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Abstract Among the animals on this planet, dogs are uniquely adapted for life with humans, a status that exposes them to risks of human-mediated traumatic experiences. At the same time, some lineages of dogs have undergone artificial selection for behavioral phenotypes that might increase risk or resilience to stress exposure, providing an opportunity to examine interactions between heritable and acquired traits. In a large-scale study (N = 4,497), English-speaking dog guardians reported on their dogs’ life histories, current living environments, and provided observer ratings of dog behavior using the Canine Behavior Assessment and Research Questionnaire (C-BARQ). Our analysis revealed that adverse experiences in the first six months of life, such as abuse and relinquishment, were significantly associated with increased aggression and fearfulness in adulthood, even when accounting for factors such as acquisition source, sex, and neuter status. Additionally, effects of adversity on fearful and aggressive behavior systematically varied at the breed level, suggesting heritable factors for risk and resilience for developing particular phenotypes. Our findings establish that breed ancestry and individual experience interact to show fear and aggressive behavior in pet dogs, confirming that socioemotional behavior is shaped by gene-environment interactions. 

The Cellular-Potts model is a powerful and ubiquitous framework for developing computational models for simulating complex multicellular biological systems. Cellular-Potts models (CPMs) are often computationally expensive due to the explicit modeling of interactions among large numbers of individual model agents and diffusive fields described by partial differential equations (PDEs). In this work, we develop a convolutional neural network (CNN) surrogate model using a U-Net architecture that accounts for periodic boundary conditions. We use this model to accelerate the evaluation of a mechanistic CPM previously used to investigatein vitrovasculogenesis. The surrogate model was trained to predict 100 computational steps ahead (Monte-Carlo steps, MCS), accelerating simulation evaluations by a factor of 562 times compared to single-core CPM code execution on CPU. Over short timescales of up to 3 recursive evaluations, or 300 MCS, our model captures the emergent behaviors demonstrated by the original Cellular-Potts model such as vessel sprouting, extension and anastomosis, and contraction of vascular lacunae. This approach demonstrates the potential for deep learning to serve as a step toward efficient surrogate models for CPM simulations, enabling faster evaluation of computationally expensive CPM simulations of biological processes. 

The Colorado potato beetle (CPB) is the primary defoliator of potatoes and is notorious for its ability to develop resistance to various insecticides. This remarkable adaptability may partly reflect selective pressures imposed due to the beetle’s coevolution with toxic Solanaceous host plants. As the initial interface between the environment and the insect olfactory system, odorant-binding proteins (OBPs) may sequester excess harmful molecules, such as insecticides and plant allelochemicals, in the perireceptor space, mitigating deleterious effects on vulnerable olfactory sensory neuronal dendrites. In this study, we identified an antenna-specific OBP (LdecOBP33) that is significantly upregulated in a pesticide resistant strain compared to a susceptible one. Competitive displacement fluorescence binding assays demonstrated that the LdecOBP33 protein exhibited broad affinity toward a range of plant volatiles and insecticides. Silencing LdecOBP33 decreased the beetle’s resistance to imidacloprid and impaired its ability to locate host plants. Together, these findings provide insight into a key molecular factor involved in the CPB’s response to environmental challenges, suggesting a potential link between insects’ adaptation to xenobiotics and their olfactory processing. 

Abstract Mechanistic, multicellular, agent-based models are commonly used to investigate tissue, organ, and organism-scale biology at single-cell resolution. The Cellular-Potts Model (CPM) is a powerful and popular framework for developing and interrogating these models. CPMs become computationally expensive at large space- and time- scales making application and investigation of developed models difficult. Surrogate models may allow for the accelerated evaluation of CPMs of complex biological systems. However, the stochastic nature of these models means each set of parameters may give rise to different model configurations, complicating surrogate model development. In this work, we leverage denoising diffusion probabilistic models (DDPMs) to train a generative AI surrogate of a CPM used to investigatein vitrovasculogenesis. We describe the use of an image classifier to learn the characteristics that define unique areas of a 2-dimensional parameter space. We then apply this classifier to aid in surrogate model selection and verification. Our CPM model surrogate generates model configurations 20,000 timesteps ahead of a reference configuration and demonstrates approximately a 22x reduction in computational time as compared to native code execution. Our work represents a step towards the implementation of DDPMs to develop digital twins of stochastic biological systems. 

We report magnetic field measurements spanning about 15 yr of four massive (7.5–15 M⊙) supergiant stars: α Per (HD 20902, F5Iab), α Lep (HD 36673A, F0Ib), η Leo (HD 87737, A0Ib) and 13 Mon (HD 46300, A1Ib). For each star, spectropolarimetric observations were collected using ESPaDOnS at the Canada–France–Hawaii Telescope. The observed spectra were coadded, normalized, and then processed using least-squares deconvolution to yield mean Stokes I and V profiles. Each spectrum was analyzed to infer the false-alarm probability of signal detection, and the longitudinal magnetic field was measured. This process yielded persistent detection of magnetic fields in all four stars. The median 1σ longitudinal field uncertainty of the Zeeman detections was 0.6 G. The maximum unsigned longitudinal magnetic fields measured from the detections are rather weak, ranging from 0.34  ±  0.19 G for α Lep to 2.61  ±  0.55 G for 13 Mon. The Zeeman signatures show different levels of complexity; those of the two hotter stars are relatively simple, while those of the two cooler stars are more complex. The stars also exhibited different levels of variability of their Zeeman signatures and longitudinal fields. We report periodic variability of the longitudinal field and (complex) Stokes V profiles of α Per with a period of either 50.75 days or 90 days. The (simple) Stokes V profiles of 13 Mon, and probably those of η Leo, show global polarity changes once during the period of observation, but the data are insufficient to place strong constraints on the variability timescales. 

Abstract Human-induced warming is amplified in the Arctic, but its causes and consequences are not precisely known. Here, we review scientific advances facilitated by the Polar Amplification Model Intercomparison Project. Surface heat flux changes and feedbacks triggered by sea-ice loss are critical to explain the magnitude and seasonality of Arctic amplification. Tropospheric responses to Arctic sea-ice loss that are robust across models and separable from internal variability have been revealed, including local warming and moistening, equatorward shifts of the jet stream and storm track in the North Atlantic, and fewer and milder cold extremes over North America. Whilst generally small compared to simulated internal variability, the response to Arctic sea-ice loss comprises a non-negligible contribution to projected climate change. For example, Arctic sea-ice loss is essential to explain projected North Atlantic jet trends and their uncertainty. Model diversity in the simulated responses has provided pathways to observationally constrain the real-world response.