965 research outputs found
Motion Switching with Sensory and Instruction Signals by designing Dynamical Systems using Deep Neural Network
To ensure that a robot is able to accomplish an extensive range of tasks, it
is necessary to achieve a flexible combination of multiple behaviors. This is
because the design of task motions suited to each situation would become
increasingly difficult as the number of situations and the types of tasks
performed by them increase. To handle the switching and combination of multiple
behaviors, we propose a method to design dynamical systems based on point
attractors that accept (i) "instruction signals" for instruction-driven
switching. We incorporate the (ii) "instruction phase" to form a point
attractor and divide the target task into multiple subtasks. By forming an
instruction phase that consists of point attractors, the model embeds a subtask
in the form of trajectory dynamics that can be manipulated using sensory and
instruction signals. Our model comprises two deep neural networks: a
convolutional autoencoder and a multiple time-scale recurrent neural network.
In this study, we apply the proposed method to manipulate soft materials. To
evaluate our model, we design a cloth-folding task that consists of four
subtasks and three patterns of instruction signals, which indicate the
direction of motion. The results depict that the robot can perform the required
task by combining subtasks based on sensory and instruction signals. And, our
model determined the relations among these signals using its internal dynamics.Comment: 8 pages, 6 figures, accepted for publication in RA-L. An accompanied
video is available at this https://youtu.be/a73KFtOOB5
Tumor cell invasion from the marginal sinus into extranodal veins during early-stage lymph node metastasis can be a starting point for hematogenous metastasis
Aim: To investigate whether tumor cells in a lymph node (LN) can invade from the marginal sinus into extranodal veins via vessel branches that communicate with intranodal veins and whether this can be a starting point for hematogenous metastasis at the early stage of LN metastasis.Methods: Vascular and lymphatic networks of LNs in MXH10/Mo-lpr/lpr mice were investigated using three-dimensional micro-computed tomography and histological methods. Flow in the blood vessel networks of LNs was investigated by fluorescence microscopy. Tumor cells were injected into the subiliac LNs of MXH10/Mo-lpr/lpr mice to induce metastasis to the proper axillary LNs. Tumor development in the proper axillary LN was detected using an in vivo bioluminescence imaging system. A two-dimensional image of the proper axillary LN microvasculature was reconstructed using a contrast-enhanced high-frequency ultrasound system.Results: Extranodal veins communicated with intranodal veins via branches that penetrated the capsule, and blood flowed from intranodal veins to extranodal veins. Tumor cells that had metastasized to the marginal sinus invaded these communicating veins to develop hematogenous metastases.Conclusion: Metastatic LNs that would be considered by clinical imaging to be stage N0 can be a starting point for hematogenous metastasis. The study findings highlight the need for the development of novel techniques for the diagnosis and treatment of early-stage LN metastasis, i.e., when standard diagnostic imaging might incorrectly classify the LN as stage N0
Compensation for undefined behaviors during robot task execution by switching controllers depending on embedded dynamics in RNN
Robotic applications require both correct task performance and compensation
for undefined behaviors. Although deep learning is a promising approach to
perform complex tasks, the response to undefined behaviors that are not
reflected in the training dataset remains challenging. In a human-robot
collaborative task, the robot may adopt an unexpected posture due to collisions
and other unexpected events. Therefore, robots should be able to recover from
disturbances for completing the execution of the intended task. We propose a
compensation method for undefined behaviors by switching between two
controllers. Specifically, the proposed method switches between learning-based
and model-based controllers depending on the internal representation of a
recurrent neural network that learns task dynamics. We applied the proposed
method to a pick-and-place task and evaluated the compensation for undefined
behaviors. Experimental results from simulations and on a real robot
demonstrate the effectiveness and high performance of the proposed method.Comment: To appear in IEEE Robotics and Automation Letters (RA-L) and IEEE
International Conference on Robotics and Automation (ICRA 2021
Atiyah-Patodi-Singer index on a lattice
We propose a non-perturbative formulation of the Atiyah-Patodi-Singer(APS)
index in lattice gauge theory, in which the index is given by the
invariant of the domain-wall Dirac operator. Our definition of the index is
always an integer with a finite lattice spacing. To verify this proposal, using
the eigenmode set of the free domain-wall fermion, we perturbatively show in
the continuum limit that the curvature term in the APS theorem appears as the
contribution from the massive bulk extended modes, while the boundary
invariant comes entirely from the massless edge-localized modes.Comment: 14 pages, appendices added, details of key equations added, typos
corrected, to appear in PTE
Tool-Use Model to Reproduce the Goal Situations Considering Relationship Among Tools, Objects, Actions and Effects Using Multimodal Deep Neural Networks
