Effects of phorbol myristate acetate and sivelestat on the lung injury caused by fat embolism in isolated lungs

<p>Abstract</p> <p>Background</p> <p>Fat embolism syndrome (FES) associated with acute lung injury (ALI) is a clinical condition following long bone fracture. We have reported 14 victims due to ALI with FES. Our laboratory has developed an animal model that produced fat emboli (FE). The major purpose of this study was to test whether neutrophil activation with phorbol myristate acetate (PMA) and inhibition with sivelestat (SVT) exert protection on the lung.</p> <p>Methods</p> <p>The lungs of Sprague-Dawley rats were isolated and perfused. FE was produced by addition of corn oil micelles into the lung perfusate. PMA and SVT were given simultaneously with FE. Parameters such as lung weight/body weight ratio, LW gain, exhaled nitric oxide (NO), protein concentration in bronchoalveolar lavage relating to ALI were measured. The neutrophil elastase (NE), myeloperoxidase, malondialdehyde and phopholipase A₂activity were determined. We also measured the nitrate/nitrite, methyl guanidine (MG), and cytokines. Pulmonary arterial pressure and microvascular permeability were assessed. Lung pathology was examined and scored. The inducible and endothelial NO synthase (iNOS and eNOS) were detected.</p> <p>Results</p> <p>FE caused ALI and increased biochemical factors. The challenge also resulted in pulmonary hypertension and increased microvascular permeability. The NE appeared to be the first to reach its peak at 1 hr, followed by other factors. Coadministration with PMA exacerbated the FE-induced changes, while SVT attenuated the effects of FE.</p> <p>Conclusions</p> <p>The FE-induced lung changes were enhanced by PMA, while SVT had the opposite effect. Sivelestat, a neutrophil inhibitor may be a therapeutic choice for patients with acute respiratory distress syndrome (ARDS) following fat embolism.</p

An LSTM Based Generative Adversarial Architecture for Robotic Calligraphy Learning System

Robotic calligraphy is a very challenging task for the robotic manipulators, which can sustain industrial manufacturing. The active mechanism of writing robots require a large sized training set including sequence information of the writing trajectory. However, manual labelling work on those training data may cause the time wasting for researchers. This paper proposes a machine calligraphy learning system using a Long Short-Term Memory (LSTM) network and a generative adversarial network (GAN), which enables the robots to learn and generate the sequences of Chinese character stroke (i.e., writing trajectory). In order to reduce the size of the training set, a generative adversarial architecture combining an LSTM network and a discrimination network is established for a robotic manipulator to learn the Chinese calligraphy regarding its strokes. In particular, this learning system converts Chinese character stroke image into the trajectory sequences in the absence of the stroke trajectory writing sequence information. Due to its powerful learning ability in handling motion sequences, the LSTM network is used to explore the trajectory point writing sequences. Each generation process of the generative adversarial architecture contains a number of loops of LSTM. In each loop, the robot continues to write by following a new trajectory point, which is generated by LSTM according to the previously written strokes. The written stroke in an image format is taken as input to the next loop of the LSTM network until the complete stroke is finally written. Then, the final output of the LSTM network is evaluated by the discriminative network. In addition, a policy gradient algorithm based on reinforcement learning is employed to aid the robot to find the best policy. The experimental results show that the proposed learning system can effectively produce a variety of high-quality Chinese stroke writing

Comparison and prediction of pullout strength of conical and cylindrical pedicle screws within synthetic bone

