Manipulating cyanobacteria: Spirulina for potential CELSS diet

Spirulina sp. as a bioregenerative photosynthetic and an edible alga for spacecraft crew in a CELSS, was characterized for the biomass yield in batch cultures, under various environmental conditions. The partitioning of the assimalitory products (proteins, carbohydrates, lipids) were manipulated by varying the environmental growth conditions. Experiments with Spirulina have shown that under stress conditions (i.e., high light 160 uE/sq m/s, temperature 38 C, nitrogen or phosphate limitation; 0.1 M sodium chloride) carbohydrates increased at the expense of proteins. In other experiments, where the growth media were sufficient in nutrients and incubated under optimum growth conditions, the total of the algal could be manipulated by growth conditions. These results support the feasibility of considering Spirulina as a subsystem in CELSS because of the ease with which its nutrient content can be manipulated

Inhibition causes ceaseless dynamics in networks of excitable nodes

The collective dynamics of a network of excitable nodes changes dramatically when inhibitory nodes are introduced. We consider inhibitory nodes which may be activated just like excitatory nodes but, upon activating, decrease the probability of activation of network neighbors. We show that, although the direct effect of inhibitory nodes is to decrease activity, the collective dynamics becomes self-sustaining. We explain this counterintuitive result by defining and analyzing a "branching function" which may be thought of as an activity-dependent branching ratio. The shape of the branching function implies that for a range of global coupling parameters dynamics are self-sustaining. Within the self-sustaining region of parameter space lies a critical line along which dynamics take the form of avalanches with universal scaling of size and duration, embedded in ceaseless timeseries of activity. Our analyses, confirmed by numerical simulation, suggest that inhibition may play a counterintuitive role in excitable networks.Comment: 11 pages, 6 figure

The Photographic Action of Irradiated Cod-Liver Oil

We have previously reported the results of experiments on the photographic action of irradiated oils. This paper gives the results obtained with various arrangements of the screens and the receptacles for the irradiated cod-liver oil

Predicting criticality and dynamic range in complex networks: effects of topology

The collective dynamics of a network of coupled excitable systems in response to an external stimulus depends on the topology of the connections in the network. Here we develop a general theoretical approach to study the effects of network topology on dynamic range, which quantifies the range of stimulus intensities resulting in distinguishable network responses. We find that the largest eigenvalue of the weighted network adjacency matrix governs the network dynamic range. Specifically, a largest eigenvalue equal to one corresponds to a critical regime with maximum dynamic range. We gain deeper insight on the effects of network topology using a nonlinear analysis in terms of additional spectral properties of the adjacency matrix. We find that homogeneous networks can reach a higher dynamic range than those with heterogeneous topology. Our analysis, confirmed by numerical simulations, generalizes previous studies in terms of the largest eigenvalue of the adjacency matrix.Comment: 4 pages, 3 figure

Effects of network topology, transmission delays, and refractoriness on the response of coupled excitable systems to a stochastic stimulus

We study the effects of network topology on the response of networks of coupled discrete excitable systems to an external stochastic stimulus. We extend recent results that characterize the response in terms of spectral properties of the adjacency matrix by allowing distributions in the transmission delays and in the number of refractory states, and by developing a nonperturbative approximation to the steady state network response. We confirm our theoretical results with numerical simulations. We find that the steady state response amplitude is inversely proportional to the duration of refractoriness, which reduces the maximum attainable dynamic range. We also find that transmission delays alter the time required to reach steady state. Importantly, neither delays nor refractoriness impact the general prediction that criticality and maximum dynamic range occur when the largest eigenvalue of the adjacency matrix is unity

Control of excitable systems is optimal near criticality

Experiments suggest that cerebral cortex gains several functional advantages by operating in a dynamical regime near the critical point of a phase transition. However, a long-standing criticism of this hypothesis is that critical dynamics are rather noisy, which might be detrimental to aspects of brain function that require precision. If the cortex does operate near criticality, how might it mitigate the noisy fluctuations? One possibility is that other parts of the brain may act to control the fluctuations and reduce cortical noise. To better understand this possibility, here we numerically and analytically study a network of binary neurons. We determine how efficacy of controlling the population firing rate depends on proximity to criticality as well as different structural properties of the network. We found that control is most effective - errors are minimal for the widest range of target firing rates - near criticality. Optimal control is slightly away from criticality for networks with heterogeneous degree distributions. Thus, while criticality is the noisiest dynamical regime, it is also the regime that is easiest to control, which may offer a way to mitigate the noise.Comment: 5 pages, 3 figure

Enhancing sustainability by improving plant salt tolerance through macro-and micro-algal biostimulants

Algal biomass, extracts, or derivatives have long been considered a valuable material to bring benefits to humans and cultivated plants. In the last decades, it became evident that algal formulations can induce multiple effects on crops (including an increase in biomass, yield, and quality), and that algal extracts contain a series of bioactive compounds and signaling molecules, in addition to mineral and organic nutrients. The need to reduce the non-renewable chemical input in agriculture has recently prompted an increase in the use of algal extracts as a plant biostimulant, also because of their ability to promote plant growth in suboptimal conditions such as saline environments is beneficial. In this article, we discuss some research areas that are critical for the implementation in agriculture of macro-and microalgae extracts as plant biostimulants. Specifically, we provide an overview of current knowledge and achievements about extraction methods, compositions, and action mechanisms of algal extracts, focusing on salt-stress tolerance. We also outline current limitations and possible research avenues. We conclude that the comparison and the integration of knowledge on the molecular and physiological response of plants to salt and to algal extracts should also guide the extraction procedures and application methods. The effects of algal biostimulants have been mainly investigated from an applied perspective, and the exploitation of different scientific disciplines is still much needed for the development of new sustainable strategies to increase crop tolerance to salt stress

httk: R Package for High-Throughput Toxicokinetics

Thousands of chemicals have been profiled by high-throughput screening programs such as ToxCast and Tox21; these chemicals are tested in part because most of them have limited or no data on hazard, exposure, or toxicokinetics. Toxicokinetic models aid in predicting tissue concentrations resulting from chemical exposure, and a "reverse dosimetry" approach can be used to predict exposure doses sufficient to cause tissue concentrations that have been identified as bioactive by high-throughput screening. We have created four toxicokinetic models within the R software package httk. These models are designed to be parameterized using high-throughput in vitro data (plasma protein binding and hepatic clearance), as well as structure-derived physicochemical properties and species-specific physiological data. The package contains tools for Monte Carlo sampling and reverse dosimetry along with functions for the analysis of concentration vs. time simulations. The package can currently use human in vitro data to make predictions for 553 chemicals in humans, rats, mice, dogs, and rabbits, including 94 pharmaceuticals and 415 ToxCast chemicals. For 67 of these chemicals, the package includes rat-specific in vitro data. This package is structured to be augmented with additional chemical data as they become available. Package httk enables the inclusion of toxicokinetics in the statistical analysis of chemicals undergoing high-throughput screening