Image statistics of the environment surrounding freely behaving hoverflies.

Natural scenes are not as random as they might appear, but are constrained in both space and time. The 2-dimensional spatial constraints can be described by quantifying the image statistics of photographs. Human observers perceive images with naturalistic image statistics as more pleasant to view, and both fly and vertebrate peripheral and higher order visual neurons are tuned to naturalistic image statistics. However, for a given animal, what is natural differs depending on the behavior, and even if we have a broad understanding of image statistics, we know less about the scenes relevant for particular behaviors. To mitigate this, we here investigate the image statistics surrounding Episyrphus balteatus hoverflies, where the males hover in sun shafts created by surrounding trees, producing a rich and dense background texture and also intricate shadow patterns on the ground. We quantified the image statistics of photographs of the ground and the surrounding panorama, as the ventral and lateral visual field is particularly important for visual flight control, and found differences in spatial statistics in photos where the hoverflies were hovering compared to where they were flying. Our results can, in the future, be used to create more naturalistic stimuli for experimenter-controlled experiments in the laboratory.

Acute sleep loss induces signs of visual discomfort in young men.

Acute sleep loss influences visual processes in humans, such as recognizing facial emotions. However, to the best of our knowledge, no study till date has examined whether acute sleep loss alters visual comfort when looking at images. One image statistic that can be used to investigate the level of visual comfort experienced under visual encoding is the slope of the amplitude spectrum, also referred to as the slope constant. The slope constant describes the spatial distribution of pixel intensities and deviations from the natural slope constant can induce visual discomfort. In the present counterbalanced crossover design study, 11 young men with normal or corrected-to-normal vision participated in two experimental conditions: one night of sleep loss and one night of sleep. In the morning after each intervention, subjects performed a computerized psychophysics task. Specifically, they were required to adjust the slope constant of images depicting natural landscapes and close-ups with a randomly chosen initial slope constant until they perceived each image as most natural looking. Subjects also rated the pleasantness of each selected image. Our analysis showed that following sleep loss, higher slope constants were perceived as most natural looking when viewing images of natural landscapes. Images with a higher slope constant are generally perceived as blurrier. The selected images were also rated as less pleasant after sleep loss. No such differences between the experimental conditions were noted for images of close-ups. The results suggest that sleep loss induces signs of visual discomfort in young men. Possible implications of these findings are discussed.

Image statistics and their processing in insect vision.

Natural scenes may appear random, but are not only constrained in space and time, but also show strong spatial and temporal correlations. Spatial constraints and correlations can be described by quantifying image statistics, which include intuitive measures such as contrast, color and luminance, but also parameters that need some type of transformation of the image. In this review we will discuss some common tools used to quantify spatial and temporal parameters of naturalistic visual input, and how these tools have been used to inform us about visual processing in insects. In particular, we will review findings that would not have been possible using conventional, experimenter defined stimuli.

In situ modeling of multimodal floral cues attracting wild pollinators across environments.

With more than 80% of flowering plant species specialized for animal pollination, understanding how wild pollinators utilize resources across environments can encourage efficient planting and maintenance strategies to maximize pollination and establish resilience in the face of environmental change. A fundamental question is how generalist pollinators recognize "flower objects" in vastly different ecologies and environments. On one hand, pollinators could employ a specific set of floral cues regardless of environment. Alternatively, wild pollinators could recognize an exclusive signature of cues unique to each environment or flower species. Hoverflies, which are found across the globe, are one of the most ecologically important alternative pollinators after bees and bumblebees. Here, we have exploited their cosmopolitan status to understand how wild pollinator preferences change across different continents. Without employing any a priori assumptions concerning the floral cues, we measured, predicted, and finally artificially recreated multimodal cues from individual flowers visited by hoverflies in three different environments (hemiboreal, alpine, and tropical) using a field-based methodology. We found that although "flower signatures" were unique for each environment, some multimodal lures were ubiquitously attractive, despite not carrying any reward, or resembling real flowers. While it was unexpected that cue combinations found in real flowers were not necessary, the robustness of our lures across insect species and ecologies could reflect a general strategy of resource identification for generalist pollinators. Our results provide insights into how cosmopolitan pollinators such as hoverflies identify flowers and offer specific ecologically based cues and strategies for attracting pollinators across diverse environments.

A higher order visual neuron tuned to the spatial amplitude spectra of natural scenes.

Animal sensory systems are optimally adapted to those features typically encountered in natural surrounds, thus allowing neurons with limited bandwidth to encode challengingly large input ranges. Natural scenes are not random, and peripheral visual systems in vertebrates and insects have evolved to respond efficiently to their typical spatial statistics. The mammalian visual cortex is also tuned to natural spatial statistics, but less is known about coding in higher order neurons in insects. To redress this we here record intracellularly from a higher order visual neuron in the hoverfly. We show that the cSIFE neuron, which is inhibited by stationary images, is maximally inhibited when the slope constant of the amplitude spectrum is close to the mean in natural scenes. The behavioural optomotor response is also strongest to images with naturalistic image statistics. Our results thus reveal a close coupling between the inherent statistics of natural scenes and higher order visual processing in insects.

MR evaluation of vessel size imaging of human gliomas: Validation by histopathology.

PURPOSE: To compare the vessel size and the cerebral blood volume in human gliomas with histopathology. Vessel size imaging (VSI) is a dynamic susceptibility contrast method for the assessment of the vessel size in normal and pathological tissue. Previous publications in rodents showed a satisfactory conformity with the vessel size derived from histopathology. To assess the clinical value, further, the progression-free interval was determined and correlated. MATERIALS AND METHODS: Twenty-five gliomas (WHO grade °II [n = 10], °III [n = 3], °IV [n = 12]) were prospectively included and received a stereotaxic biopsy after VSI. The vessel size and the cerebral blood volume (CBV) were calculated in regions of interest at the tumor edge and correlated with the vessel size measured by histopathology. RESULTS: Both VSI and CBV showed a good correlation with the vessel size in histopathology (up to r = 0.84, P < 0.001, and r = 0.62, P < 0.001, respectively). Slope and offset of the linear regression (y = 0.77x + 0.36 µm) suggest that the size of normal capillaries is overestimated with VSI, while for grossly enlarged vessels an underestimation occurs. Both VSI and CBV were negatively correlated with the progression-free interval (r = -0.57, P = 0.008, and r = -0.50, P = 0.025, respectively). CONCLUSION: The correlation between VSI and vessel size from histopathology is in good accordance with the animal studies. The overestimation of small capillary sizes is also known from the animal trials. Vessel size and CBV showed similar results, both for the correlation with the histopathological vessel size and the progression-free interval.