Toward an anisotropic atom-atom model for the crystalline phases of the molecular S8 compound

We analize two anisotropic atom-atom models used to describe the crystalline alpha,beta and gamma phases of S8 crystals, the most stable compound of elemental sulfur in solid phases, at ambient pressure and T<=400 K. The calculations are performed via a series of classical molecular dynamics (MD) simulations, with flexible molecular models and using a constant pressure-constant temperature algorithm for the numerical simulations. All intramolecular modes that mix with lattice modes, and are therefore relevant on the onset of structural phase transitions, are taken into account. Comparisons with experimental data and previous results obtained with an isotropic atom-atom molecular model are also performed.Comment: Major changes, new simulations and figures added, revtex4, to appear in J. Chem. Phy

Frizzled receptor 6 marks rare, highly tumourigenic stem-like cells in mouse and human neuroblastomas

Copyright © 2011 Cantilena et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. The article was made available through the Brunel Open Access Publishing Fund.Wnt signalling is an important component of vertebrate development, required for specification of the neural crest. Ten Wnt receptors [Frizzled receptor 1-10 (Fzd1-10)] have been identified so far, some of which are expressed in the developing nervous system and the neural crest. Here we show that expression of one such receptors, Fzd6, predicts poor survival in neuroblastoma patients and marks rare, HIF1/2 α-positive cells in tumour hypoxic areas. Fzd6 positive neuroblastoma cells form neurospheres with high efficiency, are resistant to doxorubicin killing and express high levels of mesenchymal markers such as Twist1 and Notch1. Expression of Fzd6 is required for the expression of genes of the noncanonical Wnt pathway and the spheres forming activity. When transplanted into immunodeficient mice, neuroblastoma cells expressing the Fzd6 marker grow more aggressively than their Fzd6 negative counterparts. We conclude that Fzd6 is a new surface marker of aggressive neuroblastoma cells with stem cell-like features.This work was sponsored by the Wellcome Trust, the RICC cancer fund, SPARKS, the Italian Association for Cancer Research, Regione Liguria and the Italian Ministry of Health

Cyclic motion and inversion of surface flow direction in a dense polymer brush under shear

Using molecular simulations, we study the properties of a polymer brush in contact with an explicit solvent under Couette and Poiseuille flow. The solvent is comprised of chemically identical chains. We present evidence that individual, unentangled chains in the dense brush exhibit cyclic, tumbling motion and non-Gaussian fluctuations of the molecular orientations similar to the behaviour of isolated tethered chains in shear flow. The collective molecular motion gives rise to an inversion of hydrodynamic flow direction in the vicinity of the brush-coated surface. Utilising Couette and Poiseuille flow, we investigate to what extend the effect of a brush-coated surface can be described by a Navier slip condition.Comment: 6 pages, 6 figures, submitted for publicatio

The association of adult lifecourse body mass index, waist circumference and dietary patterns with type 2 diabetes incidence in the MRC National Survey of Health and Development

Type 2 diabetes is a major public health problem and its prevalence is increasing worldwide, especially among older people. Overweight and abdominal obesity are known risk factors for the disease, but few studies have analysed their longitudinal pattern. A high glycaemic index (GI), low dietary fibre and high dietary fats have also been linked to type 2 diabetes, but their combined effect has never been studied. Using data from the MRC National Survey of Health and Development this thesis aimed to examine adult life course (from age 26 to 53 years) body mass index (BMI), waist circumference (WC) and dietary patterns in relation to type 2 diabetes incidence between age 53 and 60-64 years. At any stage of the adult life course BMI gain was associated with type 2 diabetes incidence. Early (26-36 years) and late (43-53 years) adulthood BMI gains were more important for men whereas late adulthood gains had stronger associations for women. The risk of type 2 diabetes increased with longer durations of overweight or obesity, probably because of the increasing accumulation of weight across the life course. Long-term WC change (36-53 years), independent of concomitant BMI change, was associated with increased risk of diabetes especially among women and people with an initially normal BMI. A high fat, high GI, low fibre dietary pattern was identified that was characterised by a high consumption of refined grains, processed meat, and animal fats, and a low intake of fruits, vegetables, low-fat dairy and wholegrain cereals. Higher scores for this dietary pattern at age 43 (only among women) and 53 were associated with increased type 2 diabetes incidence, predominantly via pathways that were independent of BMI and WC. Long-term score change (36-53 years) was significantly associated with diabetes only among women. Early interventions to reduce weight and WC gain and improve dietary patterns would be effective public health strategies to prevent type 2 diabetes risk at older ages

Analysis of nestin protein in the aqueous humor as biomarker of open angle glaucoma

Primary open angle glaucoma (POAG) is a progressive optic nerve degeneration, leading to irreversible visual damage. Alterations of the aqueous humor (AH), the biological fluid filling both the anterior and the posterior chambers of the eye, play a pathogenic role in POAG. AH protein composition is altered during glaucoma progression. Nestin protein was found to be differentially expressed in the AH of glaucomatous patients compared to unaffected matched controls

A high-fat, high-glycaemic index, low-fibre dietary pattern is prospectively associated with type 2 diabetes in a British birth cohort

