MUC1 O-glycosylation contributes to anoikis resistance in epithelial cancer cells

Anoikis is a fundamental cellular process for maintaining tissue homeostasis. Resistance to anoikis is a hallmark of oncogenic epithelial–mesenchymal transition and is a pre-requisite for metastasis. Previous studies have revealed that the heavily glycosylated mucin protein MUC1, which is overexpressed in all types of epithelial cancer cells, prevents anoikis initiation in response to loss of adhesion. This effect of MUC1 is largely attributed to its extracellular domain that provides cell surface anoikis-initiating molecules with a ‘homing’ microenvironment. The present study investigated the influence of O-glycosylation on MUC1 extracellular domain on MUC1-mediated cell resistance to anoikis. It shows that stable suppression of the Core 1Gal-transferase (C1GT) by shRNA substantially reduces O-glycosylation in MUC1-positively transfected human colon cancer HCT116 cells and in high MUC1-expressing SW620 cells. Suppression of C1GT significantly increased anoikis of the MUC1-positive, but not MUC1-negative, cells in response to suspended culture. This effect was shown to be associated with increased ligand accessibility to cell surface anoikis-initiating molecules such as E-cadherin, integrinβ1 and Fas. These results indicate that the extensive O-glycosylation on MUC1 extracellular domain contributes to MUC1-mediated cell resistance to anoikis by facilitating MUC1-mediated prohibition of activation of the cell surface anoikis-initiating molecules in response to loss of cell adhesion. This provides insight into the molecular mechanism of anoikis regulation and highlights the importance of cellular glycosylation in cancer progression and metastasis

Multi-view Regularized Gaussian Processes

Gaussian processes (GPs) have been proven to be powerful tools in various areas of machine learning. However, there are very few applications of GPs in the scenario of multi-view learning. In this paper, we present a new GP model for multi-view learning. Unlike existing methods, it combines multiple views by regularizing marginal likelihood with the consistency among the posterior distributions of latent functions from different views. Moreover, we give a general point selection scheme for multi-view learning and improve the proposed model by this criterion. Experimental results on multiple real world data sets have verified the effectiveness of the proposed model and witnessed the performance improvement through employing this novel point selection scheme

Dual Conformal Properties of Six-Dimensional Maximal Super Yang-Mills Amplitudes

We demonstrate that the tree-level amplitudes of maximal super-Yang-Mills theory in six dimensions, when stripped of their overall momentum and supermomentum delta functions, are covariant with respect to the six-dimensional dual conformal group. Using the generalized unitarity method, we demonstrate that this property is also present for loop amplitudes. Since the six-dimensional amplitudes can be interpreted as massive four-dimensional ones, this implies that the six-dimensional symmetry is also present in the massively regulated four-dimensional maximal super-Yang-Mills amplitudes.Comment: 20 pages, 3 figures, minor clarification, references update

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually annotated images to capture the anatomical variability in a cohort. In contrast, we develop a segmentation model that recursively evolves a segmentation in several steps, and implement it as a recurrent neural network. We learn model parameters by optimizing the interme- diate steps of the evolution in addition to the final segmentation. To this end, we train our segmentation propagation model by presenting incom- plete and/or inaccurate input segmentations paired with a recommended next step. Our work aims to alleviate challenges in segmenting heart structures from cardiac MRI for patients with congenital heart disease (CHD), which encompasses a range of morphological deformations and topological changes. We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels. Com- pared to direct segmentation, the iterative method yields more accurate segmentation for patients with the most severe CHD malformations.Comment: Presented at the Deep Learning in Medical Image Analysis Workshop, MICCAI 201

Non-modal stability analysis of low-Re separated flow around a NACA 4415 airfoil in ground effect

© 2019 Elsevier Masson SAS In this numerical–theoretical study, we perform a linear non-modal stability analysis of the separated flow around a NACA 4415 airfoil over a no-slip ground at low Reynolds numbers (300⩽Re⩽500) and high angles of attack (12∘⩽α⩽20∘). We find that: (i) the strength of the recirculation zone behind the airfoil is a key parameter controlling the absolute/convective nature of the instability in the boundary layer downstream; (ii) when Re, α or the ground clearance increases, the energy gain also increases, with the optimal perturbations switching from being three dimensional to two dimensional; and (iii) classical hairpin vortices, or Klebanoff modes, can be produced by three-dimensional optimal perturbations on a two-dimensional steady base flow containing a laminar separation bubble. Knowledge of the spatiotemporal features of the optimal mode could aid the design of advanced strategies for flow control. This study offers new insight into the transient growth behavior of airfoil–ground flow systems at low Re and high α, contributing to a better understanding of the ground-effect aerodynamics of small insects and micro aerial vehicles

Reduced elastogenesis: a clue to the arteriosclerosis and emphysematous changes in Schimke immuno-osseous dysplasia?

BACKGROUND: Arteriosclerosis and emphysema develop in individuals with Schimke immuno-osseous dysplasia (SIOD), a multisystem disorder caused by biallelic mutations in SMARCAL1 (SWI/SNF-related, matrix-associated, actin-dependent regulator of chromatin, subfamily a-like 1). However, the mechanism by which the vascular and pulmonary disease arises in SIOD remains unknown. METHODS: We reviewed the records of 65 patients with SMARCAL1 mutations. Molecular and immunohistochemical analyses were conducted on autopsy tissue from 4 SIOD patients. RESULTS: Thirty-two of 63 patients had signs of arteriosclerosis and 3 of 51 had signs of emphysema. The arteriosclerosis was characterized by intimal and medial hyperplasia, smooth muscle cell hyperplasia and fragmented and disorganized elastin fibers, and the pulmonary disease was characterized by panlobular enlargement of air spaces. Consistent with a cell autonomous disorder, SMARCAL1 was expressed in arterial and lung tissue, and both the aorta and lung of SIOD patients had reduced expression of elastin and alterations in the expression of regulators of elastin gene expression. CONCLUSIONS: This first comprehensive study of the vascular and pulmonary complications of SIOD shows that these commonly cause morbidity and mortality and might arise from impaired elastogenesis. Additionally, the effect of SMARCAL1 deficiency on elastin expression provides a model for understanding other features of SIOD