Role of auditory feedback for vocal production learning in the Egyptian fruit bat.

Some species have evolved the ability to use the sense of hearing to modify existing vocalizations, or even create new ones, which enlarges their repertoires and results in complex communication systems.1 This ability corresponds to various forms of vocal production learning that are all possessed by humans and independently displayed by distantly related vertebrates.1,2,3,4,5,6,7 Among mammals, a few species, including the Egyptian fruit bat,8,9,10 would possess such vocal production learning abilities.7 Yet the necessity of an intact auditory system for the development of the Egyptian fruit bat typical vocal repertoire has not been tested. Furthermore, a systematic causal examination of learned and innate aspects of the entire repertoire has never been performed in any vocal learner. Here we addressed these gaps by eliminating pups' sense of hearing at birth and assessing its effects on vocal production in adulthood. The deafening treatment enabled us to both causally test these bats' vocal learning ability and discern learned from innate aspects of their vocalizations. Leveraging wireless individual audio recordings from freely interacting adults, we show that a subset of the Egyptian fruit bat vocal repertoire necessitates auditory feedback. Intriguingly, these affected vocalizations belong to different acoustic groups in the vocal repertoire of males and females. These findings open the possibilities for targeted studies of the mammalian neural circuits that enable sexually dimorphic forms of vocal learning.

Role of auditory feedback for vocal production learning in the Egyptian fruit-bat.

Some species have evolved the ability to use the sense of hearing to modify existing vocalizations, or even create new ones. This ability corresponds to various forms of vocal production learning that are all possessed by humans, and independently displayed by distantly related vertebrates. Among mammals, a few species, including the Egyptian fruit-bat, would possess such vocal production learning abilities. Yet the necessity of an intact auditory system for the development of the Egyptian fruit-bat typical vocal repertoire has not been tested. Furthermore, a systematic causal examination of learned and innate aspects of the entire repertoire has never been performed in any vocal learner. Here we addressed these gaps by eliminating pups' sense of hearing at birth and assessing its effects on vocal production in adulthood. The deafening treatment enabled us to both causally test these bats vocal learning ability and discern learned from innate aspects of their vocalizations. Leveraging wireless individual audio recordings from freely interacting adults, we show that a subset of the Egyptian fruit-bat vocal repertoire necessitates auditory feedback. Intriguingly, these affected vocalizations belong to different acoustic groups in the vocal repertoire of males and females. These findings open the possibilities for targeted studies of the mammalian neural circuits that enable sexually dimorphic forms of vocal learning.

Retinal vascular segmentation using superpixel-based line operator and its application to vascular topology estimation.

PURPOSE: Automatic methods of analyzing of retinal vascular networks, such as retinal blood vessel detection, vascular network topology estimation, and arteries/veins classification are of great assistance to the ophthalmologist in terms of diagnosis and treatment of a wide spectrum of diseases. METHODS: We propose a new framework for precisely segmenting retinal vasculatures, constructing retinal vascular network topology, and separating the arteries and veins. A nonlocal total variation inspired Retinex model is employed to remove the image intensity inhomogeneities and relatively poor contrast. For better generalizability and segmentation performance, a superpixel-based line operator is proposed as to distinguish between lines and the edges, thus allowing more tolerance in the position of the respective contours. The concept of dominant sets clustering is adopted to estimate retinal vessel topology and classify the vessel network into arteries and veins. RESULTS: The proposed segmentation method yields competitive results on three public data sets (STARE, DRIVE, and IOSTAR), and it has superior performance when compared with unsupervised segmentation methods, with accuracy of 0.954, 0.957, and 0.964, respectively. The topology estimation approach has been applied to five public databases (DRIVE,STARE, INSPIRE, IOSTAR, and VICAVR) and achieved high accuracy of 0.830, 0.910, 0.915, 0.928, and 0.889, respectively. The accuracies of arteries/veins classification based on the estimated vascular topology on three public databases (INSPIRE, DRIVE and VICAVR) are 0.90.9, 0.910, and 0.907, respectively. CONCLUSIONS: The experimental results show that the proposed framework has effectively addressed crossover problem, a bottleneck issue in segmentation and vascular topology reconstruction. The vascular topology information significantly improves the accuracy on arteries/veins classification.

