Characterization of serine proteinase expression in agaricus bisporus and coprinopsis cinerea by using green fluorescent protein and the A. bisporus SPR1 Promoter

The Agaricus bisporus serine proteinase 1 (SPR1) appears to be significant in both mycelial nutrition and senescence of the fruiting body. We report on the construction of an SPR promoter::green fluorescent protein (GFP) fusion cassette, pGreen_hph1_SPR_GFP, for the investigation of temporal and developmental expression of SPR1 in homobasidiomycetes and to determine how expression is linked to physiological and environmental stimuli. Monitoring of A. bisporus pGreen_hph1_SPR_GFP transformants on media rich in ammonia or containing different nitrogen sources demonstrated that SPR1 is produced in response to available nitrogen. In A. bisporus fruiting bodies, GFP activity was localized to the stipe of postharvest senescing sporophores. pGreen_hph1_SPR_GFP was also transformed into the model basidiomycete Coprinopsis cinerea. Endogenous C. cinerea proteinase activity was profiled during liquid culture and fruiting body development. Maximum activity was observed in the mature cap, while activity dropped during autolysis. Analysis of the C. cinerea genome revealed seven genes showing significant homology to the A. bisporus SPR1 and SPR2 genes. These genes contain the aspartic acid, histidine, and serine residues common to serine proteinases. Analysis of the promoter regions revealed at least one CreA and several AreA regulatory motifs in all sequences. Fruiting was induced in C. cinerea dikaryons, and fluorescence was determined in different developmental stages. GFP expression was observed throughout the life cycle, demonstrating that serine proteinase can be active in all stages of C. cinerea fruiting body development. Serine proteinase expression (GFP fluorescence) was most concentrated during development of young tissue, which may be indicative of high protein turnover during cell differentiatio

Functional analyses of <i>Agaricus bisporus </i>Serine Proteinase 1 (SPR1) reveals a role in utilisation of humic rich substrates and adaptation to the leaf-litter ecological niche

Agaricus bisporus is a secondary decomposer fungus and an excellent model for the adaptation, persistence and growth of fungi in humic‐rich environments such as soils of temperate woodland and pastures. The A. bisporus serine proteinase SPR1 is induced by humic acids and is highly expressed during growth on compost. Three Spr1 gene silencing cassettes were constructed around sense, antisense and non‐translatable‐stop strategies (pGRsensehph, pGRantihph and pGRstophph). Transformation of A. bisporus with these cassettes generated cultures showing a reduction in extracellular proteinase activity as demonstrated by the reduction, or abolition, of a clearing zone on plate‐based bioassays. These lines were then assessed by detailed enzyme assay, RT‐qPCR and fruiting. Serine proteinase activity in liquid cultures was reduced in 83% of transformants. RT‐qPCR showed reduced Spr1 mRNA levels in all transformants analysed, and these correlated with reduced enzyme activity. When fruiting was induced, highly‐silenced transformant AS5 failed to colonize the compost, whilst for those that did colonize the compost, 60% gave a reduction in mushroom yield. Transcriptional, biochemical and developmental observations, demonstrate that SPR1 has an important role in nutrient acquisition in compost and that SPR1 is a key enzyme in the adaptation of Agaricus to the humic‐rich ecological niche formed during biomass degradation

Possibility between earthquake and explosion seismogram differentiation by discrete stochastic non-Markov processes and local Hurst exponent analysis

The basic purpose of the paper is to draw the attention of researchers to new possibilities of differentiation of similar signals having different nature. One of examples of such kind of signals is presented by seismograms containing recordings of earthquakes (EQ's) and technogenic explosions (TE's). We propose here a discrete stochastic model for possible solution of a problem of strong EQ's forecasting and differentiation of TE's from the weak EQ's. Theoretical analysis is performed by two independent methods: with the use of statistical theory of discrete non-Markov stochastic processes (Phys. Rev. E62,6178 (2000)) and the local Hurst exponent. Time recordings of seismic signals of the first four dynamic orthogonal collective variables, six various plane of phase portrait of four dimensional phase space of orthogonal variables and the local Hurst exponent have been calculated for the dynamic analysis of the earth states. The approaches, permitting to obtain an algorithm of strong EQ's forecasting and to differentiate TE's from weak EQ's, have been developed.Comment: REVTEX +12 ps and jpg figures. Accepted for publication in Phys. Rev. E, December 200

