The Experts below are selected from a list of 144771 Experts worldwide ranked by ideXlab platform
Atsumi Nitta - One of the best experts on this subject based on the ideXlab platform.
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oral administration of propentofylline a stimulator of nerve growth factor ngf synthesis recovers cholinergic neuronal dysfunction induced by the infusion of anti ngf antibody into the rat septum
Behavioural Brain Research, 1997Co-Authors: Yoshiko Ogihara, Takaaki Hasegawa, Joji Onishi, Atsumi Nitta, Shoei Furukawa, Toshitaka NabeshimaAbstract:We have reported that the continuous infusion of anti-nerve growth factor (NGF) monoclonal antibody into the septum of rats produces an impairment of memory and a decrease in choline acetyltransferase (ChAT) and cholinesterase (ChE) activities in the hippocampus. Propentofylline, a xanthine derivative, has potent stimulatory effects on NGF synthesis/secretion in mouse astrocytes in vitro. To investigate the pharmacological effects of propentofylline in vivo, we induced amnesia in rats by infusing anti-NGF antibody into the septum for 16 days. One group of rats was given no further treatment, while the other group was treated with propentofylline orally once a day for 19 days, commencing 3 days before the implantation of the mini-osmotic pump, and continuing throughout the period during which the animals performed the behavioral tasks. In the treated amnesic rats, Learning and memory in the 3 tasks and ChAT and ChE activity were reduced compared to values in control rats. The administration of propentofylline recovered the decreased Learning Capacity and the deficit in cholinergic marker enzyme activity. These results suggest that the use of NGF stimulators may provide a new approach to the treatment of dementia.
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propentofylline prevents neuronal dysfunction induced by infusion of anti nerve growth factor antibody into the rat septum
European Journal of Pharmacology, 1996Co-Authors: Yoshiko Ogihara, Takaaki Hasegawa, Joji Onishi, Atsumi Nitta, Shoei Furukawa, Toshitaka NabeshimaAbstract:We have reported that the continuous infusion of anti-nerve growth factor (NGF) monoclonal antibody into the septum of rats produces neuronal dysfunction in the cholinergic system. Propentofylline has potent stimulatory effects on NGF synthesis/secretion in mouse astrocytes in vitro. To investigate the pharmacological effects of propentofylline, we used an animal model of dementia in which anti-NGF antibody was infused into the septum for 16 days via a mini-osmotic pump. The rats were treated with propentofylline orally once a day throughout the period during which performance in Learning and memory tasks was observed. In the vehicle-treated dementia rats, Learning and memory ability and choline acetyltransferase and cholinesterase activity were reduced compared to values in the control rats. The administration of propentofylline prevented the decreased Learning Capacity and the deficit in cholinergic marker enzyme activities. These results suggest that the use of NGF stimulators may provide a new approach to the treatment of dementia.
Xianghua Ying - One of the best experts on this subject based on the ideXlab platform.
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radial lens distortion correction by adding a weight layer with inverted foveal models to convolutional neural networks
International Conference on Pattern Recognition, 2018Co-Authors: Yongjie Shi, Danfeng Zhang, Jingsi Wen, Xin Tong, Xianghua Ying, Hongbin ZhaAbstract:Radial lens distortion often exists in images taken by commercial cameras, which does not satisfy the assumption of pinhole camera model. Eliminating the radial lens distortion of an image is necessary as a preprocessing step for many vision applications. Some paper has employed Convolutional Neural Networks (CNNs), to achieve radial distortion correction. They generated images with a large number of images of high variation of radial distortion, which can be well exploited by deep CNN with a high Learning Capacity, and reach the state-of-the-art results. In this paper, we claim that a weight layer with inverted foveal models can be added to these existing CNNs methods for radial distortion correction. In the widely used very deep Resnet-18 model, our method achieves about 20 percent decrease in the loss function with faster convergence compared to the previous methods.
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radial lens distortion correction using convolutional neural networks trained with synthesized images
Asian Conference on Computer Vision, 2016Co-Authors: Jiangpeng Rong, Shiyao Huang, Zeyu Shang, Xianghua YingAbstract:Radial lens distortion often exists in images taken by common cameras, which violates the assumption of pinhole camera model. Estimating the radial lens distortion of an image is an important preprocessing step for many vision applications. This paper intends to employ CNNs (Convolutional Neural Networks), to achieve radial distortion correction. However, the main issue hinder its progress is the scarcity of training data with radial distortion annotations. Inspired by the growing availability of image dataset with non-radial distortion, we propose a framework to address the issue by synthesizing images with radial distortion for CNNs. We believe that a large number of images of high variation of radial distortion is generated, which can be well exploited by deep CNN with a high Learning Capacity. We present quantitative results that demonstrate the ability of our technique to estimate the radial distortion with comparisons against several baseline methods, including an automatic method based on Hough transforms of distorted line images.
Bernard L Simonin - One of the best experts on this subject based on the ideXlab platform.
