BackgroundInfestations of the parasitic copepod Lepeophtheirus salmonis, commonly referred to as sea lice, represent a major challenge to commercial salmon aquaculture. Dependence on a limited number of theraputants to control such infestations has led to concerns of reduced sensitivity in some sea lice populations. This study investigates trends in the efficacy of the in-feed treatment emamectin benzoate in Scotland, the active ingredient most widely used across all salmon producing regions.Methodology/Principal FindingsStudy data were drawn from over 50 commercial Atlantic salmon farms on the west coast of Scotland between 2002 and 2006. An epi-informatics approach was adopted whereby available farm records, descriptive epidemiological summaries and statistical linear modelling methods were used to identify factors that significantly affect sea lice abundance following treatment with emamectin benzoate (SLICE®, Schering Plough Animal Health). The results show that although sea lice infestations are reduced following the application of emamectin benzoate, not all treatments are effective. Specifically there is evidence of variation across geographical regions and a reduction in efficacy over time.Conclusions/SignificanceReduced sensitivity and potential resistance to currently available medicines are constant threats to maintaining control of sea lice populations on Atlantic salmon farms. There is a need for on-going monitoring of emamectin benzoate treatment efficacy together with reasons for any apparent reduction in performance. In addition, strategic rotation of medicines should be encouraged and empirical evidence for the benefit of such strategies more fully evaluated.
In this paper, we present an audio-based event detection approach shown to be effective when applied to the Sports broadcast data. The main benefit of this approach is the ability to recognise patterns that indicate high levels of crowd response which can be correlated to key events. By applying Hidden Markov Model-based classifiers, where the predefined content classes are parameterised using Mel-Frequency Cepstral Coefficients, we were able to eliminate the need for defining a heuristic set of rules to determine event detection, thus avoiding a two-class approach shown not to be suitable for this problem. Experimentation indicated that this is an effective method for classifying crowd response in Soccer matches, thus providing a basis for automatic indexing and summarisation.
Human memory plays an important role in personal information management (PIM). Several scholars have noted that people re-find information based on what they remember and it has been shown that people adapt their management strategies to compensate for the limitations of memory. Nevertheless, little is known about what people tend to remember about their personal information and how they use their memories to re-find. The aim of this article is to increase our understanding of the role that memory plays in the process of re-finding personal information. Concentrating on email re-finding, we report on a user study that investigates what attributes of email messages participants remember when trying to re-find. We look at how the attributes change in different scenarios and examine the factors which impact on what is remembered.
The purpose of this paper is to examine how different aspects of an assessor's context, in particular their knowledge of a search topic, their interest in the search topic and their confidence in assessing relevance for a topic, affect the relevance judgements made and the assessor's ability to predict which documents they will assess as being relevant. This study found that each of the three factors (interest, knowledge and confidence) had an affect on how many documents were assessed as relevant and the balance between how many documents were marked as marginally or highly relevant. Also these factors are shown to affect an assessors' ability to predict what information they will finally mark as being relevant
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