University of Iceland

About: University of Iceland is a education organization based out in Reykjavik, Suðurnes, Iceland. It is known for research contribution in the topics: Population & Genome-wide association study. The organization has 5423 authors who have published 16199 publications receiving 694762 citations. The organization is also known as: Háskóli Íslands.

Isolation and identification of antimicrobial components from the epidermal mucus of Atlantic cod (Gadus morhua)

Abstract: The epidermal mucus of fish species has been found to contain antimicrobial proteins and peptides, which is of interest in regard to fish immunity. An acidic extract from the epidermal mucus of the Atlantic cod (Gadus morhua) was found to exhibit antimicrobial activity against Bacillus megaterium, Escherichia coli and Candida albicans. This activity varied significantly when salt was added to the antimicrobial assay, and was eliminated by pepsin digestion. No lysozyme activity was detected in the extract. By using weak cationic exchange chromatography together with reversed-phase chromatography, and monitoring the antimicrobial activity, we have isolated four cationic proteins from the mucus extract. Using N-terminal and C-terminal amino acid sequence analysis, together with MS, the antimicrobial proteins were identified as histone H2B (13 565 Da), ribosomal protein L40 (6397 Da), ribosomal protein L36A (12 340 Da) and ribosomal protein L35 (14 215 Da). The broad spectra of antimicrobial activities in the cod mucus and the characterization of four antimicrobial polypeptides suggest that mucus compounds contribute to the innate host defence of cod.

Automatic Design of Convolutional Neural Network for Hyperspectral Image Classification

Abstract: Hyperspectral image (HSI) classification is a core task in the remote sensing community, and recently, deep learning-based methods have shown their capability of accurate classification of HSIs. Among the deep learning-based methods, deep convolutional neural networks (CNNs) have been widely used for the HSI classification. In order to obtain a good classification performance, substantial efforts are required to design a proper deep learning architecture. Furthermore, the manually designed architecture may not fit a specific data set very well. In this paper, the idea of automatic CNN for the HSI classification is proposed for the first time. First, a number of operations, including convolution, pooling, identity, and batch normalization, are selected. Then, a gradient descent-based search algorithm is used to effectively find the optimal deep architecture that is evaluated on the validation data set. After that, the best CNN architecture is selected as the model for the HSI classification. Specifically, the automatic 1-D Auto-CNN and 3-D Auto-CNN are used as spectral and spectral–spatial HSI classifiers, respectively. Furthermore, the cutout is introduced as a regularization technique for the HSI spectral–spatial classification to further improve the classification accuracy. The experiments on four widely used hyperspectral data sets (i.e., Salinas, Pavia University, Kennedy Space Center, and Indiana Pines) show that the automatically designed data-dependent CNNs obtain competitive classification accuracy compared with the state-of-the-art methods. In addition, the automatic design of the deep learning architecture opens a new window for future research, showing the huge potential of using neural architectures’ optimization capabilities for the accurate HSI classification.

Labeling and Cumulative Disadvantage: The Impact of Formal Police Intervention on Life Chances and Crime During Emerging Adulthood

Abstract: Research in labeling theory has been revived recently, particularly in relation to the effect of labeling on critical noncriminal outcomes that potentially exacerbate involvement in crime. This study partakes in that revitalization by examining direct and indirect effects of police intervention in the lives of adolescents who were followed into their 30s. The authors find that early police intervention is indirectly related to drug use at the ages of 29 to 31, as well as unemployment and welfare receipt. Given that such effects were found some 15 years after the labeling event, on criminal and noncriminal outcomes, and after controlling for intraindividual factors, the authors conclude that the labeling perspective is still relevant within a developmental framework.