Automatic 2-D/3-D Vessel Enhancement in Multiple Modality Images Using a Weighted Symmetry Filter.

Automated detection of vascular structures is of great importance in understanding the mechanism, diagnosis, and treatment of many vascular pathologies. However, automatic vascular detection continues to be an open issue because of difficulties posed by multiple factors, such as poor contrast, inhomogeneous backgrounds, anatomical variations, and the presence of noise during image acquisition. In this paper, we propose a novel 2-D/3-D symmetry filter to tackle these challenging issues for enhancing vessels from different imaging modalities. The proposed filter not only considers local phase features by using a quadrature filter to distinguish between lines and edges, but also uses the weighted geometric mean of the blurred and shifted responses of the quadrature filter, which allows more tolerance of vessels with irregular appearance. As a result, this filter shows a strong response to the vascular features under typical imaging conditions. Results based on eight publicly available datasets (six 2-D data sets, one 3-D data set, and one 3-D synthetic data set) demonstrate its superior performance to other state-of-the-art methods.

Co-Bootstrapping Saliency.

In this paper, we propose a visual saliency detection algorithm to explore the fusion of various saliency models in a manner of bootstrap learning. First, an original bootstrapping model, which combines both weak and strong saliency models, is constructed. In this model, image priors are exploited to generate an original weak saliency model, which provides training samples for a strong model. Then, a strong classifier is learned based on the samples extracted from the weak model. We use this classifier to classify all the salient and non-salient superpixels in an input image. To further improve the detection performance, multi-scale saliency maps of weak and strong model are integrated, respectively. The final result is the combination of the weak and strong saliency maps. The original model indicates that the overall performance of the proposed algorithm is largely affected by the quality of weak saliency model. Therefore, we propose a co-bootstrapping mechanism, which integrates the advantages of different saliency methods to construct the weak saliency model thus addresses the problem and achieves a better performance. Extensive experiments on benchmark data sets demonstrate that the proposed algorithm outperforms the state-of-the-art methods.

Global gene expression profiling of human bronchial epithelial cells exposed to airborne fine particulate matter collected from Wuhan, China.

BACKGROUND: Many studies have linked ambient fine particulate matter (PM2.5) air pollution to different cardiopulmonary diseases in the general population. However the complex mechanisms underlying PM2.5-induced adverse health effects are not yet to be fully elucidated. METHOD: In this study, we aimed to identify genes and pathways that may contribute to PM2.5-induced lung toxicity in humans through genome-wide approaches. Human bronchial epithelial (HBE) cells, exposed to various concentrations of PM2.5 collected from Wuhan, China, showed decreased cell viability in a dose-dependent manner. HBE cells were exposed to 200 µg/ml and 500 µg/ml PM2.5 and microarrays were used to obtain a global view of the transcriptomic responses. RESULTS: A total of 970 and 492 genes were identified that significantly changed after 200 µg/ml and 500 µg/ml PM2.5 exposures, respectively. PM2.5 induced a large number of genes involved in inflammatory and immune response, response to oxidative stress, and response to DNA damage stimulus, which might contribute to PM2.5 related cardiopulmonary diseases. Pathway analysis revealed that different dose of PM2.5 triggered partially common disturbed pathways. Flow cytometry assay evidenced that there were statistically significant differences in the G1 phase of cell cycle after low or high-dose PM2.5 exposure when compared to the unexposed controls. Only high-dose PM2.5 significantly increased the proportion of cells in the S phase of cell cycle. CONCLUSION: We identified many genes and pathways that altered significantly in HBE cells after PM2.5 exposures. These findings are important in providing further understanding of the mechanisms underlying PM2.5-induced adverse health effects.