Barriers and enablers of physical activity engagement for patients with COPD in primary care

Maria-Christina Kosteli,1 Nicola R Heneghan,1 Carolyn Roskell,1 Sarah E Williams,1 Peymane Adab,2 Andrew P Dickens,2 Alexandra Enocson,2 David A Fitzmaurice,2 Kate Jolly,2 Rachel Jordan,2 Sheila Greenfield,2 Jennifer Cumming1 1School of Sport, Exercise and Rehabilitation Sciences, 2Institute of Applied Health Research, University of Birmingham, Edgbaston, Birmingham, UK Background: Given that physical activity (PA) has a positive impact on COPD symptoms and prognosis, this study examined the factors that both encourage and limit participation in PA for individuals with COPD in a primary care setting from the perspective of social cognitive theory.Methods: A purposive sample of 26 individuals with a range of COPD severity (age range: 50&ndash;89&nbsp;years; males =15) were recruited from primary care to participate in one of four focus groups. Thematic analysis was undertaken to identify key concepts related to their self-efficacy beliefs.Results: Several barriers and enablers closely related to self-efficacy beliefs and symptom severity were identified. The main barriers were health related (fatigue, mobility problems, breathing issues caused by the weather), psychological (embarrassment, fear, frustration/disappointment), attitudinal (feeling in control of their condition, PA perception, older age perception), and motivational. The main enabling factors were related to motivation (autonomous or controlled), attitudes, self-regulation, and performance accomplishments.Clinical implications: When designing interventions for individuals with COPD, it is important to understand the patient-specific social cognitive influences on PA participation. This information can then inform individually tailored management planning. Keywords: COPD, social cognitive theory, self-efficacy, barriers, enablers, primary car

Nonstationary Stochastic Resonance in a Single Neuron-Like System

Stochastic resonance holds much promise for the detection of weak signals in the presence of relatively loud noise. Following the discovery of nondynamical and of aperiodic stochastic resonance, it was recently shown that the phenomenon can manifest itself even in the presence of nonstationary signals. This was found in a composite system of differentiated trigger mechanisms mounted in parallel, which suggests that it could be realized in some elementary neural networks or nonlinear electronic circuits. Here, we find that even an individual trigger system may be able to detect weak nonstationary signals using stochastic resonance. The very simple modification to the trigger mechanism that makes this possible is reminiscent of some aspects of actual neuron physics. Stochastic resonance may thus become relevant to more types of biological or electronic systems injected with an ever broader class of realistic signals.Comment: Plain Latex, 7 figure

Multifractal characterization of stochastic resonance

We use a multifractal formalism to study the effect of stochastic resonance in a noisy bistable system driven by various input signals. To characterize the response of a stochastic bistable system we introduce a new measure based on the calculation of a singularity spectrum for a return time sequence. We use wavelet transform modulus maxima method for the singularity spectrum computations. It is shown that the degree of multifractality defined as a width of singularity spectrum can be successfully used as a measure of complexity both in the case of periodic and aperiodic (stochastic or chaotic) input signals. We show that in the case of periodic driving force singularity spectrum can change its structure qualitatively becoming monofractal in the regime of stochastic synchronization. This fact allows us to consider the degree of multifractality as a new measure of stochastic synchronization also. Moreover, our calculations have shown that the effect of stochastic resonance can be catched by this measure even from a very short return time sequence. We use also the proposed approach to characterize the noise-enhanced dynamics of a coupled stochastic neurons model.Comment: 10 pages, 21 EPS-figures, RevTe

