Cotton-Grass and Blueberry have Opposite Effect on Peat Characteristics and Nutrient Transformation in Peatland

Peatlands are large repositories of carbon (C). Sphagnum mosses play a key role in C sequestration, whereas the presence of vascular plants is generally thought to stimulate peat decomposition. Recent studies stress the importance of plant species for peat quality and soil microbial activity. Thus, learning about specific plant-microbe-soil relations and their potential feedbacks for C and nutrient cycling are important for a correct understanding of C sequestration in peatlands and its potential shift associated with vegetation change. We studied how the long-term presence of blueberry and cotton-grass, the main vascular dominants of spruce swamp forests, is reflected in the peat characteristics, soil microbial biomass and activities, and the possible implications of their spread for nutrient cycling and C storage in these systems. We showed that the potential effect of vascular plants on ecosystem functioning is species specific and need not necessarily result in increased organic matter decomposition. Although the presence of blueberry enhanced phosphorus availability, soil microbial biomass and the activities of C-acquiring enzymes, cotton-grass strongly depleted phosphorus and nitrogen from the peat. The harsh conditions and prevailing anoxia retarded the decomposition of cotton-grass litter and caused no significant enhancement in microbial biomass and exoenzymatic activity. Therefore, the spread of blueberry in peatlands may stimulate organic matter decomposition and negatively affect the C sequestration process, whereas the potential spread of cotton-grass would not likely change the functioning of peatlands as C sinks.Peer reviewe

Skeletal diseases caused by mutations in PTH1R show aberrant differentiation of skeletal progenitors due to dysregulation of DEPTOR

Alterations in the balance between skeletogenesis and adipogenesis is a pathogenic feature in multiple skeletal disorders. Clinically, enhanced bone marrow adiposity in bones impairs mobility and increases fracture risk, reducing the quality of life of patients. The molecular mechanism that underlies the balance between skeletogenesis and adipogenesis is not completely understood but alterations in skeletal progenitor cells’ differentiation pathway plays a key role. We recently demonstrated that parathyroid hormone (PTH)/PTH-related peptide (PTHrP) control the levels of DEPTOR, an inhibitor of the mechanistic target of rapamycin (mTOR), and that DEPTOR levels are altered in different skeletal diseases. Here, we show that mutations in the PTH receptor-1 (PTH1R) alter the differentiation of skeletal progenitors in two different skeletal genetic disorders and lead to accumulation of fat or cartilage in bones. Mechanistically, DEPTOR controls the subcellular localization of TAZ (transcriptional co-activator with a PDZ-binding domain), a transcriptional regulator that governs skeletal stem cells differentiation into either bone and fat. We show that DEPTOR regulation of TAZ localization is achieved through the control of Dishevelled2 (DVL2) phosphorylation. Depending on nutrient availability, DEPTOR directly interacts with PTH1R to regulate PTH/PTHrP signaling or it forms a complex with TAZ, to prevent its translocation to the nucleus and therefore inhibit its transcriptional activity. Our data point DEPTOR as a key molecule in skeletal progenitor differentiation; its dysregulation under pathologic conditions results in aberrant bone/fat balance

Decay of solutions to integrodifferential equations

We discuss long time behavior of solutions to a non-linear second order integrodifferential convolution equation, in particular we focus on the speed of convergence to equilibrium. The key assumptions are that the convolution kernel is small and the non-linear operator satisfies the Lojasiewicz inequality.Non UBCUnreviewedAuthor affiliation: Charles UniversityResearche

Decay estimates for solutions of abstract wave equations with general damping function

In this article we prove convergence to equilibrium and decay estimates for a class of damped abstract wave equations. We focus on the damping term to be as general as possible, including functions that oscillate between two positive functions in a neighborhood of the origin and/or behave differently in each direction

Convergence to equilibrium of relatively compact solutions to evolution equations

We prove convergence to equilibrium for relatively compact solutions to an abstract evolution equation satisfying energy estimates near the omega-limit set. These energy estimates generalize Lojasiewicz and Kurdyka-Lojasiewicz-Simon gradient inequalities. We apply the abstract results to several ODEs and PDEs of first and second order

The effect of inhibition on rate code efficiency indicators

In this paper we investigate the rate coding capabilities of neurons whose input signal are alterations of the base state of balanced inhibitory and excitatory synaptic currents. We consider different regimes of excitation-inhibition relationship and an established conductance-based leaky integrator model with adaptive threshold and parameter sets recreating biologically relevant spiking regimes. We find that given mean post-synaptic firing rate, counter-intuitively, increased ratio of inhibition to excitation generally leads to higher signal to noise ratio (SNR). On the other hand, the inhibitory input significantly reduces the dynamic coding range of the neuron. We quantify the joint effect of SNR and dynamic coding range by computing the metabolic efficiency-the maximal amount of information per one ATP molecule expended (in bits/ATP). Moreover, by calculating the metabolic efficiency we are able to predict the shapes of the post-synaptic firing rate histograms that may be tested on experimental data. Likewise, optimal stimulus input distributions are predicted, however, we show that the optimum can essentially be reached with a broad range of input distributions. Finally, we examine which parameters of the used neuronal model are the most important for the metabolically efficient information transfer. Author summary Neurons communicate by firing action potentials, which can be considered as all-or-none events. The classical rate coding hypothesis states that neurons communicate the information about stimulus intensity by altering their firing frequency. Cortical neurons typically receive a signal from many different neurons, which, depending on the synapse type, either depolarize (excitatory input) or hyperpolarize (inhibitory input) the neural membrane. We use a neural model with excitatory and inhibitory synaptic conductances to reproduce in-vivo like activity and investigate how the intensity of presynaptic inhibitory activity affects the neuron's ability to transmit information through rate code. We reach a counter-intuitive result that increase in inhibition improves the signal-to-noise ratio of the neural response, despite introducing additional noise to the input signal. On the other hand, inhibition also limits the neuronal output range. However, in the end, the actual amount of information transmitted (in bits per energy expended) is remarkably robust to PLOS Computational Biology | https://doi.org/10.1371/journal.pcbi

Sampling bias and extrapolation.

The information-metabolic efficiency calculated by the Jimbo-Kunisawa algorithm is plotted for different numbers of principal components used. We calculated the information-metabolic efficiency from different numbers of trials. At high number of components, lower number of trials lead to significantly higher information-metabolic efficiency. This is the effect of the sampling bias. We attempted to remove the bias by using the quadratic extrapolation method. For 500 principal components the bias is still relatively low, and increasing the number of components brings little benefit in terms of information-metabolic efficiency. (TIF)</p

Effect of equalizing the resting cost on the information-metabolic efficiency.

We observed that the cost of the resting state was different for different recurrence strengths arec (Fig 3A–3C). This could potentially explain the higher information-metabolic efficiency E (Eq 10) for intermediate values of arec and its decrease for high values of arec. To quantify the effect of the resting cost, we set the resting cost in each case to the resting cost of the feedforward network W0(arec = 0). The differences in the cost of the resting state did not have a qualitative effect on the conclusions. A: The same contour plot as in Fig 5B. B: Contour plot with equalized resting costs (contours as in Fig 5B: 0.75, 1.0, 1.25, 1.5, 1.75, 2.0, and 2.25 bits/s). C: Heatmap of the relative differences. (TIF)</p