Sustainability in Textiles and Fashion – The Current Challenge

This paper is concerned with sustainability in textiles and fashion and identifies the problems and challenges faced by the international textile and fashion industry. Traditional product end uses as well as novel applications associated with the minority natural fibres such as jute, ramie and hemp are identified, and the potential advantages of focusing on developing the applicability of such in fashion applications is assessed. A series of strategic proposals, aimed at gaining attention in global textile and fashion markets, is presented

An observational study of pediatric healthcare burden in Angelman syndrome: results from a real-world study.

BACKGROUND:The objective of this study is to describe variations in the healthcare resource utilization (HRU) among individuals with Angelman syndrome (AS) over the first 12 years of life. Data for this study were drawn from the AS Natural History study (ASNHS), which is an observational study on the developmental progress, behavior, and medical morbidity of individuals with AS conducted over eight years. Caregiver-reported information on hospitalization, surgery, and medication utilization was used to assess HRU. Repeated measures mixed effect models were used to assess the relationship between age and probability of hospitalization, surgery, and prescription medication utilization. RESULTS:Mean age at study enrollment was 6 years of age and both sexes were equally represented. The mean number of visits per participant was three. Results from this study suggest that individuals with AS have a high HRU burden. Hospitalization and surgery burden were highest in the first year of life. Use of medications for seizures and sleep disturbance increased over time. CONCLUSIONS:The study highlights the significant healthcare burden among individuals with AS. Future studies that estimate cost and caregiver burden associated with AS are needed to assess the lifelong economic impact of AS on families and healthcare system

Integrated Numerical Modelling System for Extreme Wave Events at the Wave Hub Site

This paper examines an extreme wave event which occurred during a storm at the Wave Hub site in 2012. The extreme wave of 9.57 m height was identified from a time series of the heave data collected by an Oceanor Seawatch Mini II Buoy deployed at the site. An energy density spectrum was derived from this time series and then used to drive a physical model, which represents the extreme wave at 1:20 scale in Plymouth University’s new COAST Lab. The NewWave technique was used to define the input to the physical model. The experiment is reproduced in a numerical wave tank using the fully nonlinear CFD library OpenFOAM® and the wave generation toolbox waves2Foam. Results are evaluated, and issues regarding the predictions of a numerical model that is driven by the NewWave input signal are discussed. This study sets the basis for further research in coupling field data, physical modelling and numerical modelling in a more efficient and balanced way. This will lead to the new approach of composite modelling that will be implemented in future work

Innovation and Strategic Renewal in Mature Markets: A Study of the Tennis Racket Industry

This paper presents a study of successive new product introductions in the mature tennis racket industry. The inquiry examines novel design\u27s important role in strategic renewal, under the assumption that innovation includes not only the development, production, and launch of new products, but also communication between firms and market. We explore this industry\u27s transformation through the strategic actions of innovative firms and subsequent competitive contagion. A tennis racket innovation triggers competitors\u27 imitative reactions and sways the market toward a new de facto standard when the new product launch includes marketing such as product endorsement by high-profile professional players and advertising. Our results indicate that innovators should actively manage various industry participants as an integral part of their strategic renewal efforts, especially when facing rivalry with “me-too” peers. We suggest the interface between firms and consumers as a next focus for research on strategic renewal

Exploratory analysis using machine learning to predict for chest wall pain in patients with stage I non-small-cell lung cancer treated with stereotactic body radiation therapy.

Background and purposeChest wall toxicity is observed after stereotactic body radiation therapy (SBRT) for peripherally located lung tumors. We utilize machine learning algorithms to identify toxicity predictors to develop dose-volume constraints.Materials and methodsTwenty-five patient, tumor, and dosimetric features were recorded for 197 consecutive patients with Stage I NSCLC treated with SBRT, 11 of whom (5.6%) developed CTCAEv4 grade ≥2 chest wall pain. Decision tree modeling was used to determine chest wall syndrome (CWS) thresholds for individual features. Significant features were determined using independent multivariate methods. These methods incorporate out-of-bag estimation using Random forests (RF) and bootstrapping (100 iterations) using decision trees.ResultsUnivariate analysis identified rib dose to 1 cc < 4000 cGy (P = 0.01), chest wall dose to 30 cc < 1900 cGy (P = 0.035), rib Dmax < 5100 cGy (P = 0.05) and lung dose to 1000 cc < 70 cGy (P = 0.039) to be statistically significant thresholds for avoiding CWS. Subsequent multivariate analysis confirmed the importance of rib dose to 1 cc, chest wall dose to 30 cc, and rib Dmax. Using learning-curve experiments, the dataset proved to be self-consistent and provides a realistic model for CWS analysis.ConclusionsUsing machine learning algorithms in this first of its kind study, we identify robust features and cutoffs predictive for the rare clinical event of CWS. Additional data in planned subsequent multicenter studies will help increase the accuracy of multivariate analysis

Wave energy absorption by a floating air bag

license: © 2016 Cambridge University Pres

The HSV-1 Latency-Associated Transcript Functions to Repress Latent Phase Lytic Gene Expression and Suppress Virus Reactivation from Latently Infected Neurons

open access articleHerpes simplex virus 1 (HSV-1) establishes life-long latent infection within sensory neurons, during which viral lytic gene expression is silenced. The only highly expressed viral gene product during latent infection is the latency-associated transcript (LAT), a non-protein coding RNA that has been strongly implicated in the epigenetic regulation of HSV-1 gene expression. We have investigated LAT-mediated control of latent gene expression using chromatin immunoprecipitation analyses and LAT-negative viruses engineered to express firefly luciferase or β-galactosidase from a heterologous lytic promoter. Whilst we were unable to determine a significant effect of LAT expression upon heterochromatin enrichment on latent HSV-1 genomes, we show that reporter gene expression from latent HSV-1 genomes occurs at a greater frequency in the absence of LAT. Furthermore, using luciferase reporter viruses we have observed that HSV-1 gene expression decreases during long-term latent infection, with a most marked effect during LAT-negative virus infection. Finally, using a fluorescent mouse model of infection to isolate and culture single latently infected neurons, we also show that reactivation occurs at a greater frequency from cultures harbouring LAT-negative HSV-1. Together, our data suggest that the HSV-1 LAT RNA represses HSV-1 gene expression in small populations of neurons within the mouse TG, a phenomenon that directly impacts upon the frequency of reactivation and the maintenance of the transcriptionally active latent reservoir