A social learning model of conflict and cooperation in human societies

Patterns of conflict and cooperation both within and between societies may be related to the degree of cultural similarity within and between the same societies. A simple model of social learning is used to predict patterns of conflict and cooperation in hypothetical societies that differ in the roles of relatives and nonrelatives in the enculturation of children. The model is illustrated by comparing its predictions to known differences in the patterns of conflict between males inpatrilocal and matrilocal societies.     

The role of context, colour and location cues in socially learned novel food source preferences in starlings, Sternus vulgaris

Although the opportunity for errors in social learning is widely recognised, as yet little research has been directed towards understanding specific inaccuracies, biases and limitations in social learning and the mechanisms that give rise to them. In two experiments I ask how starlings, Sternus vulgaris, identify exemplars of novel feeders previously learned about socially. I find that starlings have a stronger response to feeders in the same context as that in which social learning took place, compared to identical and nonidentical feeders in a different context. Within a context that matches where social learning took place, starlings prefer feeders that show the same location and colour as the feeder demonstrated by the demonstrator starling, and show no preference when colour and location cues are dissociated. This suggests that starlings are relatively accurate social learners, since they show strong responses to novel foraging options only if they match the context, colour and location of options learned about socially, and they do so after very few trials. Furthermore, the responses of the subjects were compatible with conditioned learning-like mechanisms, which provide a useful basis for the further investigation of the origins and implications of errors in social learning.     

Structure-based protein design with deep learning

Since the first revelation of proteins functioning as macromolecular machines through their three dimensional structures, researchers have been intrigued by the marvelous ways the biochemical processes are carried out by proteins. The aspiration to understand protein structures has fueled extensive efforts across different scientific disciplines. In recent years, it has been demonstrated that proteins with new functionality or shapes can be designed via structure-based modeling methods, and the design strategies have combined all available information — but largely piece-by-piece — from sequence derived statistics to the detailed atomic-level modeling of chemical interactions. Despite the significant progress, incorporating data-derived approaches through the use of deep learning methods can be a game changer. In this review, we summarize current progress, compare the arc of developing the deep learning approaches with the conventional methods, and describe the motivation and concepts behind current strategies that may lead to potential future opportunities.     

Reliability of wavefront shaping based on coherent optical adaptive technique in deep tissue focusing

Wavefront shaping can compensate the wavefront distortions in deep tissue focusing, leading to an improved penetration depth. However, when using the backscattered signals as the feedback, unexpected compensation bias may be introduced, resulting in focusing position deviations or even no focus in the illumination focal plane. Here we investigated the reliability of wavefront shaping based on coherent optical adaptive technique in deep tissue focusing by measuring the position deviations between the foci in the illumination focal plane and the epi‐detection plane. The experimental results show that when the penetration depth reaches 150 μm in mouse brain tissue (with scattering coefficient ~22.42 mm^?1) using a 488 nm laser and an objective lens with 0.75 numerical aperture, the center of the real focus will deviate out of one radius range of the Airy disk while the optimized focus in the epi‐detection plane maintained basically at the center. With the penetration depth increases, the peak to background ratio of the focus in the illumination focal plane decreases faster than that in the epi‐detection plane. The results indicate that when the penetration depth reaches 150 μm, feedback based on backscattered signals will make wavefront shaping lose its reliability, which may provide a guidance for applications of non‐invasive precise optogenetics or deep tissue optical stimulation using wavefront shaping methods. A, Intensity distribution in the epi‐detection plane and the illumination focal plane before and after correction, corresponding to brain sections with 250 and 300 μm thickness, respectively. Scale bar is 2 μm. B, Averaged focusing deviations in the epi‐detection plane (optimized) and the illumination focal plane (monitored) after compensation. The unit of the ordinate is one Airy disk diameter. Black dashed line represents one Airy disk radius. Bars represent the SE of each measurement set.      

Ability of predatory native Australian fishes to learn to avoid toxic larvae of the introduced toad Bufo marinus

Of two native Australian fishes naïve to the introduced toad Bufo marinus most barramundi Lates calcarifer rapidly learned to avoid B. marinus tadpoles, while sooty grunter Hephaestus fuliginosus exhibited considerable intraspecific variation in their learning ability. Some sooty grunter learned to avoid tadpoles after only a few attacks, while other individuals continued to attack and reject tadpoles throughout the entire laboratory trials. Individuals of both species recognized and avoided tadpoles 1 day after their previous encounter. None of the fishes died during the trials. The observed variation in behavioural responses of fishes to B. marinus may be due to differences in (1) learning ability, (2) fish hunger levels, and or (3) tadpole palatability and toxicity. The results demonstrate that most barramundi and sooty grunter learn to avoid B. marinus tadpoles with minimal trauma. Consequently, it is anticipated that the toads are unlikely to have a significant negative impact on wild populations of these fishes through direct toxic effects.