: The benefits of multi-detector row CT (MDCT) relative to single-detector row helical CT are considerable. Multi-detector row CT allows shorter acquisition times, greater coverage, and superior image resolution. These factors substantially increase the diagnostic accuracy of the examination. Three-dimensional (3D) volume data from MDCT provides various unique applications on thoracic diseases. These includes isotropic viewings, use of multiplanar reformation (MPR), maximum and minimum intensity projections (MIP and minIP), and volume rendering performed from external and internal perspectives allowing the user to "fly around" and "fly through" the structures. Recent advances in 3D volume rendering put real-time, interactive virtual reality guidance of the procedures such as bronchoscopy and surgery into practice.

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In the central nervous system, many neurons develop axonal arbors that are crucial for information processing. Previous studies have demonstrated that premature axons contain motile and stationary mitochondria, and their balance is important for axonal arborization. However, the mechanisms by which neurons determine the positions of stationary mitochondria as well as their turnover remain to be elucidated. In this study, we investigated the regulation of spatiotemporal group dynamics of stationary mitochondria. We observed that the distribution of stationary mitochondrial spots along the unmyelinated and nonsynaptic axons is not random but rather relatively uniform both in vitro and in vivo. Intriguingly, whereas the positions of each mitochondrial spot changed over time, the overall distribution remained uniform. In addition, local inactivation of mitochondria inhibited the translocation of mitochondrial spots in adjacent axonal regions, suggesting that functional mitochondria enhance the motility of neighboring mitochondria. Furthermore, we showed that the ATP concentration was relatively high around mitochondria, and treating axons with phosphocreatine, which supplies ATP, reduced the immobile mitochondria induced by local mitochondrial inhibition. These observations indicate that intermitochondrial interactions, mediated by ATP signaling, control the uniform distribution of axonal mitochondria. The present study reveals a novel cellular system that collectively regulates stationary mitochondria in axons.

Abstract-Change detection, or anomaly detection, from street-view images acquired by an autonomous robot at multiple different times, is a major problem in robotic mapping and autonomous driving. Formulation as an image comparison task, which operates on a given pair of query and reference images is common to many existing approaches to this problem. Unfortunately, providing relevant reference images is not straightforward. In this paper, we propose a novel formulation for change detection, termed compressive change retrieval, which can operate on a query image and similar reference images retrieved from the web. Compared to previous formulations, there are two sources of difficulty. First, the retrieved reference images may frequently contain non-relevant reference images, because even state-of-the-art place-recognition techniques suffer from retrieval noise. Second, image comparison needs to be conducted in a compressed domain to minimize the storage cost of large collections of street-view images. To address the above issues, we also present a practical change detection algorithm that uses compressed bag-of-words (BoW) image representation as a scalable solution. The results of experiments conducted on a practical change detection task, "moving object detection (MOD)," using the publicly available Malaga dataset validate the effectiveness of the proposed approach.

This paper addresses the problem of change detection from a novel perspective of long-term map learning. We are particularly interested in designing an approach that can scale to large maps and that can function under global uncertainty in the viewpoint (i.e., GPS-denied situations). Our approach, which utilizes a compact bag-of-words (BoW) scene model, makes several contributions to the problem: 1) Two kinds of prior information are extracted from the view sequence map and used for change detection. Further, we propose a novel type of prior, called motion prior, to predict the relative motions of stationary objects and anomaly ego-motion detection. The proposed prior is also useful for distinguishing stationary from non-stationary objects. 2) A small set of good reference images (e.g., 10) are efficiently retrieved from the view sequence map by employing the recently developed Bag-of-Local-Convolutional-Features (BoLCF) scene model. 3) Change detection is reformulated as a scene retrieval over these reference images to find changed objects using a novel spatial Bag-of-Words (SBoW) scene model. Evaluations conducted of individual techniques and also their combinations on a challenging dataset of highly dynamic scenes in the publicly available Malaga dataset verify their efficacy.