Summer distribution of semi-domesticated reindeer relative to a new wind-power plant

Wind-power plants (WPs) within reindeer (Rangifer tarandus tarandus) habitat may have negative effects on reindeer habitat use. Avoidance effects towards a WP were tested by comparing reindeer distributions on a peninsula where a WP was built in 2006 with a control peninsula without a WP. Distributions were measured by direct observations during construction period, and in four subsequent years, and limited faecal pellet group counts along transects before, during and after the WP construction (2005–2010). We predicted higher reindeer density in the control than the WP peninsula and at increasing distances from the WP when controlling for habitat quality. We found no avoidance effects from the WP, with significantly more reindeer in the WP than the control peninsula. Faecal pellet group data supported a lack of negative effects towards the WP after construction compared to before, while area within 100 m from the access road to the WP was avoided during the construction period and for 3 years afterwards. Reindeer avoided low-quality habitat both in the control and WP peninsulas. Our study indicates that WP development might have minor effects on habitat use if built in poor habitats, at least for semi-domestic reindeer. Our results cannot be used to infer effects of a WP built in higher-quality habitats or where large-scale movements are less restrictive than on a peninsula. Disturbance effects of human infrastructure likely are context-dependent, and management should thus be careful in planning of WPs to minimize adverse effects.     

Molecular modelling and simulations in cancer research

The complexity of cancer and the vast amount of experimental data available have made computer-aided approaches necessary. Biomolecular modelling techniques are becoming increasingly easier to use, whereas hardware and software are becoming better and cheaper. Cross-talk between theoretical and experimental scientists dealing with cancer-research from a molecular approach, however, is still uncommon. This is in contrast to other fields, such as amyloid-related diseases, where molecular modelling studies are widely acknowledged. The aim of this review paper is therefore to expose some of the more common approaches in molecular modelling to cancer scientists in simple terms, illustrating success stories while also revealing the limitations of computational studies at the molecular level.     

Changes in the Intestinal Microbiota of Wild Atlantic cod Gadus morhua L. Upon Captive Rearing

The commensal microbiota plays an important role in the well-being of the host organism, and it would be worthwhile to know the tenacious communities among them. Therefore, a study was undertaken to examine the changes in constitution of the intestinal microbiota of wild fish consequential to captivity. At first, the composition of intestinal microorganisms of Atlantic cod caught from the coastal area off Bod?, Norway, was examined. Thereafter, the changes in the bacterial community of the captive fish after offering them artificial feed or subjecting them to starvation were studied. The microbiota from the intestinal contents and wall segments were analyzed quantitatively by spread plate technique and DAPI staining and qualitatively by denaturing gradient gel electrophoresis. The study revealed that the counts of intestinal microbes in wild-caught Atlantic cod were not affected by captive rearing for 6?weeks, either when fed or when starved. However, the diversity of intestinal bacterial community was reduced in response to artificial feeding, whereas the change was restricted upon starvation.     

Reconstruction of molecular network evolution from cross‐sectional omics data

Cross‐sectional studies may shed light on the evolution of a disease like cancer through the comparison of patient traits among disease stages. This problem is especially challenging when a gene–gene interaction network needs to be reconstructed from omics data, and, in addition, the patients of each stage need not form a homogeneous group. Here, the problem is operationalized as the estimation of stage‐wise mixtures of Gaussian graphical models (GGMs) from high‐dimensional data. These mixtures are fitted by a (fused) ridge penalized EM algorithm. The fused ridge penalty shrinks GGMs of contiguous stages. The (fused) ridge penalty parameters are chosen through cross‐validation. The proposed estimation procedures are shown to be consistent and their performance in other respects is studied in simulation. The down‐stream exploitation of the fitted GGMs is outlined. In a data illustration the methodology is employed to identify gene–gene interaction network changes in the transition from normal to cancer prostate tissue.