Chronic Wounds: The Persistent Infection Problem

Chronic wounds heal poorly and can have a huge impact on a sufferer’s life. They are caused by a number of factors, one of which is the presence of persistent infections. Many standard treatments are unsuccessful at destroying these infections as the bacteria form a biofilm. Biofilms encase the bacteria, preventing immune cells from destroying them. There are multiple bacterial species within a biofilm, sometimes with antibiotics resistance, and which species are present changes over time. The changing, multi-species nature of biofilms can make finding an effective antibiotic treatment difficult. Also, bacteria in biofilms genetically differ from planktonic bacteria, and are often less susceptible to antibiotics. Additionally, biofilms are thought to reduce the access of antibiotics to the bacteria within. These reasons are discussed in further detail in this review, along with some of the reasons why bacteria can prevent wound closure

Application of Probabilistic Neural Networks in Modelling Structural Deterioration of Stormwater Pipes

In Australia, when stormwater systems were first introduced over 100 years ago, they were constructed independently of the sewer systems, and they are normally the responsibility of the third level of government, i.e., local government or city councils. Because of the increasing age of these stormwater systems and their worsening performance, there are serious concerns in a significant number of city councils regarding their deterioration. A study has been conducted on the structural deterioration of concrete pipes that make up the bulk of the stormwater pipe systems in these councils. In an attempt to look for a reliable deterioration model, a probabilistic neural network (PNN) model was developed using the data set supplied from participating councils. The PNN model was validated with snapshot-based sample data, which makes up the data set. The predictive performance of the PNN model was compared with a traditional parametric model using discriminant analysis on the same data set. Structural deterioration was hypothesised to be influenced by a set of explanatory factors, including pipe design and construction factors—such as pipe size, buried depth—and site factors— such as soil type, moisture index, tree root intrusion, etc. The results show that the PNN model has a better predictive power and uses significantly more input variables (i.e., explanatory factors) than the discriminant model. More importantly, the key factors for prediction in the PNN model are difficult to interpret, suggesting that besides prediction accuracy, model interpretation is an important issue for further investigation

Exploiting Local Features from Deep Networks for Image Retrieval

Deep convolutional neural networks have been successfully applied to image classification tasks. When these same networks have been applied to image retrieval, the assumption has been made that the last layers would give the best performance, as they do in classification. We show that for instance-level image retrieval, lower layers often perform better than the last layers in convolutional neural networks. We present an approach for extracting convolutional features from different layers of the networks, and adopt VLAD encoding to encode features into a single vector for each image. We investigate the effect of different layers and scales of input images on the performance of convolutional features using the recent deep networks OxfordNet and GoogLeNet. Experiments demonstrate that intermediate layers or higher layers with finer scales produce better results for image retrieval, compared to the last layer. When using compressed 128-D VLAD descriptors, our method obtains state-of-the-art results and outperforms other VLAD and CNN based approaches on two out of three test datasets. Our work provides guidance for transferring deep networks trained on image classification to image retrieval tasks.Comment: CVPR DeepVision Workshop 201

Hot Carrier extraction with plasmonic broadband absorbers

Hot charge carrier extraction from metallic nanostructures is a very promising approach for applications in photo-catalysis, photovoltaics and photodetection. One limitation is that many metallic nanostructures support a single plasmon resonance thus restricting the light-to-charge-carrier activity to a spectral band. Here we demonstrate that a monolayer of plasmonic nanoparticles can be assembled on a multi-stack layered configuration to achieve broad-band, near-unit light absorption, which is spatially localised on the nanoparticle layer. We show that this enhanced light absorbance leads to $\sim$ 40-fold increases in the photon-to-electron conversion efficiency by the plasmonic nanostructures. We developed a model that successfully captures the essential physics of the plasmonic hot-electron charge generation and separation in these structures. This model also allowed us to establish that efficient hot carrier extraction is limited to spectral regions where the photons possessing energies higher than the Schottky junctions and the localised light absorption of the metal nanoparticles overlap.Comment: submitte

