Bailout Embeddings, Targeting of KAM Orbits, and the Control of Hamiltonian Chaos

We present a novel technique, which we term bailout embedding, that can be used to target orbits having particular properties out of all orbits in a flow or map. We explicitly construct a bailout embedding for Hamiltonian systems so as to target KAM orbits. We show how the bailout dynamics is able to lock onto extremely small KAM islands in an ergodic sea.Comment: 3 figures, 9 subpanel

Stability of additive-free water-in-oil emulsions

We calculate ion distributions near a planar oil-water interface within non-linear Poisson-Boltzmann theory, taking into account the Born self-energy of the ions in the two media. For unequal self-energies of cations and anions, a spontaneous charge separation is found such that the water and oil phase become oppositely charged, in slabs with a typical thickness of the Debye screening length in the two media. From the analytical solutions, the corresponding interfacial charge density and the contribution to the interfacial tension is derived, together with an estimate for the Yukawa-potential between two spherical water droplets in oil. The parameter regime is explored where the plasma coupling parameter exceeds the crystallization threshold, i.e. where the droplets are expected to form crystalline structures due to a strong Yukawa repulsion, as recently observed experimentally. Extensions of the theory that we discuss briefly include numerical calculations on spherical water droplets in oil, and analytical calculations of the linear PB-equation for a finite oil-water interfacial width.Comment: 9 pages, 4 figures, accepted by JPCM for proceedings of LMC

Automated Retinopathy of Prematurity Case Detection with Convolutional Neural Networks

Retinopathy of Prematurity (ROP) is an ocular disease observed in premature babies, considered one of the largest preventable causes of childhood blindness. Problematically, the visual indicators of ROP are not well understood and neonatal fundus images are usually of poor quality and resolution. We investigate two ways to aid clinicians in ROP detection using convolutional neural networks (CNN): (1) We fine-tune a pretrained GoogLeNet as a ROP detector and with small modifications also return an approximate Bayesian posterior over disease presence. To the best of our knowledge, this is the first completely automated ROP detection system. (2) To further aid grading, we train a second CNN to return novel feature map visualizations of pathologies, learned directly from the data. These feature maps highlight discriminative information, which we believe may be used by clinicians with our classifier to aid in screening

Feature-based classifiers for somatic mutation detection in tumour–normal paired sequencing data

Motivation: The study of cancer genomes now routinely involves using next-generation sequencing technology (NGS) to profile tumours for single nucleotide variant (SNV) somatic mutations. However, surprisingly few published bioinformatics methods exist for the specific purpose of identifying somatic mutations from NGS data and existing tools are often inaccurate, yielding intolerably high false prediction rates. As such, the computational problem of accurately inferring somatic mutations from paired tumour/normal NGS data remains an unsolved challenge

Integration of the β-Catenin-Dependent Wnt Pathway with Integrin Signaling through the Adaptor Molecule Grb2

THE COMPLEXITY OF WNT SIGNALING LIKELY STEMS FROM TWO SOURCES: multiple pathways emanating from frizzled receptors in response to wnt binding, and modulation of those pathways and target gene responsiveness by context-dependent signals downstream of growth factor and matrix receptors. Both rac1 and c-jun have recently been implicated in wnt signaling, however their upstream activators have not been identified.Here we identify the adapter protein Grb2, which is itself an integrator of multiple signaling pathways, as a modifier of beta-catenin-dependent wnt signaling. Grb2 synergizes with wnt3A, constitutively active (CA) LRP6, Dvl2 or CA-beta-catenin to drive a LEF/TCF-responsive reporter, and dominant negative (DN) Grb2 or siRNA to Grb2 block wnt3A-mediated reporter activity. MMP9 is a target of beta-catenin-dependent wnt signaling, and an MMP9 promoter reporter is also responsive to signals downstream of Grb2. Both a jnk inhibitor and DN-c-jun block transcriptional activation downstream of Dvl2 and Grb2, as does DN-rac1. Integrin ligation by collagen also synergizes with wnt signaling as does overexpression of Focal Adhesion Kinase (FAK), and this is blocked by DN-Grb2.These data suggest that integrin ligation and FAK activation synergize with wnt signaling through a Grb2-rac-jnk-c-jun pathway, providing a context-dependent mechanism for modulation of wnt signaling

Integrin α3β1–dependent β-catenin phosphorylation links epithelial Smad signaling to cell contacts