Next-generation ensemble projections reveal higher climate risks for marine ecosystems

Projections of climate change impacts on marine ecosystems have revealed long-term declines in global marine animal biomass and unevenly distributed impacts on fisheries. Here we apply an enhanced suite of global marine ecosystem models from the Fisheries and Marine Ecosystem Model Intercomparison Project (Fish-MIP), forced by new-generation Earth system model outputs from Phase 6 of the Coupled Model Intercomparison Project (CMIP6), to provide insights into how projected climate change will affect future ocean ecosystems. Compared with the previous generation CMIP5-forced Fish-MIP ensemble, the new ensemble ecosystem simulations show a greater decline in mean global ocean animal biomass under both strong-mitigation and high-emissions scenarios due to elevated warming, despite greater uncertainty in net primary production in the high-emissions scenario. Regional shifts in the direction of biomass changes highlight the continued and urgent need to reduce uncertainty in the projected responses of marine ecosystems to climate change to help support adaptation planning

Stochastic Resonance in Ion Channels Characterized by Information Theory

We identify a unifying measure for stochastic resonance (SR) in voltage dependent ion channels which comprises periodic (conventional), aperiodic and nonstationary SR. Within a simplest setting, the gating dynamics is governed by two-state conductance fluctuations, which switch at random time points between two values. The corresponding continuous time point process is analyzed by virtue of information theory. In pursuing this goal we evaluate for our dynamics the tau-information, the mutual information and the rate of information gain. As a main result we find an analytical formula for the rate of information gain that solely involves the probability of the two channel states and their noise averaged rates. For small voltage signals it simplifies to a handy expression. Our findings are applied to study SR in a potassium channel. We find that SR occurs only when the closed state is predominantly dwelled. Upon increasing the probability for the open channel state the application of an extra dose of noise monotonically deteriorates the rate of information gain, i.e., no SR behavior occurs.Comment: 10 pages, 2 figures, to appear in Phys. Rev.

Exploiting Reliability-Guided Aggregation for the Assessment of Curvilinear Structure Tortuosity

The study on tortuosity of curvilinear structures in medical images has been significant in support of the examination and diagnosis for a number of diseases. To avoid the bias that may arise from using one particular tortuosity measurement, the simultaneous use of multiple measurements may offer a promising approach to produce a more robust overall assessment. As such, this paper proposes a data-driven approach for the automated grading of curvilinear structures’ tortuosity, where multiple morphological measurements are aggregated on the basis of reliability to form a robust overall assessment. The proposed pipeline starts dealing with the imprecision and uncertainty inherently embedded in empirical tortuosity grades, whereby a fuzzy clustering method is applied on each available measurement. The reliability of each measurement is then assessed following a nearest neighbour guided approach before the final aggregation is made. Experimental results on two corneal nerve and one retinal vessel data sets demonstrate the superior performance of the proposed method over those where measurements are used independently or aggregated using conventional averaging operators

Comparing the performance of FA, DFA and DMA using different synthetic long-range correlated time series

Notwithstanding the significant efforts to develop estimators of long-range correlations (LRC) and to compare their performance, no clear consensus exists on what is the best method and under which conditions. In addition, synthetic tests suggest that the performance of LRC estimators varies when using different generators of LRC time series. Here, we compare the performances of four estimators [Fluctuation Analysis (FA), Detrended Fluctuation Analysis (DFA), Backward Detrending Moving Average (BDMA), and centred Detrending Moving Average (CDMA)]. We use three different generators [Fractional Gaussian Noises, and two ways of generating Fractional Brownian Motions]. We find that CDMA has the best performance and DFA is only slightly worse in some situations, while FA performs the worst. In addition, CDMA and DFA are less sensitive to the scaling range than FA. Hence, CDMA and DFA remain "The Methods of Choice" in determining the Hurst index of time series.Comment: 6 pages (including 3 figures) + 3 supplementary figure