have been cropped to remove the ears and rotated to align the eyes horizontally. The database provides portrait images resized to 224 × 224 pixels associated with several manually annotated labels: individual identity, sex, age, face view, and image quality. Labels are stored within the image metadata and in a table accompanying the image database. This database will allow training and comparing methods on individual and sex recognition, and age prediction in a non-human animal.