Signatures of de-domestication in autochthonous pig breeds and of domestication in wild boar populations from MC1R and NR6A1 allele distribution

Autochthonous pig breeds are usually reared in extensive or semi-extensive production systems that might facilitate contact with wild boars and, thus, reciprocal genetic exchanges. In this study, we analysed variants in the melanocortin 1 receptor (MC1R) gene (which cause different coat colour phenotypes) and in the nuclear receptor subfamily 6 group A member 1 (NR6A1) gene (associated with increased vertebral number) in 712 pigs of 12 local pig breeds raised in Italy (Apulo-Calabrese, Casertana, Cinta Senese, Mora Romagnola, Nero Siciliano and Sarda) and south-eastern European countries (Krskopolje from Slovenia, Black Slavonian and Turopolje from Croatia, Mangalitsa and Moravka from Serbia and East Balkan Swine from Bulgaria) and compared the data with the genetic variability at these loci investigated in 229 wild boars from populations spread in the same macro-geographic areas. None of the autochthonous pig breeds or wild boar populations were fixed for one allele at both loci. Domestic and wild-type alleles at these two genes were present in both domestic and wild populations. Findings of the distribution of MC1R alleles might be useful for tracing back the complex genetic history of autochthonous breeds. Altogether, these results indirectly demonstrate that bidirectional introgression of wild and domestic alleles is derived and affected by the human and naturally driven evolutionary forces that are shaping the Sus scrofa genome: autochthonous breeds are experiencing a sort of 'de-domestication' process, and wild resources are challenged by a 'domestication' drift. Both need to be further investigated and managed

Towards a robust face detector

In this work we present the preliminary results of a face detection system based on an hybrid approach: it combines typical feature-based techniques with image-based analysis, in order to better exploit the main characteristics available in the input image. Different modules contribute to the face detection task: 1) a template-based approach initially proposed in [12], 2) an edge-extraction technique well suited to deal with illumination-changes, 3) a multiple-classifier specifically designed to discard false positives and 4) a novel method based on a featureless representation of the eye-patterns that further improves the face/non-face discrimination. The experimental results show that the system can localize faces in images with complex background, even in presence of strong illumination changes