Impacts of eriophyoid gall mites on arctic willow in a rapidly changing Arctic

Arctic plants and herbivores are subject to ongoing climatic changes that are more rapid and extreme than elsewhere on the planet, and thus it is pivotal to understand the arctic plant-herbivore interactions in a global change context. We examined how infestation by an eriophyoid gall mite affects the circumpolar shrub Salix arctica, and how the effects vary across vegetation types. Specifically, we compared multiple leaf characteristics (leaf area, biomass, nutrient levels, δ¹⁵N and δ¹³C, and stress and performance of the photosynthetic apparatus) of infested leaves to those of un-infested leaves. Furthermore, we examined how altered environmental conditions, here experimentally manipulated levels of temperature, water and nutrients, shading, and UV-B radiation, affect the prevalence, density, and intensity of gall mite infestation and its impacts on S. arctica. Infested leaves were smaller in area and biomass and had lower nitrogen and carbon pools. However, their carbon concentration was higher, possibly because the galls acted as carbon sinks. The smaller photosynthetic area and lower nutrient content caused increased stress on the photosynthetic apparatus in infested leaves. The remaining leaf tissue responded with a higher photosynthetic performance, although there were indications of a general reduction in photosynthesis. Female leaves were more affected than male leaves. The experimental manipulations of environmental conditions did not affect the gall prevalence, density, or intensity on S. arctica leaves. Rather, plants responded positively to the treatments, reducing the effects of the galls to in-significance. This suggests a higher tolerance and defense against gall mites under future climate conditions.     

Techniques and applications of NMR to membrane proteins (Review)

The fact that membrane proteins are notoriously difficult to analyse using standard protocols for atomic-resolution structure determination methods have motivated adaptation of these techniques to membrane protein studies as well as development of new technologies. With this motivation, liquid-state nuclear magnetic resonance (NMR) has recently been used with success for studies of peptides and membrane proteins in detergent micelles, and solid-state NMR has undergone a tremendous evolution towards characterization of membrane proteins in native membrane and oriented phospholipid bilayers. In this mini-review, we describe some of the technological challenges behind these efforts and provide examples on their use in membrane biology.     

Diffusion Retardation by Binding of Tobramycin in an Alginate Biofilm Model

Microbial cells embedded in a self-produced extracellular biofilm matrix cause chronic infections, e. g. by Pseudomonas aeruginosa in the lungs of cystic fibrosis patients. The antibiotic killing of bacteria in biofilms is generally known to be reduced by 100–1000 times relative to planktonic bacteria. This makes such infections difficult to treat. We have therefore proposed that biofilms can be regarded as an independent compartment with distinct pharmacokinetics. To elucidate this pharmacokinetics we have measured the penetration of the tobramycin into seaweed alginate beads which serve as a model of the extracellular polysaccharide matrix in P. aeruginosa biofilm. We find that, rather than a normal first order saturation curve, the concentration of tobramycin in the alginate beads follows a power-law as a function of the external concentration. Further, the tobramycin is observed to be uniformly distributed throughout the volume of the alginate bead. The power-law appears to be a consequence of binding to a multitude of different binding sites. In a diffusion model these results are shown to produce pronounced retardation of the penetration of tobramycin into the biofilm. This filtering of the free tobramycin concentration inside biofilm beads is expected to aid in augmenting the survival probability of bacteria residing in the biofilm.     

Real-Time Subject-Independent Pattern Classification of Overt and Covert Movements from fNIRS Signals

Recently, studies have reported the use of Near Infrared Spectroscopy (NIRS) for developing Brain–Computer Interface (BCI) by applying online pattern classification of brain states from subject-specific fNIRS signals. The purpose of the present study was to develop and test a real-time method for subject-specific and subject-independent classification of multi-channel fNIRS signals using support-vector machines (SVM), so as to determine its feasibility as an online neurofeedback system. Towards this goal, we used left versus right hand movement execution and movement imagery as study paradigms in a series of experiments. In the first two experiments, activations in the motor cortex during movement execution and movement imagery were used to develop subject-dependent models that obtained high classification accuracies thereby indicating the robustness of our classification method. In the third experiment, a generalized classifier-model was developed from the first two experimental data, which was then applied for subject-independent neurofeedback training. Application of this method in new participants showed mean classification accuracy of 63% for movement imagery tasks and 80% for movement execution tasks. These results, and their corresponding offline analysis reported in this study demonstrate that SVM based real-time subject-independent classification of fNIRS signals is feasible. This method has important applications in the field of hemodynamic BCIs, and neuro-rehabilitation where patients can be trained to learn spatio-temporal patterns of healthy brain activity.     

Cortical Thickness,Surface Area and Subcortical Volume Differentially Contribute to Cognitive Heterogeneity in Parkinson’s Disease

Parkinson’s disease (PD) is often associated with cognitive deficits, although their severity varies considerably between patients. Recently, we used voxel-based morphometry (VBM) to show that individual differences in gray matter (GM) volume relate to cognitive heterogeneity in PD. VBM does, however, not differentiate between cortical thickness (CTh) and surface area (SA), which might be independently affected in PD. We therefore re-analyzed our cohort using the surface-based method FreeSurfer, and investigated (i) CTh, SA, and (sub)cortical GM volume differences between 93 PD patients and 45 matched controls, and (ii) the relation between these structural measures and cognitive performance on six neuropsychological tasks within the PD group. We found cortical thinning in PD patients in the left pericalcarine gyrus, extending to cuneus, precuneus and lingual areas and left inferior parietal cortex, bilateral rostral middle frontal cortex, and right cuneus, and increased cortical surface area in the left pars triangularis. Within the PD group, we found negative correlations between (i) CTh of occipital areas and performance on a verbal memory task, (ii) SA and volume of the frontal cortex and visuospatial memory performance, and, (iii) volume of the right thalamus and scores on two verbal fluency tasks. Our primary findings illustrate that i) CTh and SA are differentially affected in PD, and ii) VBM and FreeSurfer yield non-overlapping results in an identical dataset. We argue that this discrepancy is due to technical differences and the subtlety of the PD-related structural changes.