Profiling soil microbial communities influenced by reduced summer precipitation and farming system history

Soil bacteria and fungi are the basis of soil food webs and contribute to a wide range of essential soil functions in arable lands. Intense land use and climate change induced reductions in summer precipitation can have varying influences on abundance, composition, and activity of microbial communities with largely unknown consequences for soil functions and plant growth including crop yields. The impact of altered precipitation patterns on soil biodiversity and associated ecosystem functions is on top of the list of eight major research gaps identified by an expert group for the European Commission still, this relationship is rarely studied under field conditions

Measurement of Tau Filament Fragmentation Provides Insights into Prion-like Spreading.

The ordered assembly of amyloidogenic proteins causes a wide spectrum of common neurodegenerative diseases, including Alzheimer's and Parkinson's diseases. These diseases share common features with prion diseases, in which misfolded proteins can self-replicate and transmit disease across different hosts. Deciphering the molecular mechanisms that underlie the amplification of aggregates is fundamental for understanding how pathological deposits can spread through the brain and drive disease. Here, we used single-molecule microscopy to study the assembly and replication of tau at the single aggregate level. We found that tau aggregates have an intrinsic ability to amplify by filament fragmentation, and determined the doubling times for this replication process by kinetic modeling. We then simulated the spreading time for aggregates through the brain and found this to be in good agreement with both the observed time frame for spreading of pathological tau deposits in Alzheimer's disease and in experimental models of tauopathies. With this work we begin to understand the physical parameters that govern the spreading rates of tau and other amyloids through the human brain

Driving Soils to Change: Tyre Particles Modulate Microbial-Mediated Soil Functions & Nutrient Status in Vegetable Crops

Driving Soils to Change: Tyre Particles Modulate Microbial-Mediated Soil Functions & Nutrient Status in Vegetable Crop

The seaweeds Fucus vesiculosus and Ascophyllum nodosum are significant contributors to coastal iodine emissions

Based on the results of a pilot study in 2007, which found high mixing ratios of molecular iodine (I2) above the intertidal macroalgae (seaweed) beds at Mweenish Bay (Ireland), we extended the study to nine different locations in the vicinity of Mace Head Atmospheric Research Station on the west coast of Ireland during a field campaign in 2009. The mean values of \chem{I_2} mixing ratio found above the macroalgae beds at nine different locations ranged from 104 to 393 ppt, implying a high source strength of I2. Such mixing ratios are sufficient to result in photochemically driven coastal new-particle formation events. Mixing ratios above the Ascophyllum nodosum and Fucus vesiculosus beds increased with exposure time: after 6 h exposure to ambient air the mixing ratios were one order of magnitude higher than those initially present. This contrasts with the emission characteristics of Laminaria digitata, where most I2 was emitted within the first half hour of exposure. Discrete in situ measurements (offline) of I2 emission from ambient air-exposed chamber experiments of L. digitata, A. nodosum and F. vesiculosus substantially supported the field observations. Further online and time-resolved measurements of the I2 emission from O3-exposed macroalgal experiments in a chamber confirmed the distinct I2 emission characteristics of A. nodosum and F. vesiculosus compared to those of L. digitata. The emission rates of A. nodosum and F. vesiculosus were comparable to or even higher than L. digitata after the initial exposure period of ~20–30 min. We suggest that A. nodosum and F. vesiculosus may provide an important source of photolabile iodine in the coastal boundary layer and that their impact on photochemistry and coastal new-particle formation should be reevaluated in light of their longer exposure at low tide and their widespread distribution

Different soluble aggregates of Aβ42 can give rise to cellular toxicity through different mechanisms.

Protein aggregation is a complex process resulting in the formation of heterogeneous mixtures of aggregate populations that are closely linked to neurodegenerative conditions, such as Alzheimer's disease. Here, we find that soluble aggregates formed at different stages of the aggregation process of amyloid beta (Aβ42) induce the disruption of lipid bilayers and an inflammatory response to different extents. Further, by using gradient ultracentrifugation assay, we show that the smaller aggregates are those most potent at inducing membrane permeability and most effectively inhibited by antibodies binding to the C-terminal region of Aβ42. By contrast, we find that the larger soluble aggregates are those most effective at causing an inflammatory response in microglia cells and more effectively inhibited by antibodies targeting the N-terminal region of Aβ42. These findings suggest that different toxic mechanisms driven by different soluble aggregated species of Aβ42 may contribute to the onset and progression of Alzheimer's disease.This study is supported by the Marie-Curie Individual Fellowship programme (S.D.), EPSRC Studentship (D.C.W.), Boehringer Ingelheim Fonds (P.F.), Studienstiftung des deutschen Volkes (P.F.), Senior Research Fellowship from the Alzheimer's Society, Grant Number 317, AS-SF-16-003, UK (F.A.A), Swiss National Fondation for Science and Darwin College grant number P2ELP2_162116 and P300P2_171219 (F.S.R.), Borysiewicz Biomedical Fellowship from the University of Cambridge(P.S), the UK Biotechnology and Biochemical Sciences Research Council (C.M.D.); the Wellcome Trust (C.M.D) the Cambridge Centre for Misfolding Diseases (P.F., F.A.A., P.S., C.M.D., and M.V.) and the European Research Council Grant Number 669237 (D.K.) and the Royal Society (D.K.)

