A mutant of the peptide antibiotic nisin in which the dehydroalanine residue at position 5 has been replaced by an alanine has been produced and structurally characterized. It is shown to have activity very similar to that of wild-type nisin in inhibiting growth of Lactococcus lactis and Micrococcus luteus but is very much less active than nisin as an inhibitor of the outgrowth of spores of Bacillus subtilis. These observations, which parallel those of W. Liu and J. N. Hansen (Appl. Environ. Microbiol. 59:648-651, 1993) on the corresponding mutant of the related antibiotic subtilin, are discussed in terms of the mechanism(s) of action of these antibiotics.
In many real data science problems, it is common to encounter a domain mismatch between the training and testing datasets, which means that solutions designed for one may not transfer well to the other due to their differences. An example of such was in the BirdCLEF2021 Kaggle competition, where participants had to identify all bird species that could be heard in audio recordings. Thus, multi-label classifiers, capable of coping with domain mismatch, were required. In addition, classifiers needed to be resilient to a long-tailed (imbalanced) class distribution and weak labels. Throughout the competition, a diverse range of solutions based on convolutional neural networks were proposed. However, it is unclear how different solution components contribute to overall performance. In this work, we contextualise the problem with respect to the previously existing literature, analysing and discussing the choices made by the different participants. We also propose a modular solution architecture to empirically quantify the effects of different architectures. The results of this study provide insights into which components worked well for this challenge.
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