Injury-initiated epithelial to mesenchymal transition (EMT) depends on contextual signals from the extracellular matrix, suggesting a role for integrin signaling. Primary epithelial cells deficient in their prominent laminin receptor, α3β1, were found to have a markedly blunted EMT response to TGF-β1. A mechanism for this defect was explored in α3-null cells reconstituted with wild-type (wt) α3 or point mutants unable to engage laminin 5 (G163A) or epithelial cadherin (E-cadherin; H245A). After TGF-β1 stimulation, wt epithelial cells but not cells expressing the H245A mutant internalize complexes of E-cadherin and TGF-β1 receptors, generate phospho-Smad2 (p-Smad2)–pY654–β-catenin complexes, and up-regulate mesenchymal target genes. Although Smad2 phosphorylation is normal, p-Smad2–pY654–β-catenin complexes do not form in the absence of α3 or when α3β1 is mainly engaged on laminin 5 or E-cadherin in adherens junctions, leading to attenuated EMT. These findings demonstrate that α3β1 coordinates cross talk between β-catenin and Smad signaling pathways as a function of extracellular contact cues and thereby regulates responses to TGF-β1 activation

CAFET Algorithm Reveals Wnt/PCP Signature in Lung Squamous Cell Carcinoma

We analyzed the gene expression patterns of 138 Non-Small Cell Lung Cancer (NSCLC) samples and developed a new algorithm called Coverage Analysis with Fisher’s Exact Test (CAFET) to identify molecular pathways that are differentially activated in squamous cell carcinoma (SCC) and adenocarcinoma (AC) subtypes. Analysis of the lung cancer samples demonstrated hierarchical clustering according to the histological subtype and revealed a strong enrichment for the Wnt signaling pathway components in the cluster consisting predominantly of SCC samples. The specific gene expression pattern observed correlated with enhanced activation of the Wnt Planar Cell Polarity (PCP) pathway and inhibition of the canonical Wnt signaling branch. Further real time RT-PCR follow-up with additional primary tumor samples and lung cancer cell lines confirmed enrichment of Wnt/PCP pathway associated genes in the SCC subtype. Dysregulation of the canonical Wnt pathway, characterized by increased levels of β-catenin and epigenetic silencing of negative regulators, has been reported in adenocarcinoma of the lung. Our results suggest that SCC and AC utilize different branches of the Wnt pathway during oncogenesis

The somatic mutation profiles of 2,433 breast cancers refines their genomic and transcriptomic landscapes

The genomic landscape of breast cancer is complex, and inter- and intra-tumour heterogeneity are important challenges in treating the disease. In this study, we sequence 173 genes in 2,433 primary breast tumours that have copy number aberration (CNA), gene expression and long-term clinical follow-up data. We identify 40 mutation-driver (Mut-driver) genes, and determine associations between mutations, driver CNA profiles, clinical-pathological parameters and survival. We assess the clonal states of Mut-driver mutations, and estimate levels of intra-tumour heterogeneity using mutant-allele fractions. Associations between PIK3CA mutations and reduced survival are identified in three subgroups of ER-positive cancer (defined by amplification of 17q23, 11q13-14 or 8q24). High levels of intra-tumour heterogeneity are in general associated with a worse outcome, but highly aggressive tumours with 11q13-14 amplification have low levels of intra-tumour heterogeneity. These results emphasize the importance of genome-based stratification of breast cancer, and have important implications for designing therapeutic strategies.The METABRIC project was funded by Cancer Research UK, the British Columbia Cancer Foundation and Canadian Breast Cancer Foundation BC/Yukon. This sequencing project was funded by CRUK grant C507/A16278 and Illumina UK performed all the sequencing. The authors also acknowledge the support of the University of Cambridge, Hutchinson Whampoa, the NIHR Cambridge Biomedical Research Centre, the Cambridge Experimental Cancer Medicine Centre, the Centre for Translational Genomics (CTAG) Vancouver and the BCCA Breast Cancer Outcomes Unit. We thank the Genomics, Histopathology, and Biorepository Core Facilities at the Cancer Research UK Cambridge Institute, and the Addenbrooke’s Human Research Tissue Bank (supported by the National Institute for Health Research Cambridge Biomedical Research Centre).This is the final version of the article. It first appeared from Nature Publishing Group via http://dx.doi.org/10.1038/ncomms1147

Genetic algorithm based feature selection combined with dual classification for the automated detection of proliferative diabetic retinopathy

Proliferative diabetic retinopathy (PDR) is a condition that carries a high risk of severe visual impairment. The hallmark of PDR is the growth of abnormal new vessels. In this paper, an automated method for the detection of new vessels from retinal images is presented. This method is based on a dual classification approach. Two vessel segmentation approaches are applied to create two separate binary vessel map which each hold vital information. Local morphology features are measured from each binary vessel map to produce two separate 4-D feature vectors. Independent classification is performed for each feature vector using a support vector machine (SVM) classifier. The system then combines these individual outcomes to produce a final decision. This is followed by the creation of additional features to generate 21-D feature vectors, which feed into a genetic algorithm based feature selection approach with the objective of finding feature subsets that improve the performance of the classification. Sensitivity and specificity results using a dataset of 60 images are 0.9138 and 0.9600, respectively, on a per patch basis and 1.000 and 0.975, respectively, on a per image basis