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Drivers of passive leadership in wild songbirds: species-level differences and spatio-temporally dependent intraspecific effects
Abstract: Collective behaviors are typical for many social species and can have fitness benefits for participating individuals. To maximize the benefits obtained from group living, individuals must coordinate their behaviors to some extent. What are the mechanisms that make certain individuals more likely to initiate collective behaviors, for example, by taking a risk to initially access a resource (i.e., to act as “leaders”)? Here, we examine leading behavior in a natural population of great tits and blue tits. We use automated feeding stations to monitor the feeder visits of tagged individuals within mixed-species flocks, with a small cost (waiting < 2 s) associated with the initial unlocking of the feeder. We find that great tits, males, and individuals with high activity levels were more likely to be leading in each of their feeder visits. Using a null model approach, we demonstrate that the effects of sex and activity on passive leading behavior can be explained by patterns of spatial and temporal occurrence. In other words, these effects can be explained by the times and locations of when individuals visit rather than the actual order of arrival. Hence, an analysis of the causes of leading behavior is needed to separate the effects of different processes. We highlight the importance of understanding the mechanisms behind leading behavior and discuss directions for future experimental work to gain a better understanding of the causes of leadership in natural populations. Significance statement: Many species are social and engage in collective behaviors. To benefit from group actions, individuals need to fulfill different roles. Here, we examine leading behavior during feeding events; who feeds first when birds arrive at a resource? In mixed-species flocks of passerines, great tits (the larger and more dominant species), males, and individuals with higher levels of activity lead more often than blue tits, females, and individuals with lower levels of activity. While the species effect remains even when we control for the locations and dates of individual feeder visits, the effects of sex and activity are dependent on when and where birds choose to feed.
Challenging the geographic bias in recognising large-scale patterns of diversity change
Aim: Geographic structure is a fundamental organising principle in ecological and Earth sciences, and our planet is conceptually divided into distinct geographic clusters (e.g. ecoregions and biomes) demarcating unique diversity patterns. Given recent advances in technology and data availability, however, we ask whether geographically clustering diversity time-series should be the default framework to identify meaningful patterns of diversity change. Location: North America. Taxon: Aves. Methods: We first propose a framework that recognises patterns of diversity change based on similarities in the behaviour of diversity time-series, independent of their specific or relative spatial locations. Specifically, we applied an artificial neural network approach, the self-organising map (SOM), to group time-series of over 0.9 million observations from the North American Breeding Birds Survey (BBS) data from 1973 to 2016. We then test whether time-series identified as having similar behaviour are geographically structured. Results: We find little evidence of strong geographic structure in patterns of diversity change for North American breeding birds. The majority of the recognised diversity time-series patterns tend to be indistinguishable from being independently distributed in space. Main Conclusions: Our results suggest that geographic proximity may not correspond to shared temporal trends in diversity; assuming that geographic clustering is the basis for analysis may bias diversity trend estimation. We suggest that approaches that consider variability independently of geographic structure can serve as a useful addition to existing organising rules of biodiversity time-series.
Hierarchical temporal prediction captures motion processing from retina to higher visual cortex
Visual neurons respond selectively to specific features that become increasingly complex in their form and dynamics from the eyes to the cortex. Retinal neurons prefer localized flashing spots of light, primary visual cortical (V1) neurons moving bars, and those in higher cortical areas, such as middle temporal (MT) cortex, favor complex features like moving textures. Whether there are general computational principles behind this diversity of response properties remains unclear. To date, no single normative model has been able to account for the hierarchy of tuning to dynamic inputs along the visual pathway. Here we show that hierarchical application of temporal prediction - representing features that efficiently predict future sensory input from past sensory input - can explain how neuronal tuning properties, particularly those relating to motion, change from retina to higher visual cortex. This suggests that the brain may not have evolved to efficiently represent all incoming information, as implied by some leading theories. Instead, the selective representation of sensory inputs that help in predicting the future may be a general neural coding principle, which when applied hierarchically extracts temporally-structured features that depend on increasingly high-level statistics of the sensory input.
