Same father, same face: Deep learning reveals selection for signaling kinship in a wild primate

Many animals rely on facial traits to recognize their kin; however, whether these traits have been selected specifically for this function remains unknown. Using deep learning for face recognition, we present the first evidence that interindividual facial resemblance has been selected to signal paternal kinship. Mandrills (Mandrillus sphinx) live in matrilineal societies, in which females spend their entire lives not only with maternal half-sisters (MHS) but also with paternal half-sisters (PHS). We show that PHS have more differentiated social relationships compared to nonkin, suggesting the existence of kin recognition mechanisms. We further demonstrate that facial resemblance increases with genetic relatedness. However, PHS resemble each other visually more than MHS do, despite both kin categories sharing similar degrees of genetic relatedness. This paternally derived facial resemblance among PHS indicates selection to facilitate kin recognition. This study also highlights the potential of artificial intelligence to study phenotypic evolution.

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Same father, same face: deep-learning reveals paternally-derived signalling of kinship in a wild primate

10.1101/810820 ◽

2019 ◽

Author(s):

Marie JE Charpentier ◽

Mélanie Harté ◽

Clémence Poirotte ◽

Jade Meric de Bellefon ◽

Benjamin Laubi ◽

...

Keyword(s):

Artificial Intelligence ◽

Nonhuman Primates ◽

Genetic Relatedness ◽

Kin Recognition ◽

Evolutionary Mechanisms ◽

Individual Health Status ◽

Facial Similarity ◽

Animal Faces ◽

Term Data

ABSTRACTAnimal faces convey important information such as individual health status1 or identity2,3. Human and nonhuman primates rely on highly heritable facial traits4,5 to recognize their kin6–8. However, whether these facial traits have evolved for this specific function of kin recognition remains unknown. We present the first unambiguous evidence that inter-individual facial similarity has been selected to signal kinship using a state-of-the-art artificial intelligence approach based on deep neural networks and long-term data on a natural population of nonhuman primates. The typical matrilineal society of mandrills, is characterized by an extreme male’s reproductive skew with one male generally siring the large majority of offspring born into the different matrilines each year9. Philopatric females are raised and live throughout their lives with familiar maternal half-sisters (MHS) but because of male’s reproductive monopolization, they also live with unfamiliar paternal half-sisters (PHS). Because kin selection predicts differentiated interactions with kin rather than nonkin10 and that PHS largely outnumber MHS in a mandrills’ social group, natural selection should favour mechanisms to recognize PHS. Here, we first show that PHS socially interact with each other as much as MHS do, both more than nonkin. Second, using artificial intelligence trained to recognize individual mandrills from a database of 16k portrait pictures, we demonstrate that facial similarity increases with genetic relatedness. However, PHS resemble more to each other than MHS do, despite both kin categories sharing similar degrees of genetic relatedness. We propose genomic imprinting as a plausible genetic mechanism to explain paternally-derived facial similarity among PHS selected to improve kin recognition. This study further highlights the potential of artificial intelligence to study evolutionary mechanisms driving variation between phenotypes.

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Bell miner provisioning calls are more similar among relatives and are used by helpers at the nest to bias their effort towards kin

Proceedings of The Royal Society B Biological Sciences ◽

10.1098/rspb.2011.0307 ◽

2011 ◽

Vol 278 (1723) ◽

pp. 3403-3411 ◽

Cited By ~ 43

Author(s):

Paul G. McDonald ◽

Jonathan Wright

Keyword(s):