The combined association of dietary fat, glycaemic index (GI) and fibre with type 2 diabetes has rarely been investigated. The objective was to examine the relationship between a high-fat, high-GI, low-fibre dietary pattern across adult life and type 2 diabetes risk using reduced rank regression. Data were from the MRC National Survey of Health and Development. Repeated measures of dietary intake estimated using 5-d diet diaries were available at the age of 36, 43 and 53 years for 1180 study members. Associations between dietary pattern scores at each age, as well as longitudinal changes in dietary pattern z-scores, and type 2 diabetes incidence (n 106) from 53 to 60-64 years were analysed. The high-fat, high-GI, low-fibre dietary pattern was characterised by low intakes of fruit, vegetables, low-fat dairy products and whole-grain cereals, and high intakes of white bread, fried potatoes, processed meat and animal fats. There was an increasing trend in OR for type 2 diabetes with increasing quintile of dietary pattern z-scores at the age of 43 years among women but not among men. Women in the highest z-score quintile at the age of 43 years had an OR for type 2 diabetes of 5·45 (95 % CI 2·01, 14·79). Long-term increases in this dietary pattern, independently of BMI and waist circumference, were also detrimental among women: for each 1 sd unit increase in dietary pattern z-score between 36 and 53 years, the OR for type 2 diabetes was 1·67 (95 % CI 1·20, 2·43) independently of changes in BMI and waist circumference in the same periods. A high-fat, high-GI, low-fibre dietary pattern was associated with increased type 2 diabetes risk in middle-aged British women but not in men

Static and dynamic properties of the interface between a polymer brush and a melt of identical chains

Molecular dynamics simulations of a short-chain polymer melt between two brush-covered surfaces under shear have been performed. The end-grafted polymers which constitute the brush have the same chemical properties as the free chains in the melt and provide a soft deformable substrate. Polymer chains are described by a coarse-grained bead-spring model with Lennard-Jones interactions between the beads and a FENE potential between nearest neighbors along the backbone of the chains. The grafting density of the brush layer offers a way of controlling the behavior of the surface without altering the molecular interactions. We perform equilibrium and non-equilibrium Molecular Dynamics simulations at constant temperature and volume using the Dissipative Particle Dynamics thermostat. The equilibrium density profiles and the behavior under shear are studied as well as the interdigitation of the melt into the brush, the orientation on different length scales (bond vectors, radius of gyration, and end-to-end vector) of free and grafted chains, and velocity profiles. The viscosity and slippage at the interface are calculated as functions of grafting density and shear velocity.Comment: 12 pages, submitted to J Chem Phy

Semantic Segmentation of Remote-Sensing Images Through Fully Convolutional Neural Networks and Hierarchical Probabilistic Graphical Models

Deep learning (DL) is currently the dominant approach to image classification and segmentation, but the performances of DL methods are remarkably influenced by the quantity and quality of the ground truth (GT) used for training. In this article, a DL method is presented to deal with the semantic segmentation of very-high-resolution (VHR) remote-sensing data in the case of scarce GT. The main idea is to combine a specific type of deep convolutional neural networks (CNNs), namely fully convolutional networks (FCNs), with probabilistic graphical models (PGMs). Our method takes advantage of the intrinsic multiscale behavior of FCNs to deal with multiscale data representations and to connect them to a hierarchical Markov model (e.g., making use of a quadtree). As a consequence, the spatial information present in the data is better exploited, allowing a reduced sensitivity to GT incompleteness to be obtained. The marginal posterior mode (MPM) criterion is used for inference in the proposed framework. To assess the capabilities of the proposed method, the experimental validation is conducted with the ISPRS 2D Semantic Labeling Challenge datasets on the cities of Vaihingen and Potsdam, with some modifications to simulate the spatially sparse GTs that are common in real remote-sensing applications. The results are quite significant, as the proposed approach exhibits a higher producer accuracy than the standard FCNs considered and especially mitigates the impact of scarce GTs on minority classes and small spatial details

Hierarchical Probabilistic Graphical Models and Deep Convolutional Neural Networks for Remote Sensing Image Classification

The method presented in this paper for semantic segmentation of multiresolution remote sensing images involves convolutional neural networks (CNNs), in particular fully convolutional networks (FCNs), and hierarchical probabilistic graphical models (PGMs). These approaches are combined to overcome the limitations in classification accuracy of CNNs for small or non-exhaustive ground truth (GT) datasets. Hierarchical PGMs, e.g., hierarchical Markov random fields (MRFs), are structured output learning models that exploit information contained at different image scales. This perfectly matches the intrinsically multiscale behavior of the processes of a CNN (e.g., pooling layers). The framework consists of a hierarchical MRF on a quadtree and a planar Markov model on each layer, modeling the interactions among pixels and accounting for both the multiscale and the spatial-contextual information. The marginal posterior mode criterion is used for inference. The adopted FCN is the U-Net and the experimental validation is conducted on the ISPRS 2D Semantic Labeling Challenge Vaihingen dataset, with some modifications to approach the case of scarce GTs and to assess the classification accuracy of the proposed technique. The proposed framework attains a higher recall compared to the considered FCNs, progressively more relevant as the training set is further from the ideal case of exhaustive GTs