Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon

Birds primarily create and control the forces necessary for flight through changing the shape and orientation of their wings and tail. Their wing geometry is characterised by complex variation in parameters such as camber, twist, sweep and dihedral. To characterise this complexity, a multi-stereo photogrammetry setup was developed for accurately measuring surface geometry in high-resolution during free-flight. The natural patterning of the birds was used as the basis for phase correlation-based image matching, allowing indoor or outdoor use while being non-intrusive for the birds. The accuracy of the method was quantified and shown to be sufficient for characterising the geometric parameters of interest, but with a reduction in accuracy close to the wing edge and in some localized regions. To demonstrate the method's utility, surface reconstructions are presented for a barn owl (Tyto alba) and peregrine falcon (Falco peregrinus) during three instants of gliding flight per bird. The barn owl flew with a consistent geometry, with positive wing camber and longitudinal anhedral. Based on flight dynamics theory this suggests it was longitudinally statically unstable during these flights. The peregrine flew with a consistent glide angle, but at a range of airspeeds with varying geometry. Unlike the barn owl, its glide configuration did not provide a clear indication of longitudinal static stability/instability. Aspects of the geometries adopted by both birds appeared to be related to control corrections and this method would be well suited for future investigations in this area, as well as for other quantitative studies into avian flight dynamics.Flight O1 - original uncompressed tif images for flight O1 of the barn owlO1_images.zipFlight O2 - original uncompressed tif images for flight O2 of the barn owlO2_images.zipFlight O3 - original uncompressed tif images for flight O3 of the barn owlO3_images.zipFlight P1 - original uncompressed tif images for flight P1 of the peregrineP1_images.zipFlight P2 - original uncompressed tif images for flight P2 of the peregrineP2_images.zipFlight P3 - original uncompressed tif images for flight P3 of the peregrineP3_images.zipREADM

Avian surface reconstruction in free-flight with application to flight stability analysis of a barn owl and peregrine falcon

Birds primarily create and control the forces necessary for flight through changing the shape and orientation of their wings and tail. Their wing geometry is characterised by complex variation in parameters such as camber, twist, sweep and dihedral. To characterise this complexity, a multi-stereo photogrammetry setup was developed for accurately measuring surface geometry in high-resolution during free-flight. The natural patterning of the birds was used as the basis for phase correlation-based image matching, allowing indoor or outdoor use while being non-intrusive for the birds. The accuracy of the method was quantified and shown to be sufficient for characterising the geometric parameters of interest, but with a reduction in accuracy close to the wing edge and in some localized regions. To demonstrate the method's utility, surface reconstructions are presented for a barn owl (Tyto alba) and peregrine falcon (Falco peregrinus) during three instants of gliding flight per bird. The barn owl flew with a consistent geometry, with positive wing camber and longitudinal anhedral. Based on flight dynamics theory this suggests it was longitudinally statically unstable during these flights. The peregrine flew with a consistent glide angle, but at a range of airspeeds with varying geometry. Unlike the barn owl, its glide configuration did not provide a clear indication of longitudinal static stability/instability. Aspects of the geometries adopted by both birds appeared to be related to control corrections and this method would be well suited for future investigations in this area, as well as for other quantitative studies into avian flight dynamics.Flight O1 - original uncompressed tif images for flight O1 of the barn owlO1_images.zipFlight O2 - original uncompressed tif images for flight O2 of the barn owlO2_images.zipFlight O3 - original uncompressed tif images for flight O3 of the barn owlO3_images.zipFlight P1 - original uncompressed tif images for flight P1 of the peregrineP1_images.zipFlight P2 - original uncompressed tif images for flight P2 of the peregrineP2_images.zipFlight P3 - original uncompressed tif images for flight P3 of the peregrineP3_images.zipREADM

Interindividual Differences in Cortical Thickness and Their Genomic Underpinnings in Autism Spectrum Disorder

International audienceObjective: Autism spectrum disorder (ASD) is accompanied by highly individualized neuroanatomical deviations that potentially map onto distinct genotypes and clinical phenotypes. This study aimed to link differences in brain anatomy to specific biological pathways to pave the way toward targeted therapeutic interventions.Methods: The authors examined neurodevelopmental differences in cortical thickness and their genomic underpinnings in a large and clinically diverse sample of 360 individuals with ASD and 279 typically developing control subjects (ages 6-30 years) within the EU-AIMS Longitudinal European Autism Project (LEAP). The authors also examined neurodevelopmental differences and their potential pathophysiological mechanisms between clinical ASD subgroups that differed in the severity and pattern of sensory features.Results: In addition to significant between-group differences in "core" ASD brain regions (i.e., fronto-temporal and cingulate regions), individuals with ASD manifested as neuroanatomical outliers within the neurotypical cortical thickness range in a wider neural system, which was enriched for genes known to be implicated in ASD on the genetic and/or transcriptomic level. Within these regions, the individuals' total (i.e., accumulated) degree of neuroanatomical atypicality was significantly correlated with higher polygenic scores for ASD and other psychiatric conditions, and it scaled with measures of symptom severity. Differences in cortical thickness deviations were also associated with distinct sensory subgroups, especially in brain regions expressing genes involved in excitatory rather than inhibitory neurotransmission.Conclusions: The study findings corroborate the link between macroscopic differences in brain anatomy and the molecular mechanisms underpinning heterogeneity in ASD, and provide future targets for stratification and subtyping