<p>Abstract</p> <p>Background</p> <p>This study was designed to derive the theoretical formulae to predict the pullout strength of pedicle screws with an inconstant outer and/or inner diameter distribution (conical screws). For the transpedicular fixation, one of the failure modes is the screw loosening from the vertebral bone. Hence, various kinds of pedicle screws have been evaluated to measure the pullout strength using synthetic and cadaveric bone as specimens. In the literature, the Chapman's formula has been widely proposed to predict the pullout strength of screws with constant outer and inner diameters (cylindrical screws).</p> <p>Methods</p> <p>This study formulated the pullout strength of the conical and cylindrical screws as the functions of material, screw, and surgery factors. The predicted pullout strength of each screw was compared to the experimentally measured data. Synthetic bones were used to standardize the material properties of the specimen and provide observation of the loosening mechanism of the bone/screw construct.</p> <p>Results</p> <p>The predicted data from the new formulae were better correlated with the mean pullout strength of both the cylindrical and conical screws within an average error of 5.0% and <it>R</it>²= 0.93. On the other hand, the average error and <it>R</it>²value of the literature formula were as high as -32.3% and -0.26, respectively.</p> <p>Conclusion</p> <p>The pullout strength of the pedicle screws was the functions of bone strength, screw design, and pilot hole. The close correlation between the measured and predicted pullout strength validated the value of the new formulae, so as avoid repeating experimental tests.</p

GANCCRobot:Generative Adversarial Nets based Chinese Calligraphy Robot

Robotic calligraphy, as a typical application of robot movement planning, is of great significance for the inheritance and education of calligraphy culture. The existing implementations of such robots often suffer from its limited ability for font generation and evaluation, leading to poor writing style diversity and writing quality. This paper proposes a calligraphic robotic framework based on the generative adversarial nets (GAN) to address such limitation. The robot implemented using such framework is able to learn to write fundamental Chinese character strokes with rich diversities and good quality that is close to the human level, without the requirement of specifically designed evaluation functions thanks to the employment of the revised GAN. In particular, the type information of the stroke is introduced as condition information, and the latent codes are applied to maximize the style quality of the generated strokes. Experimental results demonstrate that the proposed model enables a calligraphic robot to successfully write fundamental Chinese strokes based on a given type and style, with overall good quality. Although the proposed model was evaluated in this report using calligraphy writing, the underpinning research is readily applicable to many other applications, such as robotic graffiti and character style conversion

Type-2 Fuzzy Hybrid Controller Network for Robotic Systems

Dynamic control, including robotic control, faces both the theoretical challenge of obtaining accurate system models and the practical difficulty of defining uncertain system bounds. To facilitate such challenges, this paper proposes a control system consisting of a novel type of fuzzy neural network and a robust compensator controller. The new fuzzy neural network is implemented by integrating a number of key components embedded in a Type-2 fuzzy cerebellar model articulation controller (CMAC) and a brain emotional learning controller (BELC) network, thereby mimicking an ideal sliding mode controller. The system inputs are fed into the neural network through a Type-2 fuzzy inference system (T2FIS), with the results subsequently piped into sensory and emotional channels which jointly produce the final outputs of the network. That is, the proposed network estimates the nonlinear equations representing the ideal sliding mode controllers using a powerful compensator controller with the support of T2FIS and BELC, guaranteeing robust tracking of the dynamics of the controlled systems. The adaptive dynamic tuning laws of the network are developed by exploiting the popular brain emotional learning rule and the Lyapunov function. The proposed system was applied to a robot manipulator and a mobile robot, demonstrating its efficacy and potential; and a comparative study with alternatives indicates a significant improvement by the proposed system in performing the intelligent dynamic control

A Robotic Writing Framework-Learning Human Aesthetic Preferences via Human-Machine Interactions

Intelligent robots are required to fully understand human intentions and operations in order to support or collaborate with humans to complete complicated tasks, which is typically implemented by employing human-machine interaction techniques. This paper proposes a new robotic learning framework to perform numeral writing tasks by investigating human-machine interactions with human preferences. In particular, the framework implements a trajectory generative module using a generative adversarial network (GAN)-based method and develops a human preference feedback system to enable the robot to learn human preferences. In addition, a convolutional neural network, acting as a discriminative network, classifies numeral images to support the development of the basic numeral writing ability, and another convolutional neural network, acting as a human preference network, learns a human user’s aesthetic preference by taking the feedback on two written numerical images during the training process. The experimental results show that the written numerals based on the preferences of ten users were different from those of the training data set and that the writing models with the preferences from different users generate numerals in different styles, as evidenced by the Fréchet inception distance (FID) scores. The FID scores of the proposed framework with a preference network were noticeably greater than those of the framework without a preference network. This phenomenon indicates that the human-machine interactions effectively guided the robotic system to learn different writing styles. These results prove that the proposed approach is able to enable the calligraphy robot to successfully write numerals in accordance with the preferences of a human user

