The rational development of molecularly imprinted polymer-based sensors for protein detection.

The detection of specific proteins as biomarkers of disease, health status, environmental monitoring, food quality, control of fermenters and civil defence purposes means that biosensors for these targets will become increasingly more important. Among the technologies used for building specific recognition properties, molecularly imprinted polymers (MIPs) are attracting much attention. In this critical review we describe many methods used for imprinting recognition for protein targets in polymers and their incorporation with a number of transducer platforms with the aim of identifying the most promising approaches for the preparation of MIP-based protein sensors (277 references)

Monitoring the impacts of trade agreements on food environments

The liberalization of international trade and foreign direct investment through multilateral, regional and bilateral agreements has had profound implications for the structure and nature of food systems, and therefore, for the availability, nutritional quality, accessibility, price and promotion of foods in different locations. Public health attention has only relatively recently turned to the links between trade and investment agreements, diets and health, and there is currently no systematic monitoring of this area. This paper reviews the available evidence on the links between trade agreements, food environments and diets from an obesity and non-communicable disease (NCD) perspective. Based on the key issues identified through the review, the paper outlines an approach for monitoring the potential impact of trade agreements on food environments and obesity/NCD risks. The proposed monitoring approach encompasses a set of guiding principles, recommended procedures for data collection and analysis, and quantifiable ‘minimal’, ‘expanded’ and ‘optimal’ measurement indicators to be tailored to national priorities, capacity and resources. Formal risk assessment processes of existing and evolving trade and investment agreements, which focus on their impacts on food environments will help inform the development of healthy trade policy, strengthen domestic nutrition and health policy space and ultimately protect population nutrition.The following organizations provided funding support for the travel of participants to Italy for this meeting and the preparation of background research papers: The Rockefeller Foundation, International Obesity Taskforce (IOTF), University of Auckland, Deakin University, The George Institute, University of Sydney, Queensland University of Technology, University of Oxford, University of Pennsylvania Perelman School of Medicine, World Cancer Research Fund International, University of Toronto, and The Australian National University. The Faculty of Health at Deakin University kindly supported the costs for open access availability of this paper, and the Australian National Health and Medical Research Council Centre for Research Excellence in Obesity Policy and Food Systems (APP1041020) supported the coordination and finalizing of INFORMAS manuscripts

Innovative system identification methods for monitoring applications

Monitoring the modal parameters of civil and mechanical system received plenty of interest the last decades. Several approaches have been proposed and successfully applied in civil engineering for structural health monitoring of bridges (mainly based on the monitoring of the resonant frequencies and mode shapes). In applications such as the monitoring of offshore wind turbines and flight flutter testing the monitoring of the damping ratios are essential. For offshore wind turbine monitoring the presence of time-varying harmonic components, close to the modes of interest, can complicate the identification process. The difficulty related to flight flutter testing is that, in general, only short data records are available. The aim of this contribution is to introduce system identification methods and monitoring strategies that result in more reliable decisions and that can cope with complex monitoring applications. Basic concepts of system identification will be recapitulated with attention for monitoring aspects. The proposed monitoring methodology is based on the recently introduced Transmissibility-based Operational Modal Analysis (TOMA) approach

Fidelity protocol for the Action Success Knowledge (ASK) trial: A psychosocial intervention administered by speech and language therapists to prevent depression in people with post-stroke aphasia

Introduction: Treatment fidelity is a complex, multifaceted evaluative process which refers to whether a studied intervention was delivered as intended. Monitoring and enhancing fidelity is one recommendation of the TiDIER (Template for Intervention Description and Replication) checklist, as fidelity can inform interpretation and conclusions drawn about treatment effects. Despite the methodological and translational benefits, fidelity strategies have been used inconsistently within health behaviour intervention studies; in particular, within aphasia intervention studies, reporting of fidelity remains relatively rare. This paper describes the development of a fidelity protocol for the Action Success Knowledge (ASK) study, a current cluster randomised trial investigating an early mood intervention for people with aphasia (a language disability caused by stroke). Methods and analysis: A novel fidelity protocol and tool was developed to monitor and enhance fidelity within the two arms (experimental treatment and attention control) of the ASK study. The ASK fidelity protocol was developed based on the National Institutes of Health Behaviour Change Consortium fidelity framework. Ethics and dissemination: The study protocol was approved by the Darling Downs Hospital and Health Service Human Research Ethics Committee in Queensland, Australia under the National Mutual Acceptance scheme of multicentre human research projects. Specific ethics approval was obtained for those participating sites who were not under the National Mutual Agreement at the time of application. The monitoring and ongoing conduct of the research project is in line with requirements under the National Mutual Acceptance. On completion of the trial, findings from the fidelity reviews will be disseminated via publications and conference presentations. Trial registration number ACTRN12614000979651

Attention-Based Recurrent Neural Networks (RNNs) for Short Text Classification: An Application in Public Health Monitoring

In this paper, we propose an attention-based approach to short text classification, which we have created for the practical application of Twitter mining for public health monitoring. Our goal is to automatically filter Tweets which are relevant to the syndrome of asthma/difficulty breathing. We describe a bi-directional Recurrent Neural Network architecture with an attention layer (termed ABRNN) which allows the network to weigh words in a Tweet differently based on their perceived importance. We further distinguish between two variants of the ABRNN based on the Long Short Term Memory and Gated Recurrent Unit architectures respectively, termed the ABLSTM and ABGRU. We apply the ABLSTM and ABGRU, along with popular deep learning text classification models, to a Tweet relevance classification problem and compare their performances. We find that the ABLSTM outperforms the other models, achieving an accuracy of 0.906 and an F1-score of 0.710. The attention vectors computed as a by-product of our models were also found to be meaningful representations of the input Tweets. As such, the described models have the added utility of computing document embeddings which could be used for other tasks besides classification. To further validate the approach, we demonstrate the ABLSTM’s performance in the real world application of public health surveillance and compare the results with real-world syndromic surveillance data provided by Public Health England (PHE). A strong positive correlation was observed between the ABLSTM surveillance signal and the real-world asthma/difficulty breathing syndromic surveillance data. The ABLSTM is a useful tool for the task of public health surveillance

The New Normal in the Post-pandemic Workplace? A Meta-Analysis on the Use Cases and Implementation Challenges of Internet-of-Things Technology in Office Settings

While governmental tracing apps received special attention by research and the media during the Covid-19 pandemic, the surge in new work surveillance technologies went almost unnoticed. New organizational infrastructures based on Internet-of-things (IoT) technology have emerged at both, public and private sector organizations, promising a safe return to the workplace but equally threatening the privacy of employees. The goal of this paper is to take a closer look at a technology with ambivalent use by conducting a meta-synthesis of extant IoT studies. We classify the literature into four use cases with their implementation options: physical health monitoring, mental health monitoring, environmental health monitoring, and connected workplace. We also discuss main challenges emerging from privacy concerns along the IoT data lifecycle for occupational health initiatives in the office context. Based on that, we propose normative guidelines to assist employers interested in implementing privacy preserving IoT solu-tions for health and safety at work