Search for: All records

Social and spatial structures of host populations play important roles in pathogen transmission. For environmentally transmitted pathogens, the host space use interacts with both the host social structure and the pathogen’s environmental persistence (which determines the time-lag across which two hosts can transmit). Together, these factors shape the epidemiological dynamics of environmentally transmitted pathogens. While the importance of both social and spatial structures and environmental pathogen persistence has long been recognized in epidemiology, they are often considered separately. A better understanding of how these factors interact to determine disease dynamics is required for developing robust surveillance and management strategies. Here, we use a simple agent-based model where we vary host mobility (spatial), host gregariousness (social) and pathogen decay (environmental persistence), each from low to high levels to uncover how they affect epidemiological dynamics. By comparing epidemic peak, time to epidemic peak and final epidemic size, we show that longer infectious periods, higher group mobility, larger group size and longer pathogen persistence lead to larger, faster growing outbreaks, and explore how these processes interact to determine epidemiological outcomes such as the epidemic peak and the final epidemic size. We identify general principles that can be used for planning surveillance and control for wildlife host–pathogen systems with environmental transmission across a range of spatial behaviour, social structure and pathogen decay rates. This article is part of the theme issue ‘The spatial–social interface: a theoretical and empirical integration’. 

Postsecondary instructors interested in inquiry-oriented instruction of linear algebra participated in a sequence of eight one-hour online work group meetings with other inquiry-oriented linear algebra instructors and facilitators. Recordings were analyzed for how two participants referenced goals for instruction in discussions of implementing a new instructional unit on subspaces. We identified four goals for the instruction of teaching subspaces. We discuss the intersections of several goals that exist due to the tension caused by real-world contexts and abstract mathematical concepts. The instructors presented resolutions to the tension by utilizing varying teaching knowledge. Based on the results, we make suggestions for those who want to transition to inquiry-oriented instructional approaches. 

Abstract Holistic processing of face and non-face stimuli has been framed as a perceptual strategy, with classic hallmarks of holistic processing, such as the composite effect, reflecting a failure of selective attention, which is a consequence of this strategy. Further, evidence that holistic processing is impacted by training different patterns of attentional prioritization suggest that it may be a result of learned attention to the whole, which renders it difficult to attend to only part of a stimulus. If so, holistic processing should be modulated by the same factors that shape attentional selection, such as the probability that distracting or task-relevant information will be present. In contrast, other accounts suggest that it is the match to an internal face template that triggers specialized holistic processing mechanisms. Here we probed these accounts by manipulating the probability, across different testing sessions, that the task-irrelevant face part in the composite face task will contain task-congruent or -incongruent information. Attentional accounts of holistic processing predict that when the probability that the task-irrelevant part contains congruent information is low (25%), holistic processing should be attenuated compared to when this probability is high (75%). In contrast, template-based accounts of holistic face processing predict that it will be unaffected by manipulation given the integrity of the faces remains intact. Experiment 1 found evidence consistent with attentional accounts of holistic face processing and Experiment 2 extends these findings to holistic processing of non-face stimuli. These findings are broadly consistent with learned attention accounts of holistic processing. 

The recent rise of ‘omics and other molecular research technologies alongside improved techniques for tissue preservation have broadened the scope of marine mammal research. Collecting biological samples from wild marine mammals is both logistically challenging and expensive. To enhance the power of marine mammal research, great effort has been made in both the field and the laboratory to ensure the scientific integrity of samples from collection through processing, supporting the long‐term use of precious samples across a broad range of studies. However, identifying the best methods of sample preservation can be challenging, especially as this technological toolkit continues to evolve and expand. Standardizing best practices could maximize the scientific value of biological samples, foster multi‐institutional collaborative efforts across fields, and improve the quality of individual studies by removing potential sources of error from the collection, handling, and preservation processes. With these aims in mind, we summarize relevant literature, share current expert knowledge, and suggest best practices for sample collection and preservation. This manuscript is intended as a reference resource for scientists interested in exploring collaborative studies and preserving samples in a suitable manner for a broad spectrum of analyses, emphasizing support for ‘omics technologies. 

Graphs are ubiquitous in various domains, such as social networks and biological systems. Despite the great successes of graph neural networks (GNNs) in modeling and analyzing complex graph data, the inductive bias of locality assumption, which involves exchanging information only within neighboring connected nodes, restricts GNNs in capturing long-range dependencies and global patterns in graphs. Inspired by the classic Brachistochrone problem, we seek how to devise a new inductive bias for cutting-edge graph application and present a general framework through the lens of variational analysis. The backbone of our framework is a two-way mapping between the discrete GNN model and continuous diffusion functional, which allows us to design application-specific objective function in the continuous domain and engineer discrete deep model with mathematical guarantees. First, we address over-smoothing in current GNNs. Specifically, our inference reveals that the existing layer-by-layer models of graph embedding learning are equivalent to a ℓ 2 -norm integral functional of graph gradients, which is the underlying cause of the over-smoothing problem. Similar to edge-preserving filters in image denoising, we introduce the total variation (TV) to promote alignment of the graph diffusion pattern with the global information present in community topologies. On top of this, we devise a new selective mechanism for inductive bias that can be easily integrated into existing GNNs and effectively address the trade-off between model depth and over-smoothing. Second, we devise a novel generative adversarial network (GAN) to predict the spreading flows in the graph through a neural transport equation. To avoid the potential issue of vanishing flows, we tailor the objective function to minimize the transportation within each community while maximizing the inter-community flows. Our new GNN models achieve state-of-the-art (SOTA) performance on graph learning benchmarks such as Cora, Citeseer, and Pubmed.