EMX2-GPR156-Gαi reverses hair cell orientation in mechanosensory epithelia

Hair cells detect sound, head position or water movements when their mechanosensory hair bundle is deflected. Each hair bundle has an asymmetric architecture that restricts stimulus detection to a single axis. Coordinated hair cell orientations within sensory epithelia further tune stimulus detection at the organ level. Here, we identify GPR156, an orphan GPCR of unknown function, as a critical regulator of hair cell orientation. We demonstrate that the transcription factor EMX2 polarizes GPR156 distribution, enabling it to signal through Gαi and trigger a 180° reversal in hair cell orientation. GPR156-Gαi mediated reversal is essential to establish hair cells with mirror-image orientations in mouse otolith organs in the vestibular system and in zebrafish lateral line. Remarkably, GPR156-Gαi also instructs hair cell reversal in the auditory epithelium, despite a lack of mirror-image organization. Overall, our work demonstrates that conserved GPR156-Gαi signaling is integral to the framework that builds directional responses into mechanosensory epithelia.

An investigation of benchmark image collections: how different from digital libraries?

Purpose This paper aims to introduce the construction methods, image organization, collection use and access of benchmark image collections to the digital library (DL) community. It aims to connect two distinct communities: the DL community and image processing researchers so that future image collections could be better constructed, organized and managed for both human and computer use. Design/methodology/approach Image collections are first identified through an extensive literature review of published journal articles and a web search. Then, a coding scheme focusing on image collections’ creation, organization, access and use is developed. Next, three major benchmark image collections are analysed based on the proposed coding scheme. Finally, the characteristics of benchmark image collections are summarized and compared to DLs. Findings Although most of the image collections in DLs are carefully curated and organized using various metadata schema based on an image’s external features to facilitate human use, the benchmark image collections created for promoting image processing algorithms are annotated on an image’s content to the pixel level, which makes each image collection a more fine-grained, organized database appropriate for developing automatic techniques on classification summarization, visualization and content-based retrieval. Research limitations/implications This paper overviews image collections by their application fields. The three most representative natural image collections in general areas are analysed in detail based on a homemade coding scheme, which could be further extended. Also, domain-specific image collections, such as medical image collections or collections for scientific purposes, are not covered. Practical implications This paper helps DLs with image collections to understand how benchmark image collections used by current image processing research are created, organized and managed. It informs multiple parties pertinent to image collections to collaborate on building, sustaining, enriching and providing access to image collections. Originality/value This paper is the first attempt to review and summarize benchmark image collections for DL managers and developers. The collection creation process and image organization used in these benchmark image collections open a new perspective to digital librarians for their future DL collection development.

Flickr: Organizing and Tagging Images Online

Flickr was launched when digital cameras first began to outsell analog cameras, and people were drawn to the site for the opportunities it offered them to store, organize, and share their images, as well as for the connections that could be made with other like-minded people. This article examines the links between Flickr’s success and how images are organized within the site, as well as the types of people and organizations that use Flickr and their motivations for doing so. Factors that have contributed to Flickr’s demise in popularity will be explored, and the article finishes with some suggestions for how Flickr could develop in the future, along with some conclusions for image organization.

Get the Picture? Goodness of Image Organization Contributes to Image Memorability

This is a preprint. Please find the published and peer reviewed version of the paper here: https://www.journalofcognition.org/articles/10.5334/joc.80/. Images differ in their intrinsic memorability. The factors driving image memorability are not fully understood. Here, we hypothesized that memorability at least partly depends on goodness of image organization. Good organizations have been associated with fast, efficient processing and robustness against transformation (e.g., shrinking). Based on the main hypothesis, these characteristics would then also pertain to memorable images. Study 1 focused on fast processing and predicted that memorable images are easier to categorize rapidly, while Study 2 predicted that they survive a shrinking transformation better. We used real-life scene images of 14 semantic categories from a previous memorability study. Each image was assigned a “categorizability” and “shrinkability” score based on the average performance across participants on a rapid-scene categorization task (Study 1) and a thumbnail search task (Study 2), respectively. The predicted positive relation between categorizability and memorability was not observed. A post-hoc explanation attributed this null result to a masking role of image distinctiveness. In the thumbnail search task, memorable images were located faster, as predicted, but Study 2 could not rule out that this was merely a result of their distinctiveness. A third study quantified the images on distinctiveness and statistically controlled for this variable in a reanalysis of Study 1 and Study 2. When distinctiveness was controlled for, categorizability and memorability did show a significant positive correlation. Moreover, the results also argued against the alternative explanation of the results of Study 2. Taken together, the results support the hypothesis that goodness of organization contributes to image memorability.

Dynamic Multi-View Hashing for Online Image Retrieval

Advanced hashing technique is essential to facilitate effective large scale online image organization and retrieval, where image contents could be frequently changed. Traditional multi-view hashing methods are developed based on batch-based learning, which leads to very expensive updating cost. Meanwhile, existing online hashing methods mainly focus on single-view data and thus can not achieve promising performance when searching real online images, which are multiple view based data. Further, both types of hashing methods can only produce hash code with fixed length. Consequently they suffer from limited capability to comprehensive characterization of streaming image data in the real world. In this paper, we propose dynamic multi-view hashing (DMVH), which can adaptively augment hash codes according to dynamic changes of image. Meanwhile, DMVH leverages online learning to generate hash codes. It can increase the code length when current code is not able to represent new images effectively. Moreover, to gain further improvement on overall performance, each view is assigned with a weight, which can be efficiently updated during the online learning process. In order to avoid the frequent updating of code length and view weights, an intelligent buffering scheme is also specifically designed to preserve significant data to maintain good effectiveness of DMVH. Experimental results on two real-world image datasets demonstrate superior performance of DWVH over several state-of-the-art hashing methods.