Cis-elements of protein transport to the plant vacuoles

Vacuolar proteins are synthesized and translocated into the endoplasmic reticulum and transported to the vacuoles through the secretory pathway. Three different types of vacuolar sorting signals have been identified, carried by N- or C-terminal propeptides or internal sequences. These signals are needed to target proteins to the different types of vacuoles that can coexist in a single plant cell. A conserved motif (NPIXL or NPIR) was identified within N-terminal propeptides, but can also function in a C-terminal propeptide and targets proteins in a receptor-mediated manner to a lytic vacuole. Binding to a family of putative sorting receptors for sequence-specific vacuolar sorting signals has been used as an assay to identify further peptides with other binding motifs. No motif was found in C-terminal sorting sequences, which need an accessible terminus, suggesting that they are recognized from the end by a still unknown receptor. The phosphatidylinositol kinase inhibitor wortmannin differentially affects sorting mediated by these two sorting sequences, suggesting different sorting mechanisms. Less is known about sorting mediated by internal protein sequences, which do not contain the conserved motif identified in N-terminal propeptides and may function by aggregation, leading to transport by coat-less dense vesicles to protein storage vacuoles. Even less is known about the sorting of tonoplast proteins, for which several sorting systems will also be neede

Filmy Cloud Removal on Satellite Imagery with Multispectral Conditional Generative Adversarial Nets

In this paper, we propose a method for cloud removal from visible light RGB satellite images by extending the conditional Generative Adversarial Networks (cGANs) from RGB images to multispectral images. Satellite images have been widely utilized for various purposes, such as natural environment monitoring (pollution, forest or rivers), transportation improvement and prompt emergency response to disasters. However, the obscurity caused by clouds makes it unstable to monitor the situation on the ground with the visible light camera. Images captured by a longer wavelength are introduced to reduce the effects of clouds. Synthetic Aperture Radar (SAR) is such an example that improves visibility even the clouds exist. On the other hand, the spatial resolution decreases as the wavelength increases. Furthermore, the images captured by long wavelengths differs considerably from those captured by visible light in terms of their appearance. Therefore, we propose a network that can remove clouds and generate visible light images from the multispectral images taken as inputs. This is achieved by extending the input channels of cGANs to be compatible with multispectral images. The networks are trained to output images that are close to the ground truth using the images synthesized with clouds over the ground truth as inputs. In the available dataset, the proportion of images of the forest or the sea is very high, which will introduce bias in the training dataset if uniformly sampled from the original dataset. Thus, we utilize the t-Distributed Stochastic Neighbor Embedding (t-SNE) to improve the problem of bias in the training dataset. Finally, we confirm the feasibility of the proposed network on the dataset of four bands images, which include three visible light bands and one near-infrared (NIR) band

Optical and thrust measurement of a pulse detonation combustor with a coaxial rotary valve

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RESULTATIVES AND THE ALIGNMENT OF ARGUMENTS

In this joint research, we will examine some phenomena which show that the Larsonian VP-shell strucure provides an insightfull account for resultative constructions. In what follows, we mainly restrict ourselves to the transitive resultative construction (TRC) with a resultative PP, ..

Perceptual representation and effectiveness of local figure–ground cues in natural contours

A contour shape strongly influences the perceptual segregation of a figure from the ground. We investigated the contribution of local contour shape to figure-ground segregation. Although previous studies have reported local contour features that evoke figure-ground perception, they were often image features and not necessarily perceptual features. First, we examined whether contour features, specifically, convexity, closure, and symmetry, underlie the perceptual representation of natural contour shapes. We performed similarity tests between local contours, and examined the contribution of the contour features to the perceptual similarities between the contours. The local contours were sampled from natural contours so that their distribution was uniform in the space composed of the three contour features. This sampling ensured the equal appearance frequency of the factors and a wide variety of contour shapes including those comprised of contradictory factors that induce figure in the opposite directions. This sampling from natural contours is advantageous in order to randomly pickup a variety of contours that satisfy a wide range of cue combinations. Multidimensional scaling analyses showed that the combinations of convexity, closure, and symmetry contribute to perceptual similarity, thus they are perceptual quantities. Second, we examined whether the three features contribute to local figure-ground perception. We performed psychophysical experiments to judge the direction of the figure along the local contours, and examined the contribution of the features to the figure-ground judgment. Multiple linear regression analyses showed that closure was a significant factor, but that convexity and symmetry were not. These results indicate that closure is dominant in the local figure-ground perception with natural contours when the other cues coexist with equal probability including contradictory cases

Inflow-Driven Valve System for Pulse Detonation Engines

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