Preventing E-cadherin aberrant N-glycosylation at Asn-554 improves its critical function in gastric cancer.

E-cadherin is a central molecule in the process of gastric carcinogenesis and its posttranslational modifications by N-glycosylation have been described to induce a deleterious effect on cell adhesion associated with tumor cell invasion. However, the role that site-specific glycosylation of E-cadherin has in its defective function in gastric cancer cells needs to be determined. Using transgenic mice models and human clinical samples, we demonstrated that N-acetylglucosaminyltransferase V (GnT-V)-mediated glycosylation causes an abnormal pattern of E-cadherin expression in the gastric mucosa. In vitro models further indicated that, among the four potential N-glycosylation sites of E-cadherin, Asn-554 is the key site that is selectively modified with ß1,6 GlcNAc-branched N-glycans catalyzed by GnT-V. This aberrant glycan modification on this specific asparagine site of E-cadherin was demonstrated to affect its critical functions in gastric cancer cells by affecting E-cadherin cellular localization, cis-dimer formation, molecular assembly and stability of the adherens junctions and cell-cell aggregation, which was further observed in human gastric carcinomas. Interestingly, manipulating this site-specific glycosylation, by preventing Asn-554 from receiving the deleterious branched structures, either by a mutation or by silencing GnT-V, resulted in a protective effect on E-cadherin, precluding its functional dysregulation and contributing to tumor suppression.

An actigraphy heterogeneous mixture model for sleep assessment.

Wrist actigraphy is a well established procedure to monitor human activity. Among other areas, it has a special relevance in sleep studies where its lightweight and non-intrusive nature make it a valuable tool to access the circadian cycle. While there are several methods to extract information from the data, the differentiation between sleep and wakefulness states is still an open discussion. In this paper, the characteristics of the movements in the different states are assumed to be intrinsically different. These differences are not simply related with magnitude and movement counting, but due to real differences on the statistical distributions describing the actigraphy data. Thus it is possible to refine the discrimination level when detecting these states. The proposed methodology to characterize the actigraphy data is based on a mixture of three canonical distributions; i)Exponential, ii)Rayleigh and iii)Gaussian. It is shown that the weights and parameters estimated in each state are organized into almost separable clusters on the feature space. This suggests the ability of the method to discriminate these states based only on the movements recorded on actigraphy data.

Carotid plaque 3D compound imaging and echo-morphology analysis: a Bayesian approach.

This paper describes a method for volume reconstruction of the carotid plaque and presents a novel local characterization of its echo-morphology. The data is composed by a series of nearly parallel ultrasound images (3D Compound Imaging) and the acquisition is performed using traditional non-invasive ultrasound equipment available in most medical facilities, without need of a spatial locator device. The reconstruction algorithm uses the observed pixels inside the plaque, which were obtained in a pre-segmentation stage performed under medical guidance [1]. The paper proposes a Bayesian algorithm which estimates the underlying volume inside the plaque, by filtering and interpolating the data in order to remove speckle noise and fill non-observed regions, respectively. This volume is further used in plaque echo-morphology analysis. The observation model is based on the Rayleigh distribution, commonly used to model speckle noise in ultrasound images. A prior model based on the edge preserving Total Variation Gibbs distribution is also used to fill the gaps on non-evenly spaced observations. An energy function is derived from these models and an iterative algorithm computes its minimizer. The estimated function, defined in a given volume of interest, is used in global and local plaque characterization, namely to estimate its average levels of stenosis, echo-morphology and to identify vulnerable foci inside the plaque. The goal is to make atherosclerosis diagnosis more accurate and complete than using traditional 2D ultrasound analysis.