Localization and trafficking of aquaporin 2 in the kidney

Aquaporins (AQPs) are membrane proteins serving in the transfer of water and small solutes across cellular membranes. AQPs play a variety of roles in the body such as urine formation, prevention from dehydration in covering epithelia, water handling in the blood–brain barrier, secretion, conditioning of the sensory system, cell motility and metastasis, formation of cell junctions, and fat metabolism. The kidney plays a central role in water homeostasis in the body. At least seven isoforms, namely AQP1, AQP2, AQP3, AQP4, AQP6, AQP7, and AQP11, are expressed. Among them, AQP2, the anti-diuretic hormone (ADH)-regulated water channel, plays a critical role in water reabsorption. AQP2 is expressed in principal cells of connecting tubules and collecting ducts, where it is stored in Rab11-positive storage vesicles in the basal state. Upon ADH stimulation, AQP2 is translocated to the apical plasma membrane, where it serves in the influx of water. The translocation process is regulated through the phosphorylation of AQP2 by protein kinase A. As soon as the stimulation is terminated, AQP2 is retrieved to early endosomes, and then transferred back to the Rab 11-positive storage compartment. Some AQP2 is secreted via multivesicular bodies into the urine as exosomes. Actin plays an important role in the intracellular trafficking of AQP2. Recent findings have shed light on the molecular basis that controls the trafficking of AQP2

ディープラーニングによるロボットシステムのためのマルチモーダル統合

早大学位記番号:新7031早稲田大

Multimodal integration learning of robot behavior using deep neural networks

AbstractFor humans to accurately understand the world around them, multimodal integration is essential because it enhances perceptual precision and reduces ambiguity. Computational models replicating such human ability may contribute to the practical use of robots in daily human living environments; however, primarily because of scalability problems that conventional machine learning algorithms suffer from, sensory-motor information processing in robotic applications has typically been achieved via modal-dependent processes. In this paper, we propose a novel computational framework enabling the integration of sensory-motor time-series data and the self-organization of multimodal fused representations based on a deep learning approach. To evaluate our proposed model, we conducted two behavior-learning experiments utilizing a humanoid robot; the experiments consisted of object manipulation and bell-ringing tasks. From our experimental results, we show that large amounts of sensory-motor information, including raw RGB images, sound spectrums, and joint angles, are directly fused to generate higher-level multimodal representations. Further, we demonstrated that our proposed framework realizes the following three functions: (1) cross-modal memory retrieval utilizing the information complementation capability of the deep autoencoder; (2) noise-robust behavior recognition utilizing the generalization capability of multimodal features; and (3) multimodal causality acquisition and sensory-motor prediction based on the acquired causality

Associated emotion and its expression in an entertainment robot qrio

Abstract. We human associate and memorize situations with emotional feel-ings at the time, and these experiences affect our daily behaviors. In this pa-per, we will present our attempt to design this character in an entertainment ro-bot QRIO aiming for more genuine Human-Robot interaction. 1

Dynamic Perception after Visually Guided Grasping by a Human-Like Autonomous Robot

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization

Dynamic Perception after Visually Guided Grasping by a Human-Like Autonomous Robot

We will explore dynamic perception following the visually guided grasping of several objects by a human-like autonomous robot. This competency serves for object categorization. Physical interaction with the hand-held object gives the neural network of the robot the rich, coherent and multi-modal sensory input. Multi-layered self-organizing maps are designed and examined in static and dynamic conditions. The results of the tests in the former condition show its capability of robust categorization against noise. The network also shows better performance than a single-layered map does. In the latter condition we focus on shaking behavior by moving only the forearm of the robot. In some combinations of grasping style and shaking radius the network is capable of categorizing two objects robustly. The results show that the network capability to achieve the task largely depends on how to grasp and how to move the objects. These results together with a preliminary simulation are promising toward the self-organization of a high degree of autonomous dynamic object categorization

Alternate stacking of transition metal ions and terephthalic acid molecules for the fabrication of self-assembled multilayers

Self-assembled multilayers consisting of transition metal ions and biscarboxyl acid molecules have been fabricated by a layer-by-layer chemisorption technique. As transition metal ion, zirconium (Zr(IV)) or titanium (Ti(IV)) was employed, while terephthalic acid (TPA) was used as biscarboxyl molecule. In the multilayers, two TPA monolayers were bridged by one monolayer of Zr(IV) or Ti(IV) most likely through coordination bonds between the metal ions and the carboxyl groups in the TPA molecules. Although the both transition metal ions were successfully applied to construct multilayers, the multilayer structure of the Ti–TPA system was more disordered than that of the Zr–TPA system as revealed by grazing incidence X-ray reflectivity