IS TEMPORAL OCCURRENCE OF DIATOMS RELATED TO SPECIES TRAITS,LOCAL ABUNDANCE,AND REGIONAL DISTRIBUTION?1

Water quality in streams typically changes fast, and sensitive biological indicators are crucial for monitoring these changes in water quality. Diatoms are widely used in biological stream quality assessment. However, interannual and intra‐annual variation of diatom population densities is large, often hampering a reliable quality assessment. We studied the importance of different species traits on temporal occurrence of diatom species. We also examined whether temporal occurrence of diatoms is related to species' local abundance or regional distribution using a data set collected in Finnish streams. According to the general linear model (GLM), temporal occurrence of diatoms increased with increasing local abundance and regional distribution. Species that occurred more frequently also had larger niche breadths and nonmarginal niche positions. In addition, cell size and attachment ability were positively related to species temporal occurrence. Our results imply that abundant and widely distributed species with large niches and ability to attach sustain persistent populations in varying environmental conditions typical for streams. We suggest that future studies could concentrate on monitoring these common (abundant) species when detecting the possible changes in the biological state of streams. We advise, however, to consider a relatively large number of species as many of the most common species may have low indicator values.     

Competition ofRhizobium strains in nodule-formation

Summary By the use of a sterile culture system experiments have been carried out with peas in order to elucidate to what extent inoculation with an ineffective bacterial strain effects the ability of an effective strain to produce further nodulation. It has been established that effective strains, when applied to the plant after the formation of the first nodules by an ineffective strain, altogether fail to form nodules or the nodulation is delayed very much. Differences are noted in this respect between different bacterial strains. The results can be explained easiest by assuming that the strain first entering the roots causes an immunity in the plant against later infections. Other possibilities for explanation are also discussed. The conception that the resistance would be due to the saturation of roots with nodules formed by the first strain which would inhibit further nodulation is not in accordance with the results recorded.A method has been introduced for cultivation of plants under sterile conditions by dividing the roots into two or more culture flasks. This method is of great value when the nutrient uptake of plants is elucidated. Successive inoculation with ineffective and effective strains gave varying results by this technique. The experiments seem to imply that if only a part of the root system is inoculated with an ineffective strain, immunity does not regularly occur against later infections by effective strains. This suggests that the immunity is a somewhat local phenomenon.     

Application of Mu in vitro transposition for high-precision mapping of protein–protein interfaces on a yeast two-hybrid platform

High-precision mapping of regions involved in protein–protein interfaces of interacting protein partners is an essential component on a path to understand various cellular functions. Transposon-based systems, particularly those involving in vitro reactions, offer exhaustive insertion mutant libraries and high-throughput platforms for many types of genetic analyses. We present here a genetic strategy to accurately map interacting protein regions at amino acid precision that is based on transposition-assisted construction, sampling, and analysis of a comprehensive insertion mutant library. The methodology integrates random pentapeptide mutagenesis of proteins, yeast two-hybrid screening, and high-resolution genetic footprinting. This straightforward strategy is general, and it provides a rapid and easy means to identify critical contact regions in proteins without the requirement of prior structural knowledge.     

Metacommunity Structure of Stream Insects across Three Hierarchical Spatial scales

A major challenge in community ecology is to understand the underlying factors driving metacommunity (i.e., a set of local communities connected through species dispersal) dynamics. However, little is known about the effects of varying spatial scale on the relative importance of environmental and spatial (i.e., dispersal related) factors in shaping metacommunities and on the relevance of different dispersal pathways. Using a hierarchy of insect metacommunities at three spatial scales (a small, within‐stream scale, intermediate, among‐stream scale, and large, among‐sub‐basin scale), we assessed whether the relative importance of environmental and spatial factors shaping metacommunity structure varies predictably across spatial scales, and tested how the importance of different dispersal routes vary across spatial scales. We also studied if different dispersal ability groups differ in the balance between environmental and spatial control. Variation partitioning showed that environmental factors relative to spatial factors were more important for community composition at the within‐stream scale. In contrast, spatial factors (i.e., eigenvectors from Moran's eigenvector maps) relative to environmental factors were more important at the among‐sub‐basin scale. These results indicate that environmental filtering is likely to be more important at the smallest scale with highest connectivity, while dispersal limitation seems to be more important at the largest scale with lowest connectivity. Community variation at the among‐stream and among‐sub‐basin scales were strongly explained by geographical and topographical distances, indicating that overland pathways might be the main dispersal route at the larger scales among more isolated sites. The relative effect of environmental and spatial factors on insect communities varied between low and high dispersal ability groups; this variation was inconsistent among three hierarchical scales. In sum, our study indicates that spatial scale, connectivity, and dispersal ability jointly shape stream metacommunities.     