Genetic Relatedness ◽

Kin Recognition ◽

Linear Increase ◽

Complex Societies ◽

Kin Discrimination ◽

Call Structure ◽

Large Numbers ◽

Group Members ◽

Family Based ◽

Kin Recognition Mechanisms

Kin selection predicts that helpers in cooperative systems should preferentially aid relatives to maximize fitness. In family-based groups, this can be accomplished simply by assisting all group members. In more complex societies, where large numbers of kin and non-kin regularly interact, more sophisticated kin-recognition mechanisms are needed. Bell miners ( Manorina melanophrys ) are just such a system where individuals regularly interact with both kin and non-kin within large colonies. Despite this complexity, individual helpers of both sexes facultatively work harder when provisioning the young of closer genetic relatedness. We investigated the mechanism by which such adaptive discrimination occurs by assessing genetic kinship influences on the structure of more than 1900 provisioning vocalizations of 185 miners. These ‘mew’ calls showed a significant, positive linear increase in call similarity with increasing genetic relatedness, most especially in comparisons between male helpers and the breeding male. Furthermore, individual helping effort was more heavily influenced by call similarity to breeding males than to genetic relatedness, as predicted if call similarity is indeed the rule-of-thumb used to discriminate kin in this system. Individual mew call structure appeared to be inflexible and innate, providing an effective mechanism by which helpers can assess their relatedness to any individual. This provides, to our knowledge, the first example of a mechanism for fine-scale kin discrimination in a complex avian society.

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EFFECTIVENESS OF ARTIFICIAL INTELLIGENCE USING DEEP LEARNING FOR DETECTING GASTRIC CANCER IN ENDOSCOPIC IMAGES

10.1055/s-0038-1637183 ◽

2018 ◽

Author(s):

T Hirasawa ◽

K Aoyama ◽

J Fujisaki ◽

T Tada

Keyword(s):

Artificial Intelligence ◽

Gastric Cancer ◽

Deep Learning ◽

Endoscopic Images

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Developing an Artificial Intelligence Project in your Radiology Department

Indian Journal of Musculoskeletal Radiology ◽

10.25259/ijmsr_50_2019 ◽

2020 ◽

Vol 2 ◽

pp. 58-61 ◽

Cited By ~ 1

Author(s):

Syed Junaid ◽

Asad Saeed ◽

Zeili Yang ◽

Thomas Micic ◽

Rajesh Botchu

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Health Care ◽

Deep Learning ◽

Learning Algorithms ◽

Radiology Department ◽

Short Article ◽

Computing Power ◽

Road Map ◽

Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

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Faculty Opinions recommendation of Kin recognition within a seed and the effect of genetic relatedness of an endosperm to its compatriot embryo on maize seed development.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.717973137.793471234 ◽

2013 ◽

Author(s):

Andreas Wagner ◽

Peter Szövényi

Keyword(s):

Seed Development ◽

Genetic Relatedness ◽

Kin Recognition ◽

Maize Seed

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Virtual Screening Meets Deep Learning

Current Computer - Aided Drug Design ◽

10.2174/1573409914666181018141602 ◽

2018 ◽

Vol 15 (1) ◽

pp. 6-28 ◽

Cited By ~ 6

Author(s):

Javier Pérez-Sianes ◽

Horacio Pérez-Sánchez ◽

Fernando Díaz

Keyword(s):

Artificial Intelligence ◽

Machine Learning ◽

Deep Learning ◽

Virtual Screening ◽

Great Increase ◽

New Drugs ◽

Learning Approach ◽

Screening Strategies ◽

Computer Aided ◽

Recent Developments

Background: Automated compound testing is currently the de facto standard method for drug screening, but it has not brought the great increase in the number of new drugs that was expected. Computer- aided compounds search, known as Virtual Screening, has shown the benefits to this field as a complement or even alternative to the robotic drug discovery. There are different methods and approaches to address this problem and most of them are often included in one of the main screening strategies. Machine learning, however, has established itself as a virtual screening methodology in its own right and it may grow in popularity with the new trends on artificial intelligence. Objective: This paper will attempt to provide a comprehensive and structured review that collects the most important proposals made so far in this area of research. Particular attention is given to some recent developments carried out in the machine learning field: the deep learning approach, which is pointed out as a future key player in the virtual screening landscape.

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