Nanoparticle drug delivery systems for inner ear therapy: An overview

open7noembargoed_20180701Valente, Filippo; Astolfi, Laura; Simoni, Edi; Danti, Serena; Franceschini, Valeria; Chicca, Milvia; Martini, AlessandroValente, Filippo; Astolfi, Laura; Simoni, Edi; Danti, Serena; Franceschini, Valeria; Chicca, Milvia; Martini, Alessandr

A logic for reasoning about ambiguity

Standard models of multi-agent modal logic do not capture the fact that information is often \emph{ambiguous}, and may be interpreted in different ways by different agents. We propose a framework that can model this, and consider different semantics that capture different assumptions about the agents' beliefs regarding whether or not there is ambiguity. We examine the expressive power of logics of ambiguity compared to logics that cannot model ambiguity, with respect to the different semantics that we propose.Comment: Some of the material in this paper appeared in preliminary form in "Ambiguous langage and differences of belief" (see arXiv:1203.0699

Preparation, characterization and in-vitro efficacy of quercetin loaded liquid crystalline nanoparticles for the treatment of asthma

© 2019 Elsevier B.V. The present study aims to formulate quercetin loaded liquid crystalline nanoparticles (LCN) and surface modified liquid crystalline nanoparticles (sm-LCN) as well as investigate their anti-inflammatory activity in human primary bronchial epithelial cell line (BCi-NS1.1) induced with lipopolysaccharide (LPS). Quercetin LCN were prepared using ultrasonication method. The formulated LCNs and sm-LCNs were characterised in terms of particle size, zeta potential as well as the drug encapsulation efficiency. Furthermore, their morphology and in vitro release profile were also studied. In addition, the anti-inflammatory activity of quercetin LCN and sm-LCNs were evaluated by measuring the concentration of pro-inflammatory markers namely interleukin (IL)-1β, IL-6 and IL-8 in BCI-NS1.1 cell lines via cytometric bead array. The molecular mechanism inherent to the inclusion of quercetin into monoolein nanosystem and surface modification of the nanosystem with chitosan was elucidated via molecular mechanics simulations. Quercetin LCN and sm-LCN significantly (p < 0.05) decreased the production of IL-1β, IL-6 and IL-8 compared to LPS only group. Encapsulation of quercetin into LCN and sm-LCN further enhanced its anti-inflammatory activity compared to quercetin in dimethyl sulfoxide (DMSO). In addition to that, quercetin LCN and sm-LCN also exhibited comparable activity to fluticasone in terms of significantly (p < 0.05) reducing the production of IL-1β and IL-6. Quercetin loaded LCN and sm-LCN could be a potential therapeutic intervention for asthma as they are efficacious in suppressing the production of key pro-inflammatory cytokines associated with the development of asthma

Electricity clustering framework for automatic classification of customer loads

Clustering in energy markets is a top topic with high significance on expert and intelligent systems. The main impact of is paper is the proposal of a new clustering framework for the automatic classification of electricity customers’ loads. An automatic selection of the clustering classification algorithm is also highlighted. Finally, new customers can be assigned to a predefined set of clusters in the classificationphase. The computation time of the proposed framework is less than that of previous classification tech- niques, which enables the processing of a complete electric company sample in a matter of minutes on a personal computer. The high accuracy of the predicted classification results verifies the performance of the clustering technique. This classification phase is of significant assistance in interpreting the results, and the simplicity of the clustering phase is sufficient to demonstrate the quality of the complete mining framework.Ministerio de Economía y Competitividad TEC2013-40767-RMinisterio de Economía y Competitividad IDI- 2015004

The reliability of trace DNA or low copy number (LCN) DNA evidence in court proceedings

Although forensic DNA testing is well established, some experts disagree with the interpretation and statistical significance of test results obtained from very small samples. This article discusses the problems regarding the use of the low copy number (LCN) technique as well as the value that can be derived from such an analysis. It focuses on the problematic results that can arise from using very small samples for forensic DNA identification. Since this kind of analysis is based on low amounts of DNA samples (between 100 picograms and 200 picograms in South Africa) that are amplified by using more than the normal 28 cycles to create larger samples for analysis, the reliability of the analysis has been questioned. The amplification process, known as the Polymerase Chain Reaction (PCR), is associated with risks such as stochastic effects and contamination that could make interpretation of the results difficult for the defence. While standard operating laboratory protocols could prevent contamination and although the electropherograms could aid the detection of contamination, it is highly problematic for the defence counsel to ascertain whether these procedures were indeed strictly followed. Drawing on foreign jurisprudence, this article considers the risks and key controversies and explains what lawyers need to know, in order to be able to recognise controversial results that could stem from using the LCN DNA technique for forensic DNA identification. The conclusions thus drawn may be of particular relevance to the South African context, as no reported case law exists in which the issues relating to the use of LCN DNA have yet come to the fore

ReSeg: A Recurrent Neural Network-based Model for Semantic Segmentation

We propose a structured prediction architecture, which exploits the local generic features extracted by Convolutional Neural Networks and the capacity of Recurrent Neural Networks (RNN) to retrieve distant dependencies. The proposed architecture, called ReSeg, is based on the recently introduced ReNet model for image classification. We modify and extend it to perform the more challenging task of semantic segmentation. Each ReNet layer is composed of four RNN that sweep the image horizontally and vertically in both directions, encoding patches or activations, and providing relevant global information. Moreover, ReNet layers are stacked on top of pre-trained convolutional layers, benefiting from generic local features. Upsampling layers follow ReNet layers to recover the original image resolution in the final predictions. The proposed ReSeg architecture is efficient, flexible and suitable for a variety of semantic segmentation tasks. We evaluate ReSeg on several widely-used semantic segmentation datasets: Weizmann Horse, Oxford Flower, and CamVid; achieving state-of-the-art performance. Results show that ReSeg can act as a suitable architecture for semantic segmentation tasks, and may have further applications in other structured prediction problems. The source code and model hyperparameters are available on https://github.com/fvisin/reseg.Comment: In CVPR Deep Vision Workshop, 201