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an empirical investigation of the process of knowledge transfer in international strategic alliances
Journal of International Business Studies, 2004Co-Authors: Bernard L SimoninAbstract:This research proposes and tests a basic model of organizational Learning that captures the process of knowledge transfer in international strategic alliances. Based on a cross-sectional sample of 147 multinationals and a structural equation methodology, this study empirically investigates the simultaneous effects of Learning intent, Learning Capacity (LC), knowledge ambiguity, and its two key antecedents – tacitness and partner protectiveness – on technological knowledge transfer. In the interest of expanding our understanding of the organizational mechanisms that both hinder and facilitate Learning, the concept of LC is refined into three distinct components: resource-, incentive-, and cognitive-based LC. Further, the strength of the relationships between these theoretical constructs and knowledge transfer is examined in light of the possible moderating effects of organizational culture, firm size, and the form and competitive regime of the alliance. Consistently, Learning intent (as a driver) and knowledge ambiguity (as an impediment) emerge as the most significant determinants of knowledge transfer. Moreover, the effects of partner protectiveness and LC on the Learning outcome are moderated by the firm's own culture towards Learning, the size of the firm, the structural form of the alliance, and the fact that partners may or may not be competitors.
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ambiguity and the process of knowledge transfer in strategic alliances
Strategic Management Journal, 1999Co-Authors: Bernard L SimoninAbstract:This research examines the role played by the ‘causally ambiguous’ nature of knowledge in the process of knowledge transfer between strategic alliance partners. Based on a cross-sectional sample of 147 multinationals and a structural equation methodology, this study empirically investigates the simultaneous effects of knowledge ambiguity and its antecedents—tacitness, asset specificity, prior experience, complexity, partner protectiveness, cultural distance, and organizational distance—on technological knowledge transfer. In contrast to past research that generally assumed a direct relation between these explanatory variables and transfer outcomes, this study’s findings highlight the critical role played by knowledge ambiguity as a full mediator of tacitness, prior experience, complexity, cultural distance, and organizational distance on knowledge transfer. These significant effects are further found to be moderated by the firm’s level of collaborative know-how, its Learning Capacity, and the duration of the alliance. Copyright © 1999 John Wiley & Sons, Ltd.
Toshitaka Nabeshima - One of the best experts on this subject based on the ideXlab platform.
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oral administration of propentofylline a stimulator of nerve growth factor ngf synthesis recovers cholinergic neuronal dysfunction induced by the infusion of anti ngf antibody into the rat septum
Behavioural Brain Research, 1997Co-Authors: Yoshiko Ogihara, Takaaki Hasegawa, Joji Onishi, Atsumi Nitta, Shoei Furukawa, Toshitaka NabeshimaAbstract:We have reported that the continuous infusion of anti-nerve growth factor (NGF) monoclonal antibody into the septum of rats produces an impairment of memory and a decrease in choline acetyltransferase (ChAT) and cholinesterase (ChE) activities in the hippocampus. Propentofylline, a xanthine derivative, has potent stimulatory effects on NGF synthesis/secretion in mouse astrocytes in vitro. To investigate the pharmacological effects of propentofylline in vivo, we induced amnesia in rats by infusing anti-NGF antibody into the septum for 16 days. One group of rats was given no further treatment, while the other group was treated with propentofylline orally once a day for 19 days, commencing 3 days before the implantation of the mini-osmotic pump, and continuing throughout the period during which the animals performed the behavioral tasks. In the treated amnesic rats, Learning and memory in the 3 tasks and ChAT and ChE activity were reduced compared to values in control rats. The administration of propentofylline recovered the decreased Learning Capacity and the deficit in cholinergic marker enzyme activity. These results suggest that the use of NGF stimulators may provide a new approach to the treatment of dementia.
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propentofylline prevents neuronal dysfunction induced by infusion of anti nerve growth factor antibody into the rat septum
European Journal of Pharmacology, 1996Co-Authors: Yoshiko Ogihara, Takaaki Hasegawa, Joji Onishi, Atsumi Nitta, Shoei Furukawa, Toshitaka NabeshimaAbstract:We have reported that the continuous infusion of anti-nerve growth factor (NGF) monoclonal antibody into the septum of rats produces neuronal dysfunction in the cholinergic system. Propentofylline has potent stimulatory effects on NGF synthesis/secretion in mouse astrocytes in vitro. To investigate the pharmacological effects of propentofylline, we used an animal model of dementia in which anti-NGF antibody was infused into the septum for 16 days via a mini-osmotic pump. The rats were treated with propentofylline orally once a day throughout the period during which performance in Learning and memory tasks was observed. In the vehicle-treated dementia rats, Learning and memory ability and choline acetyltransferase and cholinesterase activity were reduced compared to values in the control rats. The administration of propentofylline prevented the decreased Learning Capacity and the deficit in cholinergic marker enzyme activities. These results suggest that the use of NGF stimulators may provide a new approach to the treatment of dementia.
Jiangpeng Rong - One of the best experts on this subject based on the ideXlab platform.
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radial lens distortion correction using convolutional neural networks trained with synthesized images
Asian Conference on Computer Vision, 2016Co-Authors: Jiangpeng Rong, Shiyao Huang, Zeyu Shang, Xianghua YingAbstract:Radial lens distortion often exists in images taken by common cameras, which violates the assumption of pinhole camera model. Estimating the radial lens distortion of an image is an important preprocessing step for many vision applications. This paper intends to employ CNNs (Convolutional Neural Networks), to achieve radial distortion correction. However, the main issue hinder its progress is the scarcity of training data with radial distortion annotations. Inspired by the growing availability of image dataset with non-radial distortion, we propose a framework to address the issue by synthesizing images with radial distortion for CNNs. We believe that a large number of images of high variation of radial distortion is generated, which can be well exploited by deep CNN with a high Learning Capacity. We present quantitative results that demonstrate the ability of our technique to estimate the radial distortion with comparisons against several baseline methods, including an automatic method based on Hough transforms of distorted line images.