Exploring the association between rDNA dosage and muscular responses to resistance training in young adults

Muscle plasticity is affected by physical activity and inactivity, and resistance training has been shown to prevent and reverse negative consequences of inactivity and disuse, such as loss of muscle strength, function, and quality. Ribosomal biogenesis has been shown to be an important factor in understanding the mechanisms behind muscle growth, where total RNA (~ 80-85% rRNA) is an important proxy marker. Resistance training-induced accumulation of total RNA / rRNA has been demonstrated, it is unclear whether the amount of rDNA dose in the genome is decisive for this accumulation. 46 healthy young adults (females: 24.9 (3.8) years; men: 25.0 (4.24) years) were included, and completed 8 weeks of strength training divided into three training modalities, 0 sets (control), 3 sets, and 6 sets; with a training frequency of 3 times/week. Strength tests, ultrasound, DXA scan, blood tests, and muscle biopsy were performed to map physiological, molecular, and, phenotypic changes. A valid qPCR-based method was developed to estimate the rDNA dose in muscle and blood tissues. Total RNA increased significantly from T2 to T3, and an association was found between rDNA dose and exercise-induced accumulated total RNA at T3 (r = 0.470; p = 0.004), but no significant association was found at T2 and post-intervention. rDNA dose did not predict the observed muscle growth measured post-intervention. Contralateral resistance training showed muscular response through increased muscle strength and muscle thickness, but it was not volume-dependent (moderate versus high). In conclusion, rDNA dose appears to be associated with exercise-induced total RNA accretion. More research is needed to determine whether rDNA dose may be a determining factor for resistance training-induced responses to ribosomal biogenesis

Alien Registration- Olsen, Olaf E. (Portland, Cumberland County)

https://digitalmaine.com/alien_docs/31264/thumbnail.jp

Implementation of methane cycling for deep time, global warming simulations with the DCESS Earth System Model (Version 1.2)

Geological records reveal a number of ancient, large and rapid negative excursions of the carbon-13 isotope. Such excursions can only be explained by massive injections of depleted carbon to the Earth system over a short duration. These injections may have forced strong global warming events, sometimes accompanied by mass extinctions such as the Triassic-Jurassic and end-Permian extinctions 201 and 252 million years ago, respectively. In many cases, evidence points to methane as the dominant form of injected carbon, whether as thermogenic methane formed by magma intrusions through overlying carbon-rich sediment or from warming-induced dissociation of methane hydrate, a solid compound of methane and water found in ocean sediments. As a consequence of the ubiquity and importance of methane in major Earth events, Earth system models for addressing such events should include a comprehensive treatment of methane cycling but such a treatment has often been lacking. Here we implement methane cycling in the Danish Center for Earth System Science (DCESS) model, a simplified but well-tested Earth system model of intermediate complexity. We use a generic methane input function that allows variation in input type, size, timescale and ocean-atmosphere partition. To be able to treat such massive inputs more correctly, we extend the model to deal with ocean suboxic/anoxic conditions and with radiative forcing and methane lifetimes appropriate for high atmospheric methane concentrations. With this new model version, we carried out an extensive set of simulations for methane inputs of various sizes, timescales and ocean-atmosphere partitions to probe model behavior. We find that larger methane inputs over shorter timescales with more methane dissolving in the ocean lead to ever-increasing ocean anoxia with consequences for ocean life and global carbon cycling. Greater methane input directly to the atmosphere leads to more warming and, for example, greater carbon dioxide release from land soils. Analysis of synthetic sediment cores from the simulations provides guidelines for the interpretation of real sediment cores spanning the warming events. With this improved DCESS model version and paleo-reconstructions, we are now better armed to gauge the amounts, types, timescales and locations of methane injections driving specific, observed deep-time, global warming events.FONDECYT (Chile) 1150913 Chilean ICM grant NC12006

Gain control network conditions in early sensory coding

Gain control is essential for the proper function of any sensory system. However, the precise mechanisms for achieving effective gain control in the brain are unknown. Based on our understanding of the existence and strength of connections in the insect olfactory system, we analyze the conditions that lead to controlled gain in a randomly connected network of excitatory and inhibitory neurons. We consider two scenarios for the variation of input into the system. In the first case, the intensity of the sensory input controls the input currents to a fixed proportion of neurons of the excitatory and inhibitory populations. In the second case, increasing intensity of the sensory stimulus will both, recruit an increasing number of neurons that receive input and change the input current that they receive. Using a mean field approximation for the network activity we derive relationships between the parameters of the network that ensure that the overall level of activity of the excitatory population remains unchanged for increasing intensity of the external stimulation. We find that, first, the main parameters that regulate network gain are the probabilities of connections from the inhibitory population to the excitatory population and of the connections within the inhibitory population. Second, we show that strict gain control is not achievable in a random network in the second case, when the input recruits an increasing number of neurons. Finally, we confirm that the gain control conditions derived from the mean field approximation are valid in simulations of firing rate models and Hodgkin-Huxley conductance based models

The Araucaria Project: The Distance to the Sculptor Group Galaxy NGC 247 from Cepheid Variables Discovered in a Wide-Field

We report on the discovery of a Cepheid population in the Sculptor Group spiral galaxy NGC 247 for the first time. On the basis of wide-field images collected in photometric surveys in V and I bands which were conducted with three different telescopes and cameras, 23 Cepheid variables were discovered with periods ranging from 17 to 131 days. We have constructed the period-luminosity relations from these data and obtain distance moduli to NGC 247 of 28.20 $\pm$ 0.05 mag (internal error) in V, 28.04 $\pm$ 0.06 mag in I, and 27.80 $\pm$ 0.09 mag in the reddening-independent Wesenheit index. From our optical data we have determined the total mean reddening of the Cepheids in NGC 247 as E(B-V)=0.13 mag, which brings the true distance modulus determinations from the V and I bands into excellent agreement with the distance determination in the Wesenheit index. The best estimate for the true distance modulus of NGC 247 from our optical Cepheid photometry is 27.80 $\pm$0.09 (internal error) $\pm$ 0.09 mag (systematic error) which is in excellent agreement with other recent distance determinations for NGC 247 from the Tip of the Red Giant branch method, and from the Tully-Fisher relation. The distance for NGC 247 places this galaxy at twice the distance of two other Sculptor Group galaxies, NGC 300 and NGC 55, yielding supporting evidence for the filament-like structure of this group of galaxies. The reported distance value is tied to an assumed LMC distance modulus of 18.50 mag.Comment: AJ accepte

Feedforward Inhibition and Synaptic Scaling – Two Sides of the Same Coin?

Feedforward inhibition and synaptic scaling are important adaptive processes that control the total input a neuron can receive from its afferents. While often studied in isolation, the two have been reported to co-occur in various brain regions. The functional implications of their interactions remain unclear, however. Based on a probabilistic modeling approach, we show here that fast feedforward inhibition and synaptic scaling interact synergistically during unsupervised learning. In technical terms, we model the input to a neural circuit using a normalized mixture model with Poisson noise. We demonstrate analytically and numerically that, in the presence of lateral inhibition introducing competition between different neurons, Hebbian plasticity and synaptic scaling approximate the optimal maximum likelihood solutions for this model. Our results suggest that, beyond its conventional use as a mechanism to remove undesired pattern variations, input normalization can make typical neural interaction and learning rules optimal on the stimulus subspace defined through feedforward inhibition. Furthermore, learning within this subspace is more efficient in practice, as it helps avoid locally optimal solutions. Our results suggest a close connection between feedforward inhibition and synaptic scaling which may have important functional implications for general cortical processing