Inflammatory Proteins in Plasma Are Associated with Severity of Alzheimer’s Disease

Markers of Alzheimer’s disease (AD) are being widely sought with a number of studies suggesting blood measures of inflammatory proteins as putative biomarkers. Here we report findings from a panel of 27 cytokines and related proteins in over 350 subjects with AD, subjects with Mild Cognitive Impairment (MCI) and elderly normal controls where we also have measures of longitudinal change in cognition and baseline neuroimaging measures of atrophy. In this study, we identify five inflammatory proteins associated with evidence of atrophy on MR imaging data particularly in whole brain, ventricular and entorhinal cortex measures. In addition, we observed six analytes that showed significant change (over a period of one year) in people with fast cognitive decline compared to those with intermediate and slow decline. One of these (IL-10) was also associated with brain atrophy in AD. In conclusion, IL-10 was associated with both clinical and imaging evidence of severity of disease and might therefore have potential to act as biomarker of disease progression.     

Shedding new light on the diet of Norwegian lemmings: DNA metabarcoding of stomach content

Lemmings are key herbivores in many arctic food webs, and their population dynamics have major impacts on the functioning of tundra systems. However, current knowledge of lemming diet is limited, hampering evaluation of lemming–vegetation interactions. This lack of knowledge is mainly due to methodological challenges, as previously used microhistological methods result in large proportions of poorly resolved plant taxa. We analyzed diets of Norwegian lemmings (Lemmus lemmus) in three different habitats using a new method, DNA metabarcoding of stomach contents. To achieve detailed information on ingested vascular plants, bryophytes, and fungi, we amplified short fragments of chloroplast DNA (for plants; P6 loop of the trnL intron) and nuclear ribosomal DNA (for fungi; ITS1-region). Our results revealed that lemming diets were dominated by grasses, mainly Avenella flexuosa, and mosses, mainly Dicranum spp., but that a variety of other food items were also eaten. Vascular plant composition of the diets differed between heath, meadow, and wetland habitats, whereas bryophyte composition did not. Also, a variety of fungal taxa were retrieved, but as most of the identified taxa belong to micromycetes, they were unlikely to be consumed as food. The role of fungi in the diet of lemmings remains to be investigated. We suggest that there may be substantial variation between habitats and regions in lemming diet.     

Predicting Progression of Alzheimer’s Disease Using Ordinal Regression

We propose a novel approach to predicting disease progression in Alzheimer’s disease (AD) – multivariate ordinal regression – which inherently models the ordered nature of brain atrophy spanning normal aging (CTL) to mild cognitive impairment (MCI) to AD. Ordinal regression provides probabilistic class predictions as well as a continuous index of disease progression – the ORCHID (Ordinal Regression Characteristic Index of Dementia) score. We applied ordinal regression to 1023 baseline structural MRI scans from two studies: the US-based Alzheimer’s Disease Neuroimaging Initiative (ADNI) and the European based AddNeuroMed program. Here, the acquired AddNeuroMed dataset was used as a completely independent test set for the ordinal regression model trained on the ADNI cohort providing an optimal assessment of model generalizability. Distinguishing CTL-like (CTL and stable MCI) from AD-like (MCI converters and AD) resulted in balanced accuracies of 82% (cross-validation) for ADNI and 79% (independent test set) for AddNeuroMed. For prediction of conversion from MCI to AD, balanced accuracies of 70% (AUC of 0.75) and 75% (AUC of 0.81) were achieved. The ORCHID score was computed for all subjects. We showed that this measure significantly correlated with MMSE at 12 months (ρ = –0.64, ADNI and ρ = –0.59, AddNeuroMed). Additionally, the ORCHID score can help fractionate subjects with unstable diagnoses (e.g. reverters and healthy controls who later progressed to MCI), moderately late converters (12–24 months) and late converters (24–36 months). A comparison with results in the literature and direct comparison with a binary classifier suggests that the performance of this framework is highly competitive.