Optimisation of distributed feedback laser biosensors

A new integrated optical sensor chip is proposed, based on a modified distributed- feedback (DFB) semiconductor laser. The semiconductor layers of different refractive indices that comprise a laser form the basis of a waveguide sensor, where changes in the refractive index of material at the surface are sensed via changes in the evanescent field of the lasing mode. In DFB lasers, laser oscillation occurs at the Bragg wavelength. Since this is sensitive to the effective refractive index of the optical mode, the emission wavelength is sensitive to the index of a sample on the waveguide surface. Hence, lasers are modelled as planar waveguides and the effective index of the fundamental transverse electric mode is calculated as a function of index and thickness of a thin surface layer using the beam propagation method. We find that an optimised structure has a thin upper cladding layer of ~0.15 mum, which according to this model gives detection limits on test layer index and thickness resolution of 0.1 and 1.57 nm, respectively, a figure which may be further improved using two lasers in an interferometer-type configuration

Impossible decision? An investigation of risk trade-offs in the intensive care unit

In the intensive care unit (ICU), clinicians must often make risk trade-offs on patient care. For example, on deciding whether to discharge a patient before they have fully recovered in order to create a bed for another, sicker, patient. When misjudged, these decisions can negatively influence patient outcomes: yet it can be difficult, if not impossible, for clinicians to evaluate with certainty the safest course of action. Using a vignette-based interview methodology, a naturalistic decision-making approach was utilised to study this phenomena. The decision preferences of ICU clinicians (n=24) for two common risk trade-off scenarios were investigated. Qualitative analysis revealed the sample of clinicians to reach different, and sometimes oppositional, decision preferences. These practice variations emerged from differing analyses of risk, how decisions were ‘framed’ (e.g. philosophies on care), past experiences, and perceptions of group and organisational norms. Implications for patient safety and clinical decision-making are discussed

Prediction of the Atomization Energy of Molecules Using Coulomb Matrix and Atomic Composition in a Bayesian Regularized Neural Networks

Exact calculation of electronic properties of molecules is a fundamental step for intelligent and rational compounds and materials design. The intrinsically graph-like and non-vectorial nature of molecular data generates a unique and challenging machine learning problem. In this paper we embrace a learning from scratch approach where the quantum mechanical electronic properties of molecules are predicted directly from the raw molecular geometry, similar to some recent works. But, unlike these previous endeavors, our study suggests a benefit from combining molecular geometry embedded in the Coulomb matrix with the atomic composition of molecules. Using the new combined features in a Bayesian regularized neural networks, our results improve well-known results from the literature on the QM7 dataset from a mean absolute error of 3.51 kcal/mol down to 3.0 kcal/mol.Comment: Under review ICANN 201

Using MCDA to generate and interpret evidence to inform local government investment in public health

Smoking is the single biggest cause of preventable death in the Uited Kingdom (UK) and is a major cause of coronary heart disease, some cancers, and respiratory disease, including chronic obstructive pulmonary disease. At the time of initiating the project, smoking prevalence had not changed across four local government areas in South Yorkshire for some years. Most spending had been focussed on helping people quit, an intervention where there was clear evidence of effectiveness. A number of changes occurred in public health structures and targets, requiring a reappraisal of the range of interventions offered. This was challenging due to a lack of clear evidence for some of the areas’ alternative interventions. The aim of this paper is to describe the use of a multi-criteria decision analysis (MCDA) approach to support the health priority setting in local authorities to reduce smoking prevalence. There were three phases to this process: (1) problem structuring; (2) the multiple criteria decision analysis; (3) and using the MCDA results to influence decision making at the local government level. The MCDA approach was used to collate information in a consistent and transparent manner, using expert, stakeholder and public opinion to fill known gaps in evidence. Fifteen interventions (such as stop smoking support services, smoke-free spaces, communication and marketing exercises, and increased investment in enforcement) were ranked across eight criteria (relating to reductions in prevalence across relevant groups, as well as aspects relating to equity and feasibility), allowing a range of relevant concerns to be incorporated. Subsequent steps were taken to translate the results of this stage into workable policy options. The results differed significantly from current practice. Sensitivity analysis showed that the findings were robust to changes in preference weights. These results informed subsequent changes to the interventions offered across the four boroughs. The ability of MCDA techniques to incorporate data and both qualitative and quantitative judgements in a formal manner mean that they are well suited to support public health decision making, where evidence is often only partially available and many policies are value driven. MCDA methods, if used, should be chosen carefully based on their resource/time constraints, scientific validity, and the significance and broader context of the decision problem.This is the final version of the article. It first appeared from Springer via http://dx.doi.org/10.1007/s40070-016-0059-

U-DADA:Unsupervised Deep Action Domain Adaptation

The problem of domain adaptation has been extensively studied for object classification task. However, this problem has not been as well studied for recognizing actions. While, object recognition is well understood, the diverse variety of videos in action recognition make the task of addressing domain shift to be more challenging. We address this problem by proposing a new novel adaptation technique that we term as unsupervised deep action domain adaptation (U-DADA). The main concept that we propose is that of explicitly modeling density based adaptation and using them while adapting domains for recognizing actions. We show that these techniques work well both for domain adaptation through adversarial learning to obtain invariant features or explicitly reducing the domain shift between distributions. The method is shown to work well using existing benchmark datasets such as UCF50, UCF101, HMDB51 and Olympic Sports. As a pioneering effort in the area of deep action adaptation, we are presenting several benchmark results and techniques that could serve as baselines to guide future research in this area.</p

Computer-aided rational design of the phosphotransferase system for enhanced glucose uptake in Escherichia coli

