The Experts below are selected from a list of 315 Experts worldwide ranked by ideXlab platform
Richard L Lieber - One of the best experts on this subject based on the ideXlab platform.
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pregnancy induced adaptations in the Intrinsic Structure of rat pelvic floor muscles
American Journal of Obstetrics and Gynecology, 2015Co-Authors: Marianna Alperin, Danielle M Lawley, Mary C Esparza, Richard L LieberAbstract:Objective Maternal birth trauma to the pelvic floor muscles (PFMs) is a major risk factor for pelvic floor disorders. Modeling and imaging studies suggest that demands placed on PFMs during childbirth exceed their physiologic limits; however many parous women do not sustain PFM injury. Here we determine whether pregnancy induces adaptations in PFM architecture, the strongest predictor of muscle function, and/or intramuscular extracellular matrix (ECM), responsible for load bearing. To establish if parallel changes occur in muscles outside of the PFM, we also examined a hind limb muscle. Study Design Coccygeus, iliocaudalis, pubocaudalis, and tibialis anterior of 3-month-old Sprague-Dawley virgin, mid-pregnant, and late-pregnant; 6-month-old virgin; and 4- and 12-week postpartum rats (N = 10/group) were fixed in situ and harvested. Major architectural parameters determining muscle's excursion and force-generating capacity were quantified, namely, normalized fiber length (L fn ), physiologic cross-sectional area, and sarcomere length. Hydroxyproline content was used as a surrogate for intramuscular ECM quantity. Analyses were performed by 2-way analysis of variance with Tukey post hoc testing at a significance level of .05. Results Pregnancy induced a significant increase in L fn in all PFMs by the end of gestation relative to virgin controls. Fibers were elongated by 37% in coccygeus ( P P fn change was observed in the tibialis anterior. Physiologic cross-sectional area and sarcomere length were not affected by pregnancy. By 12 weeks' postpartum, L fn of all PFMs returned to the prepregnancy values. Relative to virgin controls, ECM increased by 140% in coccygeus, 52% in iliocaudalis, and 75% in pubocaudalis in late-pregnant group, but remained unchanged across time in the tibialis anterior. Postpartum, ECM collagen content returned to prepregnancy levels in iliocaudalis and pubocaudalis, but continued to be significantly elevated in coccygeus ( P Conclusion This study demonstrates that pregnancy induces unique adaptations in the Structure of the PFMs, which adjust their architectural design by adding sarcomeres in series to increase fiber length as well as mounting a substantial synthesis of collagen in intramuscular ECM.
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Pregnancy-induced adaptations in the Intrinsic Structure of rat pelvic floor muscles.
American journal of obstetrics and gynecology, 2015Co-Authors: Marianna Alperin, Danielle M Lawley, Mary C Esparza, Richard L LieberAbstract:Maternal birth trauma to the pelvic floor muscles (PFMs) is a major risk factor for pelvic floor disorders. Modeling and imaging studies suggest that demands placed on PFMs during childbirth exceed their physiologic limits; however many parous women do not sustain PFM injury. Here we determine whether pregnancy induces adaptations in PFM architecture, the strongest predictor of muscle function, and/or intramuscular extracellular matrix (ECM), responsible for load bearing. To establish if parallel changes occur in muscles outside of the PFM, we also examined a hind limb muscle. Coccygeus, iliocaudalis, pubocaudalis, and tibialis anterior of 3-month-old Sprague-Dawley virgin, mid-pregnant, and late-pregnant; 6-month-old virgin; and 4- and 12-week postpartum rats (N = 10/group) were fixed in situ and harvested. Major architectural parameters determining muscle's excursion and force-generating capacity were quantified, namely, normalized fiber length (Lfn), physiologic cross-sectional area, and sarcomere length. Hydroxyproline content was used as a surrogate for intramuscular ECM quantity. Analyses were performed by 2-way analysis of variance with Tukey post hoc testing at a significance level of .05. Pregnancy induced a significant increase in Lfn in all PFMs by the end of gestation relative to virgin controls. Fibers were elongated by 37% in coccygeus (P < .0001), and by 21% in iliocaudalis and pubocaudalis (P < .0001). Importantly, no Lfn change was observed in the tibialis anterior. Physiologic cross-sectional area and sarcomere length were not affected by pregnancy. By 12 weeks' postpartum, Lfn of all PFMs returned to the prepregnancy values. Relative to virgin controls, ECM increased by 140% in coccygeus, 52% in iliocaudalis, and 75% in pubocaudalis in late-pregnant group, but remained unchanged across time in the tibialis anterior. Postpartum, ECM collagen content returned to prepregnancy levels in iliocaudalis and pubocaudalis, but continued to be significantly elevated in coccygeus (P < .0001). This study demonstrates that pregnancy induces unique adaptations in the Structure of the PFMs, which adjust their architectural design by adding sarcomeres in series to increase fiber length as well as mounting a substantial synthesis of collagen in intramuscular ECM. Published by Elsevier Inc.
