Abstract

Abstract
In this work, we investigate the superfluidic properties of light propagating in a four-level coherent atomic medium. The model is derived under the paraxial approximation in the form of a generalized nonlinear Schrodinger equation and features spatially controllable and quantum-enhanced optical properties, which can offer new possibilities in the field of optical analogue systems. In particular, we use this versatility to study the dynamics of an optical vortex beam confined in a nontrivial connected geometry, finding numerical evidence of another superfluidic signature analogue: the persistent current of light. (C) 2017 Optical Society of America

Abstract
BackgroundUltrasound is an effective tool for breast cancer diagnosis. However, its relatively low image quality makes small lesion analysis challenging. This promotes the development of tools to help clinicians in the diagnosis. MethodsWe propose a method for segmentation and three-dimensional (3D) reconstruction of lesions from ultrasound images acquired using the automated breast volume scanner (ABVS). Segmentation and reconstruction algorithms are applied to obtain the lesion's 3D geometry. A total of 140 artificial lesions with different sizes and shapes are reconstructed in gelatin-based phantoms and biological tissue. Dice similarity coefficient (DSC) is used to evaluate the reconstructed shapes. The algorithm is tested using a human breast phantom and clinical data from six patients. ResultsDSC values are 0.860.06 and 0.86 +/- 0.05 for gelatin-based phantoms and biological tissue, respectively. The results are validated by a specialized clinician. ConclusionsEvaluation metrics show that the algorithm accurately segments and reconstructs various lesions. Copyright (c) 2016 John Wiley & Sons, Ltd.

Abstract
Nowadays most learning problems demand adaptive solutions. Current challenges include temporal data streams, drift and non-stationary scenarios, often with text data, whether in social networks or in business systems. Various efforts have been pursued in machine learning settings to learn in such environments, specially because of their non-trivial nature, since changes occur between the distribution data used to define the model and the current environment. In this work we present the Drift Adaptive Retain Knowledge (DARK) framework to tackle adaptive learning in dynamic environments based on recent and retained knowledge. DARK handles an ensemble of multiple Support Vector Machine (SVM) models that are dynamically weighted and have distinct training window sizes. A comparative study with benchmark solutions in the field, namely the Learn + +.NSE algorithm, is also presented. Experimental results revealed that DARK outperforms Learn + +.NSE with two different base classifiers, an SVM and a Classification and Regression Tree (CART).

Abstract
A polyamide 6,6 (PA66) fabric pre-treated with a double barrier dielectric (DBD) atmospheric plasma in air was coated with 1 and 5 layers of an intrinsically conducting glycerol-doped PEDOT:PSS polymer (PEDOT:PSS + GLY) with the final objective of developing a cost-competitive and temperature controllable flexible-heating element to be used in clothing encapsulated between an outer and an inner separator layer in order to provide heat-reflecting properties and uniform temperature distribution, respectively. FTIR, DSC, TGA, SEM, EDS, XRD and DMA analyses show significant changes in morphology, chemistry, enthalpy, crystallinity and glass transition temperature confirming that PEDOT:PSS and glycerol are not only spread over the PA66 yarn surfaces but are dispersed in the bulk facilitating relaxation and increasing structure and chain flexibility. Electrochemical and electrical resistivity (rho) measurements confirm that the plasma treated PA66 coated with 5 layers of PEDOT:PSS + GLY presents the highest stability, resistance and capacitive behaviour, and the best capability of storing electrical energy. This configuration needs only 7.5 V to induce a temperature change up to 38 degrees C at a current density of 0.3 A g(-1). The desired temperature is easily adjustable as a function of the applied voltage and by the number of coated layers of PEDOT:PSS + GLY. Despite the need to improve the uniformity of the coating thickness on the fabric for uniform heat generation, the observed results are quite impressive since they can be compared to the temperature obtained in carbon nanotube composites using similar voltages. This cost-competitive, safe, highly flexible and stable thermoelectric fabric has potential for use in large area textiles as a heating element in a wide range of applications such as garments, carpets, blankets and automotive seats.

Abstract
The impact of inherited genetic variation on gene expression in humans is well-established. The majority of known expression quantitative trait loci (eQTLs) impact expression of local genes (cis-eQTLs). More research is needed to identify effects of genetic variation on distant genes (trans-eQTLs) and understand their biological mechanisms. One common trans-eQTLs mechanism is “mediation” by a local (cis) transcript. Thus, mediation analysis can be applied to genome-wide SNP and expression data in order to identify transcripts that are “cis-mediators” of trans-eQTLs, including those “cis-hubs” involved in regulation of many trans-genes. Identifying such mediators helps us understand regulatory networks and suggests biological mechanisms underlying trans-eQTLs, both of which are relevant for understanding susceptibility to complex diseases. The multitissue expression data from the Genotype-Tissue Expression (GTEx) program provides a unique opportunity to study cis-mediation across human tissue types. However, the presence of complex hidden confounding effects in biological systems can make mediation analyses challenging and prone to confounding bias, particularly when conducted among diverse samples. To address this problem, we propose a new method: Genomic Mediation analysis with Adaptive Confounding adjustment (GMAC). It enables the search of a very large pool of variables, and adaptively selects potential confounding variables for each mediation test. Analyses of simulated data and GTEx data demonstrate that the adaptive selection of confounders by GMAC improves the power and precision of mediation analysis. Application of GMAC to GTEx data provides new insights into the observed patterns of cis-hubs and trans-eQTL regulation across tissue types.