Decoder Choice Network for Metalearning

Metalearning has been widely applied for implementing few-shot learning and fast model adaptation. Particularly, existing metalearning methods have been exploited to learn the control mechanism for gradient descent processes, in an effort to facilitate gradient-based learning in gaining high speed and generalization ability. This article presents a novel method that controls the gradient descent process of the model parameters in a neural network, by limiting the model parameters within a low-dimensional latent space. The main challenge for implementing this idea is that a decoder with many parameters may be required. To tackle this problem, the article provides an alternative design of the decoder with a structure that shares certain weights, thereby reducing the number of required parameters. In addition, this work combines ensemble learning with the proposed approach to improve the overall learning performance. Systematic experimental studies demonstrate that the proposed approach offers results superior to the state of the art in performing the Omniglot classification and miniImageNet classification tasks

Integration of an actor-critic model and generative adversarial networks for a Chinese calligraphy robot

As a combination of robotic motion planning and Chinese calligraphy culture, robotic calligraphy plays a significant role in the inheritance and education of Chinese calligraphy culture. Most existing calligraphy robots focus on enabling the robots to learn writing through human participation, such as human–robot interactions and manually designed evaluation functions. However, because of the subjectivity of art aesthetics, these existing methods require a large amount of implementation work from human engineers. In addition, the written results cannot be accurately evaluated. To overcome these limitations, in this paper, we propose a robotic calligraphy model that combines a generative adversarial network (GAN) and deep reinforcement learning to enable a calligraphy robot to learn to write Chinese character strokes directly from images captured from Chinese calligraphic textbooks. In our proposed model, to automatically establish an aesthetic evaluation system for Chinese calligraphy, a GAN is first trained to understand and reconstruct stroke images. Then, the discriminator network is independently extracted from the trained GAN and embedded into a variant of the reinforcement learning method, the “actor-critic model”, as a reward function. Thus, a calligraphy robot adopts the improved actor-critic model to learn to write multiple character strokes. The experimental results demonstrate that the proposed model successfully allows a calligraphy robot to write Chinese character strokes based on input stroke images. The performance of our model, compared with the state-of-the-art deep reinforcement learning method, shows the efficacy of the combination approach. In addition, the key technology in this work shows promise as a solution for robotic autonomous assembly

Use of human gestures for controlling a mobile robot via adaptive CMAC network and fuzzy logic controller

Mobile robots with manipulators have been more and more commonly applied in extreme and hostile environments to assist or even replace human operators for complex tasks. In addition to autonomous abilities, mobile robots need to facilitate the human–robot interaction control mode that enables human users to easily control or collaborate with robots. This paper proposes a system which uses human gestures to control an autonomous mobile robot integrating a manipulator and a video surveillance platform. A human user can control the mobile robot just as one drives an actual vehicle in the vehicle’s driving cab. The proposed system obtains human’s skeleton joints information using a motion sensing input device, which is then recognized and interpreted into a set of control commands. This is implemented, based on the availability of training data set and requirement of in-time performance, by an adaptive cerebellar model articulation controller neural network, a finite state machine, a fuzzy controller and purposely designed gesture recognition and control command generation systems. These algorithms work together implement the steering and velocity control of the mobile robot in real-time. The experimental results demonstrate that the proposed approach is able to conveniently control a mobile robot using virtual driving method, with smooth manoeuvring trajectories in various speeds