Feng Guan - One of the best experts on this subject based on the ideXlab platform.
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Neighborhood linear embedding for Intrinsic Structure discovery
Machine Vision and Applications, 2008Co-Authors: Shuzhi Sam Ge, Feng GuanAbstract:In this paper, an unsupervised learning algorithm, neighborhood linear embedding (NLE), is proposed to discover the Intrinsic Structures such as neighborhood relationships, global distributions and clustering property of a given set of input data. This algorithm eases the process of Intrinsic Structure discovery by avoiding the trial and error operations for neighbor selection, and at the same time, allows the discovery to adapt to the characteristics of the input data. In addition, it is able to explore different Intrinsic Structures of data simultaneously, and the discovered Structures can be used to compute manipulative embeddings for potential data classification and recognition applications. Experiments for image object segmentation are carried out to demonstrate some potential applications of the NLE algorithm.
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Feature representation based on Intrinsic Structure discovery in high dimensional space
Proceedings 2006 IEEE International Conference on Robotics and Automation 2006. ICRA 2006., 2006Co-Authors: Shuzhi Sam Ge, Feng GuanAbstract:In this paper, an image is regarded as a collection of image patches that can be referred to as points with certain Intrinsic Structures/patterns in high-dimensional space. These Structures contain vital information of image features and thus provide a novel method for image feature representation. To discover these Intrinsic Structures, we first propose neighborhood linear embedding (NLE), an unsupervised learning algorithm, to discover neighborhood relationship and global distribution of input data simultaneously. Secondly, NLE is extended to discover the clustering Structure of data by incorporating with a Euclidean distance histogram and a series of band pass filters. Finally, by combining with a dimensionality reduction technique, the discovered Intrinsic Structures are visualized and manipulated in low-dimensional space in the format known as embeddings. The proposed NLE allows the discovery process to adapt to the characteristics of input data. In addition, it is revealed that an image feature composed of image patches can be tracked by tracking the contour containing embeddings of the corresponding image patches
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ICRA - Feature representation based on Intrinsic Structure discovery in high dimensional space
Proceedings 2006 IEEE International Conference on Robotics and Automation 2006. ICRA 2006., 2006Co-Authors: Shuzhi Sam Ge, Feng GuanAbstract:In this paper, an image is regarded as a collection of image patches that can be referred to as points with certain Intrinsic Structures/patterns in high-dimensional space. These Structures contain vital information of image features and thus provide a novel method for image feature representation. To discover these Intrinsic Structures, we first propose neighborhood linear embedding (NLE), an unsupervised learning algorithm, to discover neighborhood relationship and global distribution of input data simultaneously. Secondly, NLE is extended to discover the clustering Structure of data by incorporating with a Euclidean distance histogram and a series of band pass filters. Finally, by combining with a dimensionality reduction technique, the discovered Intrinsic Structures are visualized and manipulated in low-dimensional space in the format known as embeddings. The proposed NLE allows the discovery process to adapt to the characteristics of input data. In addition, it is revealed that an image feature composed of image patches can be tracked by tracking the contour containing embeddings of the corresponding image patches
Haizhou Ai - One of the best experts on this subject based on the ideXlab platform.
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Learning object Intrinsic Structure for robust visual tracking
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003. Proceedings., 2003Co-Authors: Qiang Wang, Guangyou Xu, Haizhou AiAbstract:In this paper, a novel method to learn the Intrinsic object Structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object Intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this Intrinsic representation. Thirdly the learned Intrinsic object Structure is integrated into a particle-filter style tracker. We will show that this Intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.
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CVPR (2) - Learning object Intrinsic Structure for robust visual tracking
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003. Proceedings., 2003Co-Authors: Qiang Wang, Guangyou Xu, Haizhou AiAbstract:In this paper, a novel method to learn the Intrinsic object Structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object Intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this Intrinsic representation. Thirdly the learned Intrinsic object Structure is integrated into a particle-filter style tracker. We will show that this Intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.
Shuzhi Sam Ge - One of the best experts on this subject based on the ideXlab platform.