Simple spectral transformations capture the contribution of peripheral processing to cortical responses to natural sounds
Processing in the sensory periphery involves various mechanisms that enable the detection and discrimination of sensory information. Despite their biological complexity, could these processing steps sub-serve a relatively simple transformation of sensory inputs, which are then transmitted to the CNS? Here we explored both biologically-detailed and very simple models of the auditory periphery to find the appropriate input to a phenomenological model of auditory cortical responses to natural sounds. We examined a range of cochlear models, from those involving detailed biophysical characteristics of the cochlea and auditory nerve to very pared-down spectrogram-like approximations of the information processing in these structures. We tested the capacity of these models to predict the time-course of single-unit neural responses recorded in the ferret primary auditory cortex, when combined with a linear non-linear encoding model. We show that a simple model based on a log-spaced, log-scaled power spectrogram with Hill-function compression performs as well as biophysically-detailed models of the cochlea and the auditory nerve. These findings emphasize the value of using appropriate simple models of the periphery when building encoding models of sensory processing in the brain, and imply that the complex properties of the auditory periphery may together result in a simpler than expected functional transformation of the inputs.
Simple spectral transformations capture the contribution of peripheral processing to cortical responses to natural sounds
Processing in the sensory periphery involves various mechanisms that enable the detection and discrimination of sensory information. Despite their biological complexity, could these processing steps sub-serve a relatively simple transformation of sensory inputs, which are then transmitted to the CNS? Here we explored both biologically-detailed and very simple models of the auditory periphery to find the appropriate input to a phenomenological model of auditory cortical responses to natural sounds. We examined a range of cochlear models, from those involving detailed biophysical characteristics of the cochlea and auditory nerve to very pared-down spectrogram-like approximations of the information processing in these structures. We tested the capacity of these models to predict the time-course of single-unit neural responses recorded in the ferret primary auditory cortex, when combined with a linear non-linear encoding model. We show that a simple model based on a log-spaced, log-scaled power spectrogram with Hill-function compression performs as well as biophysically-detailed models of the cochlea and the auditory nerve. These findings emphasize the value of using appropriate simple models of the periphery when building encoding models of sensory processing in the brain, and imply that the complex properties of the auditory periphery may together result in a simpler than expected functional transformation of the inputs.
Ancient chicken remains reveal the origins of virulence in Marek's disease virus.
The pronounced growth in livestock populations since the 1950s has altered the epidemiological and evolutionary trajectory of their associated pathogens. For example, Marek's disease virus (MDV), which causes lymphoid tumors in chickens, has experienced a marked increase in virulence over the past century. Today, MDV infections kill >90% of unvaccinated birds, and controlling it costs more than US$1 billion annually. By sequencing MDV genomes derived from archeological chickens, we demonstrate that it has been circulating for at least 1000 years. We functionally tested the Meq oncogene, one of 49 viral genes positively selected in modern strains, demonstrating that ancient MDV was likely incapable of driving tumor formation. Our results demonstrate the power of ancient DNA approaches to trace the molecular basis of virulence in economically relevant pathogens.
Evolution of methods to detect paraneoplastic antibodies.
An adaptive immune response in less than 1% of people who develop cancer produces antibodies against neuronal proteins. These antibodies can be associated with paraneoplastic syndromes, and their accurate detection should instigate a search for a specific cancer. Over the years, multiple systems, from indirect immunofluorescence to live cell-based assays, have been developed to identify these antibodies. As the specific antigens were identified, high throughput, multi-antigen substrates such as line blots and ELISAs were developed for clinical laboratories. However, the evolution of assays required to identify antibodies to membrane targets has shone a light on the importance of antigen conformation for antibody detection. This chapter discusses the early antibody assays used to detect antibodies to nuclear and cytosolic targets and how new approaches are required to detect antibodies to membrane targets. The chapter presents recent data that support international recommendations against the sole use of line blots for antibody detection and highlights a new antigen-specific approach that appears promising for the detection of submembrane targets.
Nuclei-specific hypothalamus networks predict a dimensional marker of stress in humans.
