Overview of Seafood Research at Ashtown Food Research Centre (1990 - 2007)

End of project reportIn recent years, the Irish seafood industry has faced stringent quotas and dwindling fish stocks. The introduction of fish farming added a new dimension but falling prices also created difficulties for this sector. However, the recent report of the Seafood Industry Strategy Group on ‘Steering a New Course’ and the Sea Change Programme of the Marine Institute will add new impetus to the industry. The current report summarises R&D on seafood conducted at Ashtown Food Research Centre (AFRC) in the period 1990-2007 and represents a major portion of seafood R&D conducted nationally during that period.Thanks are also extended to the European Union (and especially the SEAFOODplus project), Bord Iascaigh Mhara, Marine Institute, Enterprise Ireland and various seafood companies for their support and part-funding of elements of this research

An evaluation of energy expenditure estimation by three activity monitors

This is an Author's Accepted Manuscript of an article published in European Journal of Sport Science, 13(6), 681 - 688, 2013 [date of publication] [copyright Taylor & Francis], available online at: http://www.tandfonline.com/ 10.1080/17461391.2013.776639.A comparative evaluation of the ability of activity monitors to predict energy expenditure (EE) is necessary to aid in the investigation of the effect of EE on health. The purpose of this study was to validate and compare the RT3, the SWA and the IDEEA at measuring EE in adults and children. Twenty-six adults and 22 children completed a resting metabolic rate (RMR) test and performed four treadmill activities at 3 km.h−1, 6 km.h−1, 6 km.h−1 at a 10% incline, 9 km.h−1. EE was assessed throughout the protocol by the RT3, the SWA and the IDEEA. Indirect calorimetry (IC) was used as a criterion measure of EE against which each monitor was compared. Mean bias was assessed by subtracting EE from IC from EE from each monitor for each activity. Limit of agreement plots were used to assess the agreement between each monitor and IC. Limits of agreement for resting EE were narrowest for the RT3 for adults and children. Although the IDEEA displayed the smallest mean bias between measures at 3 km.h−1, 6 km.h−1 and 9 km.h−1 in adults and children, the SWA agreed closest with IC at 6 km.h−1, 6 km.h−1 at a 10% incline and 9 km.h−1. Limits of agreement were closest for the SWA at 9 km.h−1 in adults representing 42% of the overall mean EE. Although the RT3 provided the best estimate of resting EE in adults and children, the SWA provided the most accurate estimate of EE across a range of physical activity intensities

Epitope profiling via mixture modeling of ranked data

We propose the use of probability models for ranked data as a useful alternative to a quantitative data analysis to investigate the outcome of bioassay experiments, when the preliminary choice of an appropriate normalization method for the raw numerical responses is difficult or subject to criticism. We review standard distance-based and multistage ranking models and in this last context we propose an original generalization of the Plackett-Luce model to account for the order of the ranking elicitation process. The usefulness of the novel model is illustrated with its maximum likelihood estimation for a real data set. Specifically, we address the heterogeneous nature of experimental units via model-based clustering and detail the necessary steps for a successful likelihood maximization through a hybrid version of the Expectation-Maximization algorithm. The performance of the mixture model using the new distribution as mixture components is compared with those relative to alternative mixture models for random rankings. A discussion on the interpretation of the identified clusters and a comparison with more standard quantitative approaches are finally provided.Comment: (revised to properly include references

Measuring sexual interest using a pictorial modified Stroop task, a pictorial Implicit Association Test, and a Choice Reaction Time task

Tasks that can successfully measure sexual interest have utility in forensic settings. Prior to use with problematic sexual interest however, work is needed in validating such tasks. This study focused on the measurement of non-deviant sexual interest. Eleven gay and fourteen straight participants each completed a pictorial Implicit Association Test (IAT), a pictorial modified Stroop task (P-MST) and a Choice Reaction Time (CRT) task. Each task was designed to tap into the sexual interest of participants. Stimuli were of males and females in bathing suits along with control images and sexual and non-sexual words. The IAT was most successful in differentiating between gay and straight participants. The P-MST also performed well, though the task’s position in the battery of tasks seemed to affect the results. The CRT tasks did not successfully show group differences. Theoretical and methodological implications of the effectiveness of the three tasks in tapping into sexual interest are discussed

Model Based Clustering for Mixed Data: clustMD

A model based clustering procedure for data of mixed type, clustMD, is developed using a latent variable model. It is proposed that a latent variable, following a mixture of Gaussian distributions, generates the observed data of mixed type. The observed data may be any combination of continuous, binary, ordinal or nominal variables. clustMD employs a parsimonious covariance structure for the latent variables, leading to a suite of six clustering models that vary in complexity and provide an elegant and unified approach to clustering mixed data. An expectation maximisation (EM) algorithm is used to estimate clustMD; in the presence of nominal data a Monte Carlo EM algorithm is required. The clustMD model is illustrated by clustering simulated mixed type data and prostate cancer patients, on whom mixed data have been recorded

Improved Relation Extraction with Feature-Rich Compositional Embedding Models

Compositional embedding models build a representation (or embedding) for a linguistic structure based on its component word embeddings. We propose a Feature-rich Compositional Embedding Model (FCM) for relation extraction that is expressive, generalizes to new domains, and is easy-to-implement. The key idea is to combine both (unlexicalized) hand-crafted features with learned word embeddings. The model is able to directly tackle the difficulties met by traditional compositional embeddings models, such as handling arbitrary types of sentence annotations and utilizing global information for composition. We test the proposed model on two relation extraction tasks, and demonstrate that our model outperforms both previous compositional models and traditional feature rich models on the ACE 2005 relation extraction task, and the SemEval 2010 relation classification task. The combination of our model and a log-linear classifier with hand-crafted features gives state-of-the-art results.Comment: 12 pages for EMNLP 201

Adding Value To Under utilised Fish Species

End of project reportTightening fish quotas and supply shortages for conventional species are causing major difficulties for both fishermen and seafood processors. There is a need, therefore, to explore the potential of underutilised fish species both as fillets or portions and as added-value products. The current project at Ashtown Food Research Centre (AFRC) addressed this issue for a number of underutilised species via (a) sous vide processing (with savoury sauces),(b)marinating (salt- and sugar-based marinades) and (c) via a combination of freeze-chilling and modified atmosphere packaging (MAP).A range of physico-chemical and sensory tests was conducted on the products and their shelf-life status was also determined.National Development Plan (NDP