The phosphotransferase system (PTS) is the sugar transportation machinery that is widely distributed in prokaryotes and is critical for enhanced production of useful metabolites. To increase the glucose uptake rate, we propose a rational strategy for designing the molecular architecture of the Escherichia coli glucose PTS by using a computer-aided design (CAD) system and verified the simulated results with biological experiments. CAD supports construction of a biochemical map, mathematical modeling, simulation, and system analysis. Assuming that the PTS aims at controlling the glucose uptake rate, the PTS was decomposed into hierarchical modules, functional and flux modules, and the effect of changes in gene expression on the glucose uptake rate was simulated to make a rational strategy of how the gene regulatory network is engineered. Such design and analysis predicted that the mlc knockout mutant with ptsI gene overexpression would greatly increase the specific glucose uptake rate. By using biological experiments, we validated the prediction and the presented strategy, thereby enhancing the specific glucose uptake rate

Black Stork Down: Military Discourses in Bird Conservation in Malta

Tensions between Maltese hunters and bird conservation NGOs have intensified over the past decade. Conservation NGOs have become frustrated with the Maltese State for conceding to the hunter lobby and negotiating derogations from the European Union’s Bird Directive. Some NGOs have recently started to organize complex field-operations where volunteers are trained to patrol the landscape, operate drones and other surveillance technologies, detect illegalities, and lead police teams to arrest poachers. We describe the sophisticated military metaphors which conservation NGOs have developed to describe, guide and legitimize their efforts to the Maltese public and their fee-paying members. We also discuss why such groups might be inclined to adopt these metaphors. Finally, we suggest that anthropological studies of discourse could help understand delicate contexts such as this where conservation NGOs, hunting associations and the State have ended in political deadlock

Life! in Australia : translating prevention research into a large scale intervention

The increasing prevalence of type 2 diabetes is of great public health concern. In the state of Victoria, Australia, a group-based lifestyle intervention programme, Life! &ndash; Taking Action on Diabetes, was developed for people over the age of 50 years who are at high risk of diabetes. It aims to reduce the risk of diabetes by providing practical skills, including goal setting and problem solving, to encourage participants to adopt a healthy diet and active lifestyle. The programme is delivered by specially trained facilitators who have undergone an accredited three-stage training programme. A quality assurance process is also in place to ensure that it is delivered to a consistently high standard. The Life! programis a direct progression from the Finnish randomised controlled trial and the Greater Green Triangle Diabetes Prevention Project implementation trial. This paper describes how a diabetes prevention programme was implemented at a state-wide level and the training of facilitators to conduct the group sessions. Future studies are needed to examine the cost effectiveness and development of specific programmes for diverse population groups.<br /

Tolfenamic Acid Induces Apoptosis and Growth Inhibition in Head and Neck Cancer: Involvement of NAG-1 Expression

Nonsteroidal anti-inflammatory drug-activated gene-1 (NAG-1) is induced by nonsteroidal anti-inflammatory drugs and possesses proapoptotic and antitumorigenic activities. Although tolfenamic acid (TA) induces apoptosis in head and neck cancer cells, the relationship between NAG-1 and TA has not been determined. This study investigated the induction of apoptosis in head and neck cancer cells treated by TA and the role of NAG-1 expression in this induction. TA reduced head and neck cancer cell viability in a dose-dependent manner and induced apoptosis. The induced apoptosis was coincident with the expression of NAG-1. Overexpression of NAG-1 enhanced the apoptotic effect of TA, whereas suppression of NAG-1 expression by small interfering RNA attenuated TA-induced apoptosis. TA significantly inhibited tumor formation as assessed by xenograft models, and this result accompanied the induction of apoptotic cells and NAG-1 expression in tumor tissue samples. Taken together, these results demonstrate that TA induces apoptosis via NAG-1 expression in head and neck squamous cell carcinoma, providing an additional mechanistic explanation for the apoptotic activity of TA

Cancer Cachexia: Traditional Therapies and Novel Molecular Mechanism-Based Approaches to Treatment

The complex syndrome of cancer cachexia (CC) that occurs in 50% to 80% cancer patients has been identified as an independent predictor of shorter survival and increased risk of treatment failure and toxicity, contributing to the mortality and morbidity in this population. CC is a pathological state including a symptom cluster of loss of muscle (skeletal and visceral) and fat, manifested in the cardinal feature of emaciation, weakness affecting functional status, impaired immune system, and metabolic dysfunction. The most prominent feature of CC is its non-responsiveness to traditional treatment approaches; randomized clinical trials with appetite stimulants, 5-HT3 antagonists, nutrient supplementation, and Cox-2 inhibitors all have failed to demonstrate success in reversing the metabolic abnormalities seen in CC. Interventions based on a clear understanding of the mechanism of CC, using validated markers relevant to the underlying metabolic abnormalities implicated in CC are much needed. Although the etiopathogenesis of CC is poorly understood, studies have proposed that NFkB is upregulated in CC, modulating immune and inflammatory responses induce the cellular breakdown of muscle, resulting in sarcopenia. Several recent laboratory studies have shown that n-3 fatty acid may attenuate protein degradation, potentially by preventing NFkB accumulation in the nucleus, preventing the degradation of muscle proteins. However, clinical trials to date have produced mixed results potentially attributed to timing of interventions (end stage) and utilizing outcome markers such as weight which is confounded by hydration, cytotoxic therapies, and serum cytokines. We propose that selective targeting of proteasome activity with a standardized dose of omega-3-acid ethyl esters, administered to cancer patients diagnosed with early stage CC, in addition to a standard intervention with nutritionally adequate diet and appetite stimulants, will alter metabolic abnormalities by downregulating NFkB, preventing the breakdown of myofibrillar proteins and resulting in increasing serum protein markers, lean body mass, and functional status