Proceedings of the inaugural International Summit for Medical Nutrition Education and Research

© 2016 The Royal Society for Public Health Medical Nutrition Education (MNE) has been identified as an area with potential public health impact. Despite countries having distinctive education systems, barriers and facilitators to effective MNE are consistent across borders, demanding a common platform to initiate global programmes. A shared approach to supporting greater MNE is ideal to support countries to work together. In an effort to initiate this process, the Need for Nutrition Education/Innovation Programme group, in association with their strategic partners, hosted the inaugural International Summit on Medical Nutrition Education and Research on August 8, 2015 in Cambridge, UK. Speakers from the UK, the USA, Canada, Australia, New Zealand, Italy, and India provided insights into their respective countries including their education systems, inherent challenges, and potential solutions across two main themes: (1) Medical Nutrition Education, focused on best practice examples in competencies and assessment; and (2) Medical Nutrition Research, discussing how to translate nutrition research into education opportunities. The Summit identified shared needs across regions, showcased examples of transferrable strategies and identified opportunities for collaboration in nutrition education for healthcare (including medical) professionals. These proceedings highlight the key messages presented at the Summit and showcase opportunities for working together towards a common goal of improvement in MNE to improve public health at large

A progressive refinement approach for the visualisation of implicit surfaces

Visualising implicit surfaces with the ray casting method is a slow procedure. The design cycle of a new implicit surface is, therefore, fraught with long latency times as a user must wait for the surface to be rendered before being able to decide what changes should be introduced in the next iteration. In this paper, we present an attempt at reducing the design cycle of an implicit surface modeler by introducing a progressive refinement rendering approach to the visualisation of implicit surfaces. This progressive refinement renderer provides a quick previewing facility. It first displays a low quality estimate of what the final rendering is going to be and, as the computation progresses, increases the quality of this estimate at a steady rate. The progressive refinement algorithm is based on the adaptive subdivision of the viewing frustrum into smaller cells. An estimate for the variation of the implicit function inside each cell is obtained with an affine arithmetic range estimation technique. Overall, we show that our progressive refinement approach not only provides the user with visual feedback as the rendering advances but is also capable of completing the image faster than a conventional implicit surface rendering algorithm based on ray casting

Optimal procurement of flexibility services within electricity distribution networks

The increased injection of volatile renewable energy at local levels into the electricity grid is forcing the distribution network operators to seek participation in emerging service markets in order to obtain the flexibility required to balance their systems. However, the distribution companies lack experience in designing new market arrangements. We consider a market framework wherein a proactive distribution company is willing to purchase reserve capacity for overload management, using a two-part tariff. The problem is modelled as a three-stage stochastic market including Day-Ahead, Intra-Day and Real-Time, with uncertainty on both demand and generation. After assessing our formulation, we discuss the impact of risk-aversion at each stage with an objective function based on CVaR. Finally, different Intra-Day clearing horizons and delivery settings are evaluated. The results show that risk-aversion close to Real-Time becomes the main driver for decision makers and that early hedging strategies lead to sub-optimal solutions

Evaluation of flexibility markets for retailer-DSO-TSO coordination

The rise of distributed energy resources (DERs) can enhance the efficiency of system operations by providing flexibility services to the different agents involved, but they also pose a major resource allocation problem. This study considers three different agents procuring DER services: distribution system operators (DSOs) for local congestion management, transmission system operators (TSOs) for system-wide reserve deployment, and retailers for hedging against network usage tariffs based upon peak-load pricing. A variety of market mechanisms are identified to co-ordinate these needs, and three schemes are developed in detail. These are separate markets for each agent, co-ordinated Shapley value allocations for TSO and DSO, and a co-ordinated mechanism including retailers. These designs are evaluated on a realistic distribution network in Britain for two operational days. The results show a more efficient dispatch from the TSO–DSO co-ordinated procurement over independent sequential procurements. However, the inclusion of retailers in the joint dispatch is surprisingly less attractive due to the lack of improvement in social welfare and the undesirable impacts on the DSO

Supervised Domain Adaptation using Graph Embedding

Getting deep convolutional neural networks to perform well requires a large amount of training data. When the available labelled data is small, it is often beneficial to use transfer learning to leverage a related larger dataset (source) in order to improve the performance on the small dataset (target). Among the transfer learning approaches, domain adaptation methods assume that distributions between the two domains are shifted and attempt to realign them. In this paper, we consider the domain adaptation problem from the perspective of dimensionality reduction and propose a generic framework based on graph embedding. Instead of solving the generalised eigenvalue problem, we formulate the graph-preserving criterion as a loss in the neural network and learn a domain-invariant feature transformation in an end-to-end fashion. We show that the proposed approach leads to a powerful Domain Adaptation framework; a simple LDA-inspired instantiation of the framework leads to state-of-the-art performance on two of the most widely used Domain Adaptation benchmarks, Office31 and MNIST to USPS datasets.Comment: 7 pages, 3 figures, 3 table

Efecto del consumo de mate sobre el perfil lipídico en pacientes hipercolesterolémicos

El consumo diario de mate, en dosis de 50g o 100g, produce un descenso en CT y CLDL, mejorando el índice aterogénico en individuos hipercolesterolémicos

Asociación entre PSA y RsIL-6

Estudios recientes describen la intervención de la IL-6 en la fisiopatología del cáncer de próstata (CaP). Siendo esta patología de elevada incidencia en la población de edad avanzada, es relevante el conocimiento de los factores que intervienen en su desarrollo. El antígeno prostático específico (PSA) constituye el marcador tumoral de elección para screening y seguimiento del CaP. Por su parte, los niveles séricos de Receptor soluble de IL-6 (RsIL-6) serían indicativos del estado inflamatorio del paciente. El objetivo del trabajo fue estudiar la relación entre RsIL-6 y PSA en pacientes con y sin CaP de la población mendocina

Efecto del consumo de mate sobre el perfil lípido.

Varios estudios sugieren que el consumo habitual de mate tendría efectos hipocolesterolémicos por la presencia de saponinas que interfieren en la absorción del colesterol. El objetivo del presente trabajo fue analizar el efecto del consumo habitual de mate sobre el perfil lipídico. Se estudiaron 68 varones entre 50 y 79 años, sin medicación hipolipemiante