Nurse honeybee workers tend capped brood, which does not require feeding, around the clock.

'Nurse' honeybees tend brood around the clock with attenuated or no circadian rhythms, but the brood signals inducing this behavior remain elusive. We first tested the hypothesis that worker circadian rhythms are regulated by brood pheromones. We monitored locomotor activity of individually isolated nurse bees that were exposed to either various doses of larval extract or synthetic brood ester pheromone (BEP). Bees orally treated with larval extract showed attenuated circadian rhythms in one of four tested colonies; a similar but statistically non-significant trend was seen in two additional colonies. Nurse bees treated with synthetic BEP showed rhythm attenuation in one of three tested colonies. Next, we tested the hypothesis that capped brood, which does not require feeding, nevertheless induces around-the-clock activity in nurses. By combining a new protocol that enables brood care by individually isolated nurse bees, detailed behavioral observations and automatic high-resolution monitoring of locomotor activity, we found that isolated nurses tended capped brood around the clock with attenuated circadian rhythms. Bees individually isolated in similar cages but without brood showed strong circadian rhythms in locomotor activity and rest. This study shows for the first time that the need to feed hungry larvae is not the only factor accounting for around-the-clock activity in nurse bees. Our results further suggest that the transition between activity with and without circadian rhythms is not a simple switch triggered by brood pheromones. Around-the-clock tending may enhance brood development and health in multiple ways that include improved larval feeding, thermoregulation or hygienic behavior.

Multimodal Correlative Preclinical Whole Body Imaging and Segmentation.

Segmentation of anatomical structures and particularly abdominal organs is a fundamental problem for quantitative image analysis in preclinical research. This paper presents a novel approach for whole body segmentation of small animals in a multimodal setting of MR, CT and optical imaging. The algorithm integrates multiple imaging sequences into a machine learning framework, which generates supervoxels by an efficient hierarchical agglomerative strategy and utilizes multiple SVM-kNN classifiers each constrained by a heatmap prior region to compose the segmentation. We demonstrate results showing segmentation of mice images into several structures including the heart, lungs, liver, kidneys, stomach, vena cava, bladder, tumor, and skeleton structures. Experimental validation on a large set of mice and organs, indicated that our system outperforms alternative state of the art approaches. The system proposed can be generalized to various tissues and imaging modalities to produce automatic atlas-free segmentation, thereby enabling a wide range of applications in preclinical studies of small animal imaging.