Dietary n-6/n-3 LC-PUFA ratio, temperature and time interactions on nutrients and fatty acids digestibility in Atlantic salmon

The objective of the present study was to evaluate the effects of altered dietary n-3/n-6 LC-PUFA ratio, adaptation to diet over time, different water temperatures, and their interactions on nutrients and fatty acids digestibility in juvenile Atlantic salmon. Three experimental diets were formulated to be identical, with the only exception of the ratio of eicosapentaenoic acid (EPA, 20:5n-3) to arachidonic acid (ARA, 20:4n-6), and fed to triplicate groups of juvenile Atlantic salmon (Salmo salar) of 55. g initial body weight. Fish were reared in a fully controlled recirculating aquaculture system, fed to apparent satiety twice daily and kept at 10. °C and for an initial period of 100. days, and faeces were collected for digestibility estimation. Then, half of the fish of each experimental tank were moved to a separate system, where the water temperature was gradually increased up to 20. °C. Fish were maintained in the two systems for an additional period of 50. days, and faeces were collected for digestibility estimation from both groups of fish at the two water temperatures. This study concluded that dietary treatments and time had only minor effects, whereas environmental temperature resulted in modified digestibility values, with increased nutrient digestibility with increasing temperature. Varying EPA/ARA ratio in the diet had only minor direct effects on digestibility, with no direct effect on overall nutrients digestibility, and fundamentally only statistically significant effects in the fatty acid digestibility of EPA and ARA themselves. Because of current increasing pressure for more efficient fish oil replacement strategies, increasing interest in dietary ARA in aquafeed and increasing relevance and occurrence of sub-optimal rearing temperature in commercial aquaculture, this study can be considered to be important as it provided a series of fundamental information, which are envisaged to be useful towards addressing these constraints and possible nutritional remedial strategies.

Efficient and accurate set-based registration of time-separated aerial images

This paper addresses the task of time-separated aerial image registration. The ability to solve this problem accurately and reliably is important for a variety of subsequent image understanding applications. The principal challenge lies in the extent and nature of transient appearance variation that a land area can undergo, such as that caused by the change under illumination conditions, seasonal variations, or the occlusion by non-persistent objects (people, cars). Our work introduces several major novelties (i) unlike previous work on aerial image registration, we approach the problem using a set-based paradigm; (ii) we show how image space local, pair-wise constraints can be used to enforce a globally good registration using a constraints graph structure; (iii) we show how a simple holistic representation derived from raw aerial images can be used as a basic building block of the constraints graph in a manner which achieves both high registration accuracy and speed; (iv) lastly, we introduce a new and, to the best of our knowledge, the only data corpus suitable for the evaluation of set-based aerial image registration algorithms. Using this data set, we demonstrate (i) that the proposed method outperforms the state-of-the-art for pair-wise registration already, achieving greater accuracy and reliability, while at the same time reducing the computational cost of the task and (ii) that the increase in the number of available images in a set consistently reduces the average registration error, with a major difference already for a single additional image.

Small-variance asymptotics for bayesian nonparametric models with constraints

The users often have additional knowledge when Bayesian nonparametric models (BNP) are employed, e.g. for clustering there may be prior knowledge that some of the data instances should be in the same cluster (must-link constraint) or in different clusters (cannot-link constraint), and similarly for topic modeling some words should be grouped together or separately because of an underlying semantic. This can be achieved by imposing appropriate sampling probabilities based on such constraints. However, the traditional inference technique of BNP models via Gibbs sampling is time consuming and is not scalable for large data. Variational approximations are faster but many times they do not offer good solutions. Addressing this we present a small-variance asymptotic analysis of the MAP estimates of BNP models with constraints. We derive the objective function for Dirichlet process mixture model with constraints and devise a simple and efficient K-means type algorithm. We further extend the small-variance analysis to hierarchical BNP models with constraints and devise a similar simple objective function. Experiments on synthetic and real data sets demonstrate the efficiency and effectiveness of our algorithms.