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Neighborhood linear embedding for Intrinsic Structure discovery
Machine Vision and Applications, 2008Co-Authors: Shuzhi Sam Ge, Feng GuanAbstract:In this paper, an unsupervised learning algorithm, neighborhood linear embedding (NLE), is proposed to discover the Intrinsic Structures such as neighborhood relationships, global distributions and clustering property of a given set of input data. This algorithm eases the process of Intrinsic Structure discovery by avoiding the trial and error operations for neighbor selection, and at the same time, allows the discovery to adapt to the characteristics of the input data. In addition, it is able to explore different Intrinsic Structures of data simultaneously, and the discovered Structures can be used to compute manipulative embeddings for potential data classification and recognition applications. Experiments for image object segmentation are carried out to demonstrate some potential applications of the NLE algorithm.
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Feature representation based on Intrinsic Structure discovery in high dimensional space
Proceedings 2006 IEEE International Conference on Robotics and Automation 2006. ICRA 2006., 2006Co-Authors: Shuzhi Sam Ge, Feng GuanAbstract:In this paper, an image is regarded as a collection of image patches that can be referred to as points with certain Intrinsic Structures/patterns in high-dimensional space. These Structures contain vital information of image features and thus provide a novel method for image feature representation. To discover these Intrinsic Structures, we first propose neighborhood linear embedding (NLE), an unsupervised learning algorithm, to discover neighborhood relationship and global distribution of input data simultaneously. Secondly, NLE is extended to discover the clustering Structure of data by incorporating with a Euclidean distance histogram and a series of band pass filters. Finally, by combining with a dimensionality reduction technique, the discovered Intrinsic Structures are visualized and manipulated in low-dimensional space in the format known as embeddings. The proposed NLE allows the discovery process to adapt to the characteristics of input data. In addition, it is revealed that an image feature composed of image patches can be tracked by tracking the contour containing embeddings of the corresponding image patches
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ICRA - Feature representation based on Intrinsic Structure discovery in high dimensional space
Proceedings 2006 IEEE International Conference on Robotics and Automation 2006. ICRA 2006., 2006Co-Authors: Shuzhi Sam Ge, Feng GuanAbstract:In this paper, an image is regarded as a collection of image patches that can be referred to as points with certain Intrinsic Structures/patterns in high-dimensional space. These Structures contain vital information of image features and thus provide a novel method for image feature representation. To discover these Intrinsic Structures, we first propose neighborhood linear embedding (NLE), an unsupervised learning algorithm, to discover neighborhood relationship and global distribution of input data simultaneously. Secondly, NLE is extended to discover the clustering Structure of data by incorporating with a Euclidean distance histogram and a series of band pass filters. Finally, by combining with a dimensionality reduction technique, the discovered Intrinsic Structures are visualized and manipulated in low-dimensional space in the format known as embeddings. The proposed NLE allows the discovery process to adapt to the characteristics of input data. In addition, it is revealed that an image feature composed of image patches can be tracked by tracking the contour containing embeddings of the corresponding image patches
Qiang Wang - One of the best experts on this subject based on the ideXlab platform.
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Learning object Intrinsic Structure for robust visual tracking
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003. Proceedings., 2003Co-Authors: Qiang Wang, Guangyou Xu, Haizhou AiAbstract:In this paper, a novel method to learn the Intrinsic object Structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object Intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this Intrinsic representation. Thirdly the learned Intrinsic object Structure is integrated into a particle-filter style tracker. We will show that this Intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.
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CVPR (2) - Learning object Intrinsic Structure for robust visual tracking
2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition 2003. Proceedings., 2003Co-Authors: Qiang Wang, Guangyou Xu, Haizhou AiAbstract:In this paper, a novel method to learn the Intrinsic object Structure for robust visual tracking is proposed. The basic assumption is that the parameterized object state lies on a low dimensional manifold and can be learned from training data. Based on this assumption, firstly we derived the dimensionality reduction and density estimation algorithm for unsupervised learning of object Intrinsic representation, the obtained non-rigid part of object state reduces even to 2 dimensions. Secondly the dynamical model is derived and trained based on this Intrinsic representation. Thirdly the learned Intrinsic object Structure is integrated into a particle-filter style tracker. We will show that this Intrinsic object representation has some interesting properties and based on which the newly derived dynamical model makes particle-filter style tracker more robust and reliable. Experiments show that the learned tracker performs much better than existing trackers on the tracking of complex non-rigid motions such as fish twisting with self-occlusion and large inter-frame lip motion. The proposed method also has the potential to solve other type of tracking problems.
