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

Measurement of the microwave effective permittivity in tensile-strained polyvinylidene difluoride trifluoroethylene filled with graphene

We report an interesting effect in the form of a rise (up to 13%) in the permittivity of graphene (GE) filled polyvinylidene difluoride trifluoroethylene (P(VDF-TrFE)) subjected to a small uniaxial deformation (up to 7% in the principal direction). Our findings differ from GE-PVDF homopolymer samples that show a decrease of permittivity upon elongation. We argue that the VDF content which controls the spontaneous polarization has a profound effect on the charge storage through the addition of interface density by the GE phase. (C) 2014 AIP Publishing LLC

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

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

Phase Space Analysis of Quintessence Cosmologies with a Double Exponential Potential

We use phase space methods to investigate closed, flat, and open Friedmann-Robertson-Walker cosmologies with a scalar potential given by the sum of two exponential terms. The form of the potential is motivated by the dimensional reduction of M-theory with non-trivial four-form flux on a maximally symmetric internal space. To describe the asymptotic features of run-away solutions we introduce the concept of a `quasi fixed point.' We give the complete classification of solutions according to their late-time behavior (accelerating, decelerating, crunch) and the number of periods of accelerated expansion.Comment: 46 pages, 5 figures; v2: minor changes, references added; v3: title changed, refined classification of solutions, 3 references added, version which appeared in JCA

User-made immobilities: a transitions perspective

In this paper we aim to conceptualize the role of users in creating, expanding and stabilizing the automobility system. Drawing on transition studies we offer a typology of user roles including user-producers, user-legitimators, user-intermediaries, user-citizens and user-consumers, and explore it on the historical transition to the automobile regime in the USA. We find that users play an important role during the entire transition process, but some roles are more salient than others in particular phases. Another finding is that the success of the transition depends on the stabilization of the emerging regime that will trigger upscaling in terms of the numbers of adopters. The findings are used to reflect on potential crossovers between transitions and mobilities research

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

SPAMS: A Novel Incremental Approach for Sequential Pattern Mining in Data Streams

International audienceMining sequential patterns in data streams is a new challenging problem for the datamining community since data arrives sequentially in the form of continuous rapid and infinite streams. In this paper, we propose a new on-line algorithm, SPAMS, to deal with the sequential patterns mining problem in data streams. This algorithm uses an automaton-based structure to maintain the set of frequent sequential patterns, i.e. SPA (Sequential Pat- tern Automaton). The sequential pattern automaton can be smaller than the set of frequent sequential patterns by two or more orders of magnitude, which allows us to overcome the problem of combinatorial explosion of se- quential patterns. Current results can be output constantly on any user 's specified thresholds. In addition, taking into account the characteristics of data streams, we propose a well-suited method said to be approximate since we can provide near optimal results with a high probability. Experimental studies show the relevance of the SPA data structure and the efficiency of the SPAMS algorithm on various datasets. Our contribution opens a promis- ing gateway, by using an automaton as a data structure for mining frequent sequential patterns in data streams