Cellular localization and associations of the major lipolytic proteins in human skeletal muscle at rest and during exercise

Lipolysis involves the sequential breakdown of fatty acids from triacylglycerol and is increased during energy stress such as exercise. Adipose triglyceride lipase (ATGL) is a key regulator of skeletal muscle lipolysis and perilipin (PLIN) 5 is postulated to be an important regulator of ATGL action of muscle lipolysis. Hence, we hypothesized that non-genomic regulation such as cellular localization and the interaction of these key proteins modulate muscle lipolysis during exercise. PLIN5, ATGL and CGI-58 were highly (>60%) colocated with Oil Red O (ORO) stained lipid droplets. PLIN5 was significantly colocated with ATGL, mitochondria and CGI-58, indicating a close association between the key lipolytic effectors in resting skeletal muscle. The colocation of the lipolytic proteins, their independent association with ORO and the PLIN5/ORO colocation were not altered after 60 min of moderate intensity exercise. Further experiments in cultured human myocytes showed that PLIN5 colocation with ORO or mitochondria is unaffected by pharmacological activation of lipolytic pathways. Together, these data suggest that the major lipolytic proteins are highly expressed at the lipid droplet and colocate in resting skeletal muscle, that their localization and interactions appear to remain unchanged during prolonged exercise, and, accordingly, that other post-translational mechanisms are likely regulators of skeletal muscle lipolysis.

3D Intervertebral disc localization and segmentation from MR images by data-driven regression and classification

In this paper we propose a new fully-automatic method for localizing and segmenting 3D intervertebral discs from MR images, where the two problems are solved in a unified data-driven regression and classification framework. We estimate the output (image displacements for localization, or fg/bg labels for segmentation) of image points by exploiting both training data and geometric constraints simultaneously. The problem is formulated in a unified objective function which is then solved globally and efficiently. We validate our method on MR images of 25 patients. Taking manually labeled data as the ground truth, our method achieves a mean localization error of 1.3 mm, a mean Dice metric of 87%, and a mean surface distance of 1.3 mm. Our method can be applied to other localization and segmentation tasks.

Enterprise localization practices and their implication for knowledge management in foreign participation in the Chinese construction sector

Foreign firms active in the Chinese construction industry find themselves in a competitive environment unlike the environments in which they operate back home. Adaptations are therefore required in order that firms retain competitive leverage while integrating into the circumstances of the Chinese market. Since these firms primarily compete on superior knowledge capabilities it is instructive to understand how adaptations both serve to transfer propriety capabilities to China while adapting to the knowledge characteristics of the Chinese market into which those capabilities are transplanted. Five localizing parameters across which adaptations take place are identified in this paper and rates of localization are tabulated for 60 foreign firms active in the Chinese construction industry. Revealed localizing behaviors are then analyzed in the light of existing literature for explanatory power. It is concluded that in order to be strategically competitive firms must not only retain knowledge differentials, but that the knowledge must be coded and disseminated in a manner suited to the environment in which it is to be utilized.

The effect of deformation twinning on stress localization in a three dimensional TWIP steel microstructure

We present an investigation of the effect of deformation twinning on the visco-plastic response and stress localization in a low stacking fault energy twinning-induced plasticity (TWIP) steel under uniaxial tension loading. The three-dimensional full field response was simulated using the fast Fourier transform method. The initial microstructure was obtained from a three dimensional serial sectionusing electron backscatter diffraction. Twin volume fraction evolution upon strain was measured so the hardening parameters of the simple Voce model could be identified to fit both the stress-strain behavior and twinning activity. General trends of texture evolution were acceptably predicted including the typical sharpening and balance between the 1 1 1 fiber and the 1 0 0 fiber. Twinning was found to nucleate preferentially at grain boundaries although the predominant twin reorientation scheme did not allow spatial propagation to be captured. Hot spots in stress correlated with the boundaries of twinned voxel domains, which either impeded or enhanced twinning based on which deformation modes were active locally.

Localization and degmentation of 3D intervertebral discs in MR Images by data driven estimation

This paper addresses the problem of fully-automatic localization and segmentation of 3D intervertebral discs (IVDs) from MR images. Our method contains two steps, where we first localize the center of each IVD, and then segment IVDs by classifying image pixels around each disc center as foreground (disc) or background. The disc localization is done by estimating the image displacements from a set of randomly sampled 3D image patches to the disc center. The image displacements are estimated by jointly optimizing the training and test displacement values in a data-driven way, where we take into consideration both the training data and the geometric constraint on the test image. After the disc centers are localized, we segment the discs by classifying image pixels around disc centers as background or foreground. The classification is done in a similar data-driven approach as we used for localization, but in this segmentation case we are aiming to estimate the foreground/background probability of each pixel instead of the image displacements. In addition, an extra neighborhood smooth constraint is introduced to enforce the local smoothness of the label field. Our method is validated on 3D T2-weighted turbo spin echo MR images of 35 patients from two different studies. Experiments show that compared to state of the art, our method achieves better or comparable results. Specifically, we achieve for localization a mean error of 1.6-2.0 mm, and for segmentation a mean Dice metric of 85%-88% and a mean surface distance of 1.3-1.4 mm.

Fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images via a learning-based method

In this paper, we address the problems of fully automatic localization and segmentation of 3D vertebral bodies from CT/MR images. We propose a learning-based, unified random forest regression and classification framework to tackle these two problems. More specifically, in the first stage, the localization of 3D vertebral bodies is solved with random forest regression where we aggregate the votes from a set of randomly sampled image patches to get a probability map of the center of a target vertebral body in a given image. The resultant probability map is then further regularized by Hidden Markov Model (HMM) to eliminate potential ambiguity caused by the neighboring vertebral bodies. The output from the first stage allows us to define a region of interest (ROI) for the segmentation step, where we use random forest classification to estimate the likelihood of a voxel in the ROI being foreground or background. The estimated likelihood is combined with the prior probability, which is learned from a set of training data, to get the posterior probability of the voxel. The segmentation of the target vertebral body is then done by a binary thresholding of the estimated probability. We evaluated the present approach on two openly available datasets: 1) 3D T2-weighted spine MR images from 23 patients and 2) 3D spine CT images from 10 patients. Taking manual segmentation as the ground truth (each MR image contains at least 7 vertebral bodies from T11 to L5 and each CT image contains 5 vertebral bodies from L1 to L5), we evaluated the present approach with leave-one-out experiments. Specifically, for the T2-weighted MR images, we achieved for localization a mean error of 1.6 mm, and for segmentation a mean Dice metric of 88.7% and a mean surface distance of 1.5 mm, respectively. For the CT images we achieved for localization a mean error of 1.9 mm, and for segmentation a mean Dice metric of 91.0% and a mean surface distance of 0.9 mm, respectively.