Genome-wide comparative analysis of the codon usage patterns in plants

Codon usage analysis has been a classical area of study for decades and is important for evolution, mRNA translation, and new gene discovery. Recently, genome sequencing has made it possible to perform studies of the entire genome in plant kingdoms. The base composition of the coding sequence, codon usage pattern, codon pairs, and related indicators of relative synonymous codon usage (RSCU), including the Fop, Nc, RSCU, CAI and GC contents, were analyzed. We found that the GC content of single-celled algae is the highest, whereas dicotyledons are the lowest. Moreover, the base composition of plants is similar within the same family. In addition, the GC content of the second base of the codon is lower than the first and third base. In conclusion, the codon usage characteristics are opposite in Gramineae, single-celled algae, fern and dicotyledon, moss, and Pinaceae. Furthermore, the degree of codon usage bias is decreasing with evolution. Therefore, we hypothesize that the lower the plants, the more that they must optimize codons and that higher plants no longer need to optimize codons.     

Pull or Push? Octopuses Solve a Puzzle Problem

Octopuses have large brains and exhibit complex behaviors, but relatively little is known about their cognitive abilities. Here we present data from a five-level learning and problem-solving experiment. Seven octopuses (Octopus vulgaris) were first trained to open an L shaped container to retrieve food (level 0). After learning the initial task all animals followed the same experimental protocol, first they had to retrieve this L shaped container, presented at the same orientation, through a tight fitting hole in a clear Perspex partition (level 1). This required the octopuses to perform both pull and release or push actions. After reaching criterion the animals advanced to the next stage of the test, which would be a different consistent orientation of the object (level 2) at the start of the trial, an opaque barrier (level 3) or a random orientation of the object (level 4). All octopuses were successful in reaching criterion in all levels of the task. At the onset of each new level the performance of the animals dropped, shown as an increase in working times. However, they adapted quickly so that overall working times were not significantly different between levels. Our findings indicate that octopuses show behavioral flexibility by quickly adapting to a change in a task. This can be compared to tests in other species where subjects had to conduct actions comprised of a set of motor actions that cannot be understood by a simple learning rule alone.     

Carbon Abatement and Emissions Associated with the Gasification of Walnut Shells for Bioenergy and Biochar Production

By converting biomass residue to biochar, we could generate power cleanly and sequester carbon resulting in overall greenhouse gas emissions (GHG) savings when compared to typical fossil fuel usage and waste disposal. We estimated the carbon dioxide (CO₂) abatements and emissions associated to the concurrent production of bioenergy and biochar through biomass gasification in an organic walnut farm and processing facility in California, USA. We accounted for (i) avoided-CO₂ emissions from displaced grid electricity by bioenergy; (ii) CO₂ emissions from farm machinery used for soil amendment of biochar; (iii) CO₂ sequestered in the soil through stable biochar-C; and (iv) direct CO₂ and nitrous oxide (N₂O) emissions from soil. The objective of these assessments was to pinpoint where the largest C offsets can be expected in the bioenergy-biochar chain. We found that energy production from gasification resulted in 91.8% of total C offsets, followed by stable biochar-C (8.2% of total C sinks), offsetting a total of 107.7 kg CO₂-C eq Mg^-1 feedstock. At the field scale, we monitored gas fluxes from soils for 29 months (180 individual observations) following field management and precipitation events in addition to weekly measurements within three growing seasons and two tree dormancy periods. We compared four treatments: control, biochar, compost, and biochar combined with compost. Biochar alone or in combination with compost did not alter total N₂O and CO₂ emissions from soils, indicating that under the conditions of this study, biochar-prompted C offsets may not be expected from the mitigation of direct soil GHG emissions. However, this study revealed a case where a large environmental benefit was given by the waste-to-bioenergy treatment, addressing farm level challenges such as waste management, renewable energy generation, and C sequestration.     

Estimating and Analyzing Savannah Phenology with a Lagged Time Series Model

Savannah regions are predicted to undergo changes in precipitation patterns according to current climate change projections. This change will affect leaf phenology, which controls net primary productivity. It is of importance to study this since savannahs play an important role in the global carbon cycle due to their areal coverage and can have an effect on the food security in regions that depend on subsistence farming. In this study we investigate how soil moisture, mean annual precipitation, and day length control savannah phenology by developing a lagged time series model. The model uses climate data for 15 flux tower sites across four continents, and normalized difference vegetation index from satellite to optimize a statistical phenological model. We show that all three variables can be used to estimate savannah phenology on a global scale. However, it was not possible to create a simplified savannah model that works equally well for all sites on the global scale without inclusion of more site specific parameters. The simplified model showed no bias towards tree cover or between continents and resulted in a cross-validated r² of 0.6 and root mean squared error of 0.1. We therefore expect similar average results when applying the model to other savannah areas and further expect that it could be used to estimate the productivity of savannah regions.     

Inertia weight control strategies for particle swarm optimization

Particle swarm optimization (PSO) is a population-based, stochastic optimization technique inspired by the social dynamics of birds. The PSO algorithm is rather sensitive to the control parameters, and thus, there has been a significant amount of research effort devoted to the dynamic adaptation of these parameters. The focus of the adaptive approaches has largely revolved around adapting the inertia weight as it exhibits the clearest relationship with the exploration/exploitation balance of the PSO algorithm. However, despite the significant amount of research efforts, many inertia weight control strategies have not been thoroughly examined analytically nor empirically. Thus, there are a plethora of choices when selecting an inertia weight control strategy, but no study has been comprehensive enough to definitively guide the selection. This paper addresses these issues by first providing an overview of 18 inertia weight control strategies. Secondly, conditions required for the strategies to exhibit convergent behaviour are derived. Finally, the inertia weight control strategies are empirically examined on a suite of 60 benchmark problems. Results of the empirical investigation show that none of the examined strategies, with the exception of a randomly selected inertia weight, even perform on par with a constant inertia weight.     

Turing learning: a metric-free approach to inferring behavior and its application to swarms

We propose Turing Learning, a novel system identification method for inferring the behavior of natural or artificial systems. Turing Learning simultaneously optimizes two populations of computer programs, one representing models of the behavior of the system under investigation, and the other representing classifiers. By observing the behavior of the system as well as the behaviors produced by the models, two sets of data samples are obtained. The classifiers are rewarded for discriminating between these two sets, that is, for correctly categorizing data samples as either genuine or counterfeit. Conversely, the models are rewarded for ‘tricking’ the classifiers into categorizing their data samples as genuine. Unlike other methods for system identification, Turing Learning does not require predefined metrics to quantify the difference between the system and its models. We present two case studies with swarms of simulated robots and prove that the underlying behaviors cannot be inferred by a metric-based system identification method. By contrast, Turing Learning infers the behaviors with high accuracy. It also produces a useful by-product—the classifiers—that can be used to detect abnormal behavior in the swarm. Moreover, we show that Turing Learning also successfully infers the behavior of physical robot swarms. The results show that collective behaviors can be directly inferred from motion trajectories of individuals in the swarm, which may have significant implications for the study of animal collectives. Furthermore, Turing Learning could prove useful whenever a behavior is not easily characterizable using metrics, making it suitable for a wide range of applications.