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Animal colouration is fundamentally important for social communication within conspecifics to advertising threat to competitors or fitness to possible mates. Social status and animal colouration are covarying traits that are plastic in response to dynamic environments. In the African cichlid,Astatotilapia burtoni, body colouration and behaviour have been extensively reported to vary with social rank. However, the nature of the interaction between these two traits is poorly understood. We hypothesise that pigmentation patterns could be linked to the behavioural repertoires underlying social status and can be resolved to regions on the cichlid body plan. To test this hypothesis, we generated Territorial (T) and Non-territorial (NT) males and employed computer vision tools to quantify and visualise patterns/colour enrichment associated with stereotyped T/NT male behaviour. We report colour-behaviour interactions localised in specific areas of the body and face for two colour morphs illustrating a more nuanced view of social behaviour and pigmentation. Since behavioural and morphological variation are key drivers of selection in the East African Great Rift Lakes, we surmise our data may be translatable to other cichlid lineages and underline the importance of trait covariance in sexual selection and male competition. 

Alaska, with its unique geographical and ecological characteristics, is experiencing the detrimental eects of climate change at an alarming rate. The Alaska Native (AN) population, deeply connected to the land and its resources, faces disproportionate vulnerability to these impacts. We call attention to climate change impacts on AN food sovereignty, mental and behavioral health, cultural and spiritual practices, resiliency and adaptation, and how local Alaskan organizations are addressing climate change impacts. This article also highlights the urgent need for environmental public health professionals to engage with AN and Native American communities, address health inequities, and participate in mitigation and adaptation efforts to address the environmental public health threats and consequences of climate change. Increasing awareness of climate related health impacts on these communities is crucial and immediate actions are needed to support safer, healthier, and more sustainable and climate-resilient communities. Government agencies at all levels should also seek to integrate perspectives from Indigenous Peoples, engage in co-management strategies, and provide equitable funding and support for Indigenous communities. Unity, resilience, and adaptation become attainable goals by joining communities in caring for the environment. This message resonates not only in Alaska but also globally, highlighting the need for collective action in the face of climate change. 

Full Authors: Chang, A., Bare, G., Bliss, J., Schaeffer, J.Q., Thomson, A.E., Schmitt, N., and Dyjack, D.T. 

A. Chang*, S.R.V. Chabot, J. Mathews*, S. Briggs, T. Strzalkowski, M. Si, J. Braasch (2022) A spatially-aware companion system for language learning and foreign-language dialogue, In: Proceedings of the 24th International Congress on Acoustics (ICA), October 24–28, Gyeongju, Korea, Paper No. ABS-0631 (A-21, Virtual Acoustics), p. 67–76, https://www.ica2022korea.org/data/Proceedings_A21.pdf Full program -- see: https://www.ica2022korea.org/sub_proceedings.php 

As data are generated more and more from multiple disparate sources, multiview data sets, where each sample has features in distinct views, have grown in recent years. However, no comprehensive package exists that enables non-specialists to use these methods easily. mvlearn is a Python library which implements the leading multiview machine learning methods. Its simple API closely follows that of scikit-learn for increased ease-of-use. The package can be installed from Python Package Index (PyPI) and the conda package manager and is released under the MIT open-source license. The documentation, detailed examples, and all releases are available at https://mvlearn.github.io/. 

We investigate the influence of strain and dislocations on band alignment in GaSb/GaAs quantum dot systems. Composition profiles from cross-sectional scanning tunneling microscopy images are interpolated onto a finite element mesh in order to calculate the distribution of local elastic strain, which is converted to a spatially varying band alignment using deformation potential theory. Our calculations predict that dislocation-induced strain relaxation and charging lead to significant local variations in band alignment. Furthermore, misfit strain induces a transition from a nested (type I) to a staggered (type II) band alignment. Although dislocation-induced strain relaxation prevents the type I to type II transition, electrostatic charging at dislocations induces the staggered band alignment once again.