Calls to a home birth helpline: empowerment in childbirth

In the UK a woman has the right to decide to give birth at home, irrespective of whether she is expecting her first or a subsequent child and of any perceived ‘risk’ factors. However, the rate of home births in the UK is very low (around 2%), varies widely across the country and many women do not know how to arrange midwifery cover. The Home Birth helpline is a UK-based voluntary organisation offering support and information for women planning a home birth. In order to gain direct access to the issues that are of concern to women when planning a home birth, 80 calls to the helpline were recorded. The aims of this paper are to document the problems that callers to this helpline report having when trying to arrange home births and to explore the strategies the call-taker uses in helping women to exercise their right to birth at home. The paper concludes that women are not easily able to exercise their right to choose the place of birth and suggests a number of recommendations for action

Servitization through outcome-based contract – a systems perspective from the defence industry

This paper provides a viable systems perspective of an outcome-based service initiative involving major manufacturers in the defence industry. The viable systems perspective allowed a coherent structuration of the complex servitization context involving provider and customer organizations. It also unveiled critical relationship mechanisms that enable synergy and facilitate the achievement of co-capability by the organizations involved. Through a case study approach, the research finds that interventions in the customer system reduce variability in the provider system as well as in the service system as a whole. The systemic interventions are implemented via key provider/customer relationships the study identifies. The relationships deal with the high level of internal variety in outcome-based service systems. A typology for the identified relationships is developed, offering a helpful basis for the purposeful planning and design of interactions aimed at developing co-capability. The paper also offers theoretical propositions defining fundamental features of outcome-based service systems. The unique characteristics of these systems addressed in this paper provide particularly useful insights concerning the implementation of this type of servitization initiative not only in the defence industry, but also in other industrial sectors where servitization initiatives involve complex configurations of provider and customer organizations

Draft Genome Sequences of 1,183 Salmonella Strains from the 100K Pathogen Genome Project.

Salmonella is a common food-associated bacterium that has substantial impact on worldwide human health and the global economy. This is the public release of 1,183 Salmonella draft genome sequences as part of the 100K Pathogen Genome Project. These isolates represent global genomic diversity in the Salmonella genus

Development of a Coherent Doppler Lidar for Precision Maneuvering and Landing of Space Vehicles

A coherent Doppler lidar has been developed to address NASAs need for a high-performance, compact, and cost-effective velocity and altitude sensor onboard its landing vehicles. Future robotic and manned missions to planetary bodies require precise ground-relative velocity vector and altitude data to execute complex descent maneuvers and safe, soft landing at a pre-designated site. This lidar sensor, referred to as a Navigation Doppler Lidar, meets the required performance of landing missions while complying with vehicle size, mass, and power constraints. Operating from over five kilometers altitude, the lidar obtains velocity and range precision measurements with 2 cm/sec and 2 meters, respectively, dominated by the vehicle motion. After a series of flight tests onboard helicopters and rocket-powered free-flyer vehicles, the Navigation Doppler Lidar is now being ruggedized for future missions to various destinations in the solar system

DeepBrain: Functional Representation of Neural In-Situ Hybridization Images for Gene Ontology Classification Using Deep Convolutional Autoencoders

This paper presents a novel deep learning-based method for learning a functional representation of mammalian neural images. The method uses a deep convolutional denoising autoencoder (CDAE) for generating an invariant, compact representation of in situ hybridization (ISH) images. While most existing methods for bio-imaging analysis were not developed to handle images with highly complex anatomical structures, the results presented in this paper show that functional representation extracted by CDAE can help learn features of functional gene ontology categories for their classification in a highly accurate manner. Using this CDAE representation, our method outperforms the previous state-of-the-art classification rate, by improving the average AUC from 0.92 to 0.98, i.e., achieving 75% reduction in error. The method operates on input images that were downsampled significantly with respect to the original ones to make it computationally feasible