Compared to conventional, ecological intensive management promotes beneficial proteolytic soil microbial communities for agro-ecosystem functioning under climate change-induced rain regimes

Projected climate change and rainfall variability will affect soil microbial communities, biogeochemical cycling and agriculture. Nitrogen (N) is the most limiting nutrient in agroecosystems and its cycling and availability is highly dependent on microbial driven processes. In agroecosystems, hydrolysis of organic nitrogen (N) is an important step in controlling soil N availability. We analyzed the effect of management (ecological intensive vs. conventional intensive) on N-cycling processes and involved microbial communities under climate change-induced rain regimes. Terrestrial model ecosystems originating from agroecosystems across Europe were subjected to four different rain regimes for 263 days. Using structural equation modelling we identified direct impacts of rain regimes on N-cycling processes, whereas N-related microbial communities were more resistant. In addition to rain regimes, management indirectly affected N-cycling processes via modifications of N-related microbial community composition. Ecological intensive management promoted a beneficial N-related microbial community composition involved in N-cycling processes under climate change-induced rain regimes. Exploratory analyses identified phosphorus-associated litter properties as possible drivers for the observed management effects on N-related microbial community composition. This work provides novel insights into mechanisms controlling agro-ecosystem functioning under climate change

Adaptation and visual search in mammographic images

Abstract Radiologists face the visually challenging task of detecting suspicious features within the complex and noisy backgrounds characteristic of medical images. We used a search task to examine whether the salience of target features in x-ray mammograms could be enhanced by prior adaptation to the spatial structure of the images. The observers were not radiologists, and thus had no diagnostic training with the im-ages. The stimuli were randomly selected sections from nor-mal mammograms previously classified with BIRADS Den-sity scores of Bfatty ^ versus Bdense, ^ corresponding to differ-ences in the relative quantities of fat versus fibroglandular tissue. These categories reflect conspicuous differences in vi-sual texture, with dense tissue being more likely to obscure lesion detection. The targets were simulated masses corre-sponding to bright Gaussian spots, superimposed by adding the luminance to the background. A single target was random-ly added to each image, with contrast varied over five levels so that they varied from difficult to easy to detect. Reaction times were measured for detecting the target location, before or after adapting to a gray field or to random sequences of a different set of dense or fatty images. Observers were faster at detecting the targets in either dense or fatty images after adapting to the specific background type (dense or fatty) that they were searching within. Thus, the adaptation led to a facilitation of search performance that was selective for the background tex-ture. Our results are consistent with the hypothesis that adap-tation allows observers to more effectively suppress the spe-cific structure of the background, thereby heightening visual salience and search efficiency

Ranked Adjusted Rand: integrating distance and partition information in a measure of clustering agreement

BACKGROUND: Biological information is commonly used to cluster or classify entities of interest such as genes, conditions, species or samples. However, different sources of data can be used to classify the same set of entities and methods allowing the comparison of the performance of two data sources or the determination of how well a given classification agrees with another are frequently needed, especially in the absence of a universally accepted "gold standard" classification. RESULTS: Here, we describe a novel measure – the Ranked Adjusted Rand (RAR) index. RAR differs from existing methods by evaluating the extent of agreement between any two groupings, taking into account the intercluster distances. This characteristic is relevant to evaluate cases of pairs of entities grouped in the same cluster by one method and separated by another. The latter method may assign them to close neighbour clusters or, on the contrary, to clusters that are far apart from each other. RAR is applicable even when intercluster distance information is absent for both or one of the groupings. In the first case, RAR is equal to its predecessor, Adjusted Rand (HA) index. Artificially designed clusterings were used to demonstrate situations in which only RAR was able to detect differences in the grouping patterns. A study with larger simulated clusterings ensured that in realistic conditions, RAR is effectively integrating distance and partition information. The new method was applied to biological examples to compare 1) two microbial typing methods, 2) two gene regulatory network distances and 3) microarray gene expression data with pathway information. In the first application, one of the methods does not provide intercluster distances while the other originated a hierarchical clustering. RAR proved to be more sensitive than HA in the choice of a threshold for defining clusters in the hierarchical method that maximizes agreement between the results of both methods. CONCLUSION: RAR has its major advantage in combining cluster distance and partition information, while the previously available methods used only the latter. RAR should be used in the research problems were HA was previously used, because in the absence of inter cluster distance effects it is an equally effective measure, and in the presence of distance effects it is a more complete one