The hypothalamus is part of the hypothalamic-pituitary-adrenal axis which activates stress responses through release of cortisol. It is a small but heterogeneous structure comprising multiple nuclei. In vivo human neuroimaging has rarely succeeded in recording signals from individual hypothalamus nuclei. Here we use human resting-state fMRI (n = 498) with high spatial resolution to examine relationships between the functional connectivity of specific hypothalamic nuclei and a dimensional marker of prolonged stress. First, we demonstrate that we can parcellate the human hypothalamus into seven nuclei in vivo. Using the functional connectivity between these nuclei and other subcortical structures including the amygdala, we significantly predict stress scores out-of-sample. Predictions use 0.0015% of all possible brain edges, are specific to stress, and improve when using nucleus-specific compared to whole-hypothalamus connectivity. Thus, stress relates to connectivity changes in precise and functionally meaningful subcortical networks, which may be exploited in future studies using interventions in stress disorders.
A competency framework on simulation modelling-supported decision-making for Master of Public Health graduates
Background Simulation models are increasingly important for supporting decision-making in public health. However, due to lack of training, many public health professionals remain unfamiliar with constructing simulation models and using their outputs for decision-making. This study contributes to filling this gap by developing a competency framework on simulation model-supported decision-making targeting Master of Public Health education. Methods The study combined a literature review, a two-stage online Delphi survey and an online consensus workshop. A draft competency framework was developed based on 28 peer-reviewed publications. A two-stage online Delphi survey involving 15 experts was conducted to refine the framework. Finally, an online consensus workshop, including six experts, evaluated the competency framework and discussed its implementation. Results The competency framework identified 20 competencies related to stakeholder engagement, problem definition, evidence identification, participatory system mapping, model creation and calibration and the interpretation and dissemination of model results. The expert evaluation recommended differentiating professional profiles and levels of expertise and synergizing with existing course contents to support its implementation. Conclusions The competency framework developed in this study is instrumental to including simulation model-supported decision-making in public health training. Future research is required to differentiate expertise levels and develop implementation strategies.
Diagnostic potential of saccadometry in progressive supranuclear palsy.
BACKGROUND: Progressive supranuclear palsy (PSP), an atypical parkinsonian syndrome characterized by extrapyramidal features, imbalance, supranuclear gaze paresis and dementia, can be difficult to diagnose, especially in the early stages. From the clinician's point of view, the main difficulty with this disorder is the inability to provide an accurate diagnosis, at least for the initial stages of the disease, where symptoms are often confused with other parkinsonian disorders. This inability complicates the recruitment of patients with early-stage parkinsonism to trials of disease-modifying therapy. OBJECTIVES: To determine whether quantitative, objective examination of saccadic latency distributions can help to distinguish PSP patients from other groups of parkinsonian patients. MATERIALS & METHODS: We used a newly developed portable saccadometer to compare saccadic latency distributions of a group of PSP patients with two other groups in whom the initial differential diagnosis included PSP: one of these groups had Parkinson's disease and the other had developed a range of parkinsonian conditions (multiple system atrophy, dementia with Lewy bodies and corticobasal degeneration). RESULTS: The use of a combination of saccadic parameters provided a greater discriminative power than the use of only one parameter, such as median latency. Statistical analysis and parameterization of the distributions robustly distinguished the three groups. CONCLUSIONS: This approach appears to have considerable diagnostic potential in allowing a more accurate diagnosis of PSP, and may help particularly to eliminate misdiagnosis with other parkinsonian conditions.
Accelerated Cardiac Parametric Mapping Using Deep Learning-Refined Subspace Models
Cardiac parametric mapping is useful for evaluating cardiac fibrosis and edema. Parametric mapping relies on single-shot heartbeat-by-heartbeat imaging, which is susceptible to intra-shot motion during the imaging window. However, reducing the imaging window requires undersampled reconstruction techniques to preserve image fidelity and spatial resolution. The proposed approach is based on a low-rank tensor model of the multi-dimensional data, which jointly estimates spatial basis images and temporal basis time-courses from an auxiliary parallel imaging reconstruction. The tensor-estimated spatial basis is then further refined using a deep neural network, trained in a fully supervised fashion, improving the fidelity of the spatial basis using learned representations of cardiac basis functions. This two-stage spatial basis estimation will be compared against Fourier-based reconstructions and parallel imaging alone to demonstrate the sharpening and denoising properties of the deep learning-based subspace analysis.