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Fumonisin B1 is a mycotoxin produced by fungi of the genus Fusarium that frequently occurs on maize (Zea mays) and feeds containing it. The use of plant-based protein sources in feeds designed for aquaculture has increased due to their low costs compared with fishmeal. This trend has resulted in a global increase in feed formulations contaminated with mycotoxins, producing economical losses in aquaculture industry. The topic also causes concerns because of the potential health consequences that aquaculture products could have on human consumers. FB1 mainly disrupts sphingolipid metabolism and also has immune suppressive effects. Fish mycotoxicosis produces a wide range of symptoms from poor growth rate and weight gain to reproductive, immune, liver and kidney disorders that can lead to mortality. Despite its importance, very little is known about the effects of FB1 in Baltic salmon, Salmo salar. In this study growth performance, feed intake, mortality and liver histopathology of juvenile salmon exposed to FB1 doses 0, 1, 5, 10 or 20 mg/kg feed was evaluated. The hypothesis was that FB1 ingestion would reduce salmon growth, feed intake and would produce liver damage. At the end of the 10-week experiment no differences in the evaluated parameters were found. Species-specific differences in vulnerability because of variations in toxin metabolism could explain the results. However, due to the slow growth of fish during the trial additional research to confirm the results are suggested.
Denoising is often addressed via sparse coding with respect to an overcomplete dictionary. There are two main approaches when the dictionary is composed of translates of an orthonormal basis. The first, traditionally employed by techniques such as wavelet cycle spinning, separately seeks sparsity w.r.t. each translate of the orthonormal basis, solving multiple partial optimizations and obtaining a collection of sparse approximations of the noise-free image, which are aggregated together to obtain a final estimate. The second approach, recently employed by convolutional sparse representations, instead seeks sparsity over the entire dictionary via a global optimization. It is tempting to view the former approach as providing a suboptimal solution of the latter. In this letter, we analyze whether global sparsity is a desirable property, and under what conditions the global optimization provides a better solution to the denoising problem. In particular, our experimental analysis shows that the two approaches attain comparable performance in case of natural images and global optimization outperforms the simpler aggregation of partial estimates only when the image admits an extremely sparse representation. We explain this phenomenon by separately studying the bias and variance of these solutions, and by noting that the variance of the global solution increases very rapidly as the original signal becomes less and less sparse.
The automatic detection of anomalies, defined as patterns that are not encountered in representative set of normal images, is an important problem in industrial control and biomedical applications. We have shown that this problem can be successfully addressed by the sparse representation of individual image patches using a dictionary learned from a large set of patches extracted from normal images. Anomalous patches are detected as those for which the sparse representation on this dictionary exceeds sparsity or error tolerances. Unfortunately, this solution is not suitable for many real-world visual inspection-systems since it is not scale invariant: since the dictionary is learned at a single scale, patches in normal images acquired at a different magnification level might be detected as anomalous. We present an anomaly-detection algorithm that learns a dictionary that is invariant to a range of scale changes, and overcomes this limitation by use of an appropriate sparse coding stage. The algorithm was successfully tested in an industrial application by analyzing a dataset of Scanning Electron Microscope (SEM) images, which typically exhibit different magnification levels.