Vector opinion dynamics in a model for social influence

We present numerical simulations of a model of social influence, where the opinion of each agent is represented by a binary vector. Agents adjust their opinions as a result of random encounters, whenever the difference between opinions is below a given threshold. Evolution leads to a steady state, which highly depends on the threshold and a convergence parameter of the model. We analyze the transition between clustered and homogeneous steady states. Results of the cases of complete mixing and small-world networks are compared.Comment: Latex file, 14 pages and 11 figures, Accepted in Physica

The dynamics of opinion in hierarchical organizations

We study the mutual influence of authority and persuasion in the flow of opinion. Many social organizations are characterized by a hierarchical structure where the propagation of opinion is asymmetric. In the normal flow of opinion formation a high-rank agent uses its authority (or its persuasion when necessary) to impose its opinion on others. However, agents with no authority may only use the force of its persuasion to propagate their opinions. In this contribution we describe a simple model with no social mobility, where each agent belongs to a class in the hierarchy and has also a persuasion capability. The model is studied numerically for a three levels case, and analytically within a mean field approximation, with a very good agreement between the two approaches. The stratum where the dominant opinion arises from is strongly dependent on the percentage of agents in each hierarchy level, and we obtain a phase diagram identifying the relative frequency of prevailing opinions. We also find that the time evolution of the conflicting opinions polarizes after a short transient.Comment: 6 pages, 5 figures, submitted to Phys. Rev.

Fully convolutional neural networks improve abdominal organ segmentation

Abdominal image segmentation is a challenging, yet important clinical problem. Variations in body size, position, and relative organ positions greatly complicate the segmentation process. Historically, multi-atlas methods have achieved leading results across imaging modalities and anatomical targets. However, deep learning is rapidly overtaking classical approaches for image segmentation. Recently, Zhou et al. showed that fully convolutional networks produce excellent results in abdominal organ segmentation of computed tomography (CT) scans. Yet, deep learning approaches have not been applied to whole abdomen magnetic resonance imaging (MRI) segmentation. Herein, we evaluate the applicability of an existing fully convolutional neural network (FCNN) designed for CT imaging to segment abdominal organs on T2 weighted (T2w) MRI's with two examples. In the primary example, we compare a classical multi-atlas approach with FCNN on forty-five T2w MRI's acquired from splenomegaly patients with five organs labeled (liver, spleen, left kidney, right kidney, and stomach). Thirty-six images were used for training while nine were used for testing. The FCNN resulted in a Dice similarity coefficient (DSC) of 0.930 in spleens, 0.730 in left kidneys, 0.780 in right kidneys, 0.913 in livers, and 0.556 in stomachs. The performance measures for livers, spleens, right kidneys, and stomachs were significantly better than multi-atlas (p < 0.05, Wilcoxon rank-sum test). In a secondary example, we compare the multi-atlas approach with FCNN on 138 distinct T2w MRI's with manually labeled pancreases (one label). On the pancreas dataset, the FCNN resulted in a median DSC of 0.691 in pancreases versus 0.287 for multi-atlas. The results are highly promising given relatively limited training data and without specific training of the FCNN model and illustrate the potential of deep learning approaches to transcend imaging modalities. 1 ?? COPYRIGHT SPIE. Downloading of the abstract is permitted for personal use only

Arachidonic acid, but not its metabolites, is essential for FcγR-stimulated intracellular killing of Staphylococcus aureus by human monocytes

Since arachidonic acid (AA) production by phospholipase A2 (PLA2) is essential for the Fcγ receptor (FcγR)-mediated respiratory burst and phagocytosis of opsonized erythrocytes by monocytes and macrophages, we focused in this study on the role of AA and its metabolites in the FcγR-stimulated intracellular killing of Staphylococcus aureus by human monocytes. The results revealed that the PLA2 inhibitors, but not inhibitors of cyclo-oxygenase and lipoxygenase, markedly suppressed the FcγR-mediated killing process. The production of O−2 by monocytes upon FcγR cross-linking was inhibited by 4-bromophenacyl bromide in a dose-dependent fashion, indicating that inhibition of PLA2 activity impairs the oxygen-dependent bactericidal mechanisms of monocytes, which could be partially restored by addition of exogenous AA and docosahexaenoic acid, but not myristic acid. These polyunsaturated fatty acids, but not myristic acid, stimulated the intracellular killing of S. aureus by monocytes, although not as effectively as FcγR cross-linking. Furthermore, FcγR cross-linking stimulated the release of AA from monocytes. Studies with selective inhibitors revealed that the FcγR-mediated activation of PLA2 is dependent on Ca2+ and tyrosine kinase activity. Together these results indicate a key role for PLA2/AA, but not its major metabolites, in mediating the FcγR-stimulated intracellular killing of S. aureus by monocytes