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Abstract Distinguishing between nectar and non-nectar odors is challenging for animals due to shared compounds and varying ratios in complex mixtures. Changes in nectar production throughout the day and over the animal’s lifetime add to the complexity. The honeybee olfactory system, containing fewer than 1000 principal neurons in the early olfactory relay, the antennal lobe (AL), must learn to associate diverse volatile blends with rewards. Previous studies identified plasticity in the AL circuits, but its role in odor learning remains poorly understood. Using a biophysical computational model, tuned by in vivo electrophysiological data, and live imaging of the honeybee’s AL, we explored the neural mechanisms of plasticity in the AL. Our findings revealed that when trained with a set of rewarded and unrewarded odors, the AL inhibitory network suppresses responses to shared chemical compounds while enhancing responses to distinct compounds. This results in improved pattern separation and a more concise neural code. Our calcium imaging data support these predictions. Analysis of a graph convolutional neural network performing an odor categorization task revealed a similar mechanism for contrast enhancement. Our study provides insights into how inhibitory plasticity in the early olfactory network reshapes the coding for efficient learning of complex odors. 

Bacteriophages are the most numerous, ubiquitous, and diverse biological entities on the planet. Prior studies have identified bacteriophages associated with pathogenic and commensal microbiota of honeybees. In this study we expand on what is known about bacteriophages from the lineages Caudoviricetes, Inoviridae, and Microviridae, which are associated with honeybees (Apidae, Apis mellifera), solitary bees of the genus Nomia (Halictidae, Nomia), and hoverflies (Syrphidae). The complete genomes of seven caudoviruses, seven inoviruses, and 288 microviruses were assembled from honeybees (n = 286) and hoverflies in Arizona (n = 2). We used bacterial host predictive software and sequence read mapping programs to infer the commensal and transient bacterial hosts of pollinating insects. Lastly, this study explores the phylogenetic relationships of microviruses sampled from bees, opportunistically sampled pollinating insects such as hoverflies, and blackflies. 

Tree taper has been of interest for over a century, yet questions remain regarding the effects of silvicultural treatments and forest health on recoverable volume. This work utilizes data from Douglas-fir ( Pseudotsuga menziesii (Mirb.)) ( n = 608) and red alder ( Alnus rubra (Bong.)) ( n = 495) trees to assess the influences of fertilization, pruning, thinning, regeneration origin, and defoliation caused by Swiss Needle Cast (SNC; Nothophaeocryptopus gaeumannii), on stem taper in the Pacific Northwest. The Kozak (2004; For. Chor. 80: 507–515) variable-exponent equation was used to test the addition of treatment and crown variables as the model is widely regarded for its flexibility in application. Using a mixed effects framework, results reveal that thinning of Douglas-fir can result in a 3.5% increase in upper stem diameter inside bark, while pruning may lead to a 4.1% decrease. SNC-induced defoliation of Douglas-fir reduced mean diameter above-breast height by 11.5%. Total volume of artificially regenerated red alder was 16% greater than naturally regenerated stems. Overall, thinning of healthy Douglas-fir and planting red alder may increase recoverable volume and C captured in long-term timber products in the region, and the inclusion of crown variables can increase the predictive power of taper estimates for some species. 

We compare the signal-to-noise ratio for different measurements that could be used for stellar interferometry. We find that single-photon sources with number-resolved detection outperform other weak local oscillator states. 

Errors are the fundamental barrier to the development of quantum systems. Quantum networks are complex systems formed by the interconnection of multiple components and suffer from error accumulation. Characterizing errors introduced by quantum network components becomes a fundamental task to overcome their depleting effects in quantum communication. Quantum Network Tomography (QNT) addresses end-to-end characterization of link errors in quantum networks. It is a tool for building error-aware applications, network management, and system validation. We provide an overview of QNT and its initial results for characterizing quantum star networks. We apply a previously defined QNT protocol for estimating bit-flip channels to estimate depolarizing channels. We analyze the performance of our estimators numerically by assessing the Quantum Cramèr-Rao Bound (QCRB) and the Mean Square Error (MSE) in the finite sample regime. Finally, we provide a discussion on current challenges in the field of QNT and elicit exciting research directions for future investigation. 

When animals learn the association of a conditioned stimulus (CS) with an unconditioned stimulus (US), later presentation of the CS invokes a representation of the US. When the expected US fails to occur, theoretical accounts predict that conditioned inhibition can accrue to any other stimuli that are associated with this change in the US. Empirical work with mammals has confirmed the existence of conditioned inhibition. But the way it is manifested, the conditions that produce it, and determining whether it is the opposite of excitatory conditioning are important considerations. Invertebrates can make valuable contributions to this literature because of the well-established conditioning protocols and access to the central nervous system (CNS) for studying neural underpinnings of behavior. Nevertheless, although conditioned inhibition has been reported, it has yet to be thoroughly investigated in invertebrates. Here, we evaluate the role of the US in producing conditioned inhibition by using proboscis extension response conditioning of the honeybee (Apis mellifera). Specifically, using variations of a “feature-negative” experimental design, we use downshifts in US intensity relative to US intensity used during initial excitatory conditioning to show that an odorant in an odor–odor mixture can become a conditioned inhibitor. We argue that some alternative interpretations to conditioned inhibition are unlikely. However, we show variation across individuals in how strongly they show conditioned inhibition, with some individuals possibly revealing a different means of learning about changes in reinforcement. We discuss how the resolution of these differences is needed to fully understand whether and how conditioned inhibition is manifested in the honeybee, and whether it can be extended to investigate how it is encoded in the CNS. It is also important for extension to other insect models. In particular, work like this will be important as more is revealed of the complexity of the insect brain from connectome projects. 

With increasing understanding of the inextricable connections between learners and the tools that facilitate their learning within complex social systems, new theoretical and methodological developments have emerged to allow us to explore the materiality in learning environments. Sociomateriality (Fenwick, 2015) urges us to consider the interdependence of social and material elements in learning. Rather than viewing classroom spaces and educational tools as static, inert material objects, sociomateriality posits them as capable of exerting force by the way they are acted on or by. This approach has the potential to help respond to the global crises by interrogating and recoupling learning and knowledge with networks and the power relationships inherent in all learning. To this end, this symposium aims to bring researchers together around a common theme of unpacking how sociomateriality might be used as a theoretical foundation or analytical approach for Learning Sciences research. 

Abstract Geographic barriers are frequently invoked to explain genetic structuring across the landscape. However, inferences on the spatial and temporal origins of population variation have been largely limited to evolutionary neutral models, ignoring the potential role of natural selection and intrinsic genomic processes known as genomic architecture in producing heterogeneity in differentiation across the genome. To test how variation in genomic characteristics (e.g. recombination rate) impacts our ability to reconstruct general patterns of differentiation between species that cooccur across geographic barriers, we sequenced the whole genomes of multiple bird populations that are distributed across rivers in southeastern Amazonia. We found that phylogenetic relationships within species and demographic parameters varied across the genome in predictable ways. Genetic diversity was positively associated with recombination rate and negatively associated with species tree support. Gene flow was less pervasive in genomic regions of low recombination, making these windows more likely to retain patterns of population structuring that matched the species tree. We further found that approximately a third of the genome showed evidence of selective sweeps and linked selection, skewing genome-wide estimates of effective population sizes and gene flow between populations toward lower values. In sum, we showed that the effects of intrinsic genomic characteristics and selection can be disentangled from neutral processes to elucidate spatial patterns of population differentiation.