We propose a tool-use model that enables a robot to act toward a provided goal. It is important to consider features of the four factors; tools, objects actions, and effects at the same time because they are related to each other and one factor can influence the others. The tool-use model is constructed with deep neural networks (DNNs) using multimodal sensorimotor data; image, force, and joint angle information. To allow the robot to learn tool-use, we collect training data by controlling the robot to perform various object operations using several tools with multiple actions that leads different effects. Then the tool-use model is thereby trained and learns sensorimotor coordination and acquires relationships among tools, objects, actions and effects in its latent space. We can give the robot a task goal by providing an image showing the target placement and orientation of the object. Using the goal image with the tool-use model, the robot detects the features of tools and objects, and determines how to act to reproduce the target effects automatically. Then the robot generates actions adjusting to the real time situations even though the tools and objects are unknown and more complicated than trained ones
Usefulness of NIRS for medication adherence
The symptoms of attention deficit hyperactivity disorder (ADHD) are inattention, hyperactivity, and impulsiveness. Physicians often prescribe methylphenidate (MPH) for children with ADHD for long periods of time. The purpose of the present study was to investigate the usefulness of near-infrared spectroscopy (NIRS) for evaluating drug effects and improvements in medication adherence in children with ADHD. Subjects were 10 male children diagnosed with ADHD : average age, 9.3 years, and 10 boys with typical development : average age 9.5 years. Children with intellectual disability, autism, and obvious depressive symptoms were excluded. The present study revealed that in the ADHD group, oxy-Hb concentrations in the left and right lateral prefrontal cortex significantly increased during the execution of the Stroop color-word test in both channels when taking MPH. This method was considered to be useful for assessing drug effects on ADHD because NIRS is an objective indicator for evaluating ADHD executive dysfunction and visualizes the activation of frontal lobe function by MPH. A pediatric neurologist explained the results of NIRS while presenting images to the ADHD group, and medication adherence and the drug-taking ratio both markedly improved. Therefore, this therapeutic explanation is an effective strategy for improving medication compliance and adherence among patients
Tapping but Not Massage Enhances Vasodilation and Improves Venous Palpation of Cutaneous Veins
This paper investigated whether tapping on the median cubital vein or massaging the forearm was more effective in obtaining better venous palpation for venipuncture. Forty healthy volunteers in their twenties were subjected to tapping (10 times in 5 sec) or massage (10 strokes in 20 sec from the wrist to the cubital fossa) under tourniquet inflation on the upper arm. Venous palpation was assessed using the venous palpation score (0-6, with 0 being impalpable). Three venous factors―venous depth, cross-sectional area, and elevation―were also measured using ultrasonography. The venous palpation score increased significantly by tapping but not by massage. Moreover, all 3 venous measurements changed significantly by tapping, while only the depth decreased significantly by massage. The three venous measurements correlated significantly with the venous palpation score, indicating that they are useful objective indicators for evaluating vasodilation. We suggest that tapping is an effective vasodilation technique
Regulation of Oxidative Stress and Cardioprotection in Diabetes Mellitus
Analysis of the Framingham data has shown that the risk of heart failure is increased substantially among diabetic patients, while persons with the metabolic syndrome have an increased risk of both atherosclerosis and diabetes mellitus. Sleep apnea may be related to the metabolic syndrome and systemic inflammation through hypoxia, which might also cause the cardiac remodeling by increased oxidative stress. On the other hand, the renin-angiotensin system is activated in diabetes, and local angiotensin II production may lead to oxidative damage via the angiotensin II type 1 receptor. Basic and clinical data indicate that angiotensin II receptor blockers have the potential to preserve left ventricular function and prevent cardiac remodeling that is exaggerated by oxidative stress in patients with diabetes. Thus, alleviation of oxidative stress might be one possible strategy in the treatment of diabetic patients associated with sleep apnea
A motor neuron disease-associated mutation produces non-glycosylated Seipin that induces ER stress and apoptosis by inactivating SERCA2b
遺伝病の原因タンパク質が小胞体ストレスを引き起こすメカニズムの解明 --神経変性疾患の新規治療戦略の確立に向けて--. 京都大学プレスリリース. 2022-12-13.A causal relationship between endoplasmic reticulum (ER) stress and the development of neurodegenerative diseases remains controversial. Here, we focused on Seipinopathy, a dominant motor neuron disease, based on the finding that its causal gene product, Seipin, is a protein that spans the ER membrane twice. Gain-of-function mutations of Seipin produce non-glycosylated Seipin (ngSeipin), which was previously shown to induce ER stress and apoptosis at both cell and mouse levels albeit with no clarified mechanism. We found that aggregation-prone ngSeipin dominantly inactivated SERCA2b, the major calcium pump in the ER, and decreased the calcium concentration in the ER, leading to ER stress and apoptosis in human colorectal carcinoma-derived cells (HCT116). This inactivation required oligomerization of ngSeipin and direct interaction of the C-terminus of ngSeipin with SERCA2b, and was observed in Seipin-deficient neuroblastoma (SH-SY5Y) cells expressing ngSeipin at an endogenous protein level. Our results thus provide a new direction to the controversy noted above
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