A simple topographical model to predict Golden Eagle Aquila chrysaetos space use during dispersal

Many large raptors exploit or rely on anabatic and orographic winds which provide vertical lift, to supplement or provide the energy fuelling flight. Airspace is therefore a critical habitat for such large raptors and its use is subject to the underlying terrestrial topography, because particular topographical features are more likely to provide wind-energetic lift. Accordingly, ridges and/or ‘rugged topography’ are common preferred features in habitat use by large raptors. Our study aimed to provide a simple model of space use for a large raptor, the Golden Eagle Aquila chrysaetos, based on thousands of GPS telemetry records during juvenile dispersal of 92 birds tagged as nestlings between 2007 and 2016 across upland Scotland. Model development was based on the hypothesis that four topographical variables would be influential: slope, aspect, altitude and distance from ridge. The telemetry dataset was divided into training and two testing components. The first testing set was derived by a temporal split resulting in approximately equal sample size on records and some temporal overlap in individuals’ records with training data. The second testing set involved no individuals from the training set. Aspect was removed early in training model development because it was not influential. The model found that young Golden Eagles preferred, or used according to availability, space above slopes greater than 10°, at an altitude of ≥ 300 m, and within 300 m of a ridge. The test data were highly correlated with those from the training data in the model variables, and performance as regard to expected preferences from the model was improved in both test datasets, indicating the model was robust. Given the apparent universal nature of large raptor dependence on topography, that topography is relatively immutable according to time and use, and that topographical data are readily available, we commend our approach to other habitat preference studies of Golden Eagles and other large raptors elsewhere.     

E35 ablates acute leukemia stem and progenitor cells in vitro and in vivo

Leukemia stem cells (LSCs) have critical functions in acute leukemia (AL) pathogenesis, participating in its initiation and relapse. Thus, identifying new molecules to eradicate LSCs represents a high priority for AL management. This work identified E35, a novel Emodin derivative, which strongly inhibited growth and enhanced apoptosis of AL stem cell lines, and primary stem and progenitor cells from AL cases, while sparing normal hematopoietic cells. Furthermore, functional assays in cultured cells and animals suggested that E35 preferentially ablated primitive leukemia cell populations without impairing their normal counterparts. Moreover, molecular studies showed that E35 remarkably downregulated drug-resistant gene and dramatically inhibited the Akt/mammalian target of rapamycin signaling pathway. Notably, the in vivo anti-LSC activity of E35 was further confirmed in murine xenotransplantation models. Collectively, these findings indicate E35 constitutes a novel therapeutic candidate for AL, potentially targeting leukemia stem and progenitor cells.     

Approaching mathematical model of the immune network based DNA Strand Displacement system

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot.     

In silico investigation of novel biological pathways: The role of CD200 in regulation of T cell priming in experimental autoimmune encephalomyelitis

The use of simulation to investigate biological domains will inevitably lead to the need to extend existing simulations as new areas of these domains become more fully understood. Such simulation extensions can entail the incorporation of additional cell types, molecules or molecular pathways, all of which can exert a profound influence on the simulation behaviour. Where the biological domain is not well characterised, a structured development methodology must be employed to ensure that the extended simulation is well aligned with its predecessor. We develop and discuss such a methodology, relying on iterative simulation development and sensitivity analysis. The utility of this methodology is demonstrated using a case study simulation of experimental autoimmune encephalomyelitis (EAE), a murine T cell-mediated autoimmune disease model of multiple sclerosis, where it is used to investigate the activity of an additional regulatory pathway. We discuss how application of this methodology guards against creating inappropriate simulation representations of the biology when investigating poorly characterised biological mechanisms.     

On evaluation of infection probabilities for different age groups

It is well known that evaluation of parameters for any mathematical model is always very important and often a very difficult problem. This is especially true for agents-based models because as a rule the evaluation of an agent's parameters can be made only based on available information from the higher levels of a complex system. Such problems can be ill-posed or even have a non-unique solution. That is why there is no general algorithm for solving these problems and a researcher has to search for it for every specific model type.     

A new fast algorithm for solving the minimum spanning tree problem based on DNA molecules computation

The minimum spanning tree (MST) problem is to find minimum edge connected subsets containing all the vertex of a given undirected graph. It is a vitally important NP-complete problem in graph theory and applied mathematics, having numerous real life applications. Moreover in previous studies, DNA molecular operations usually were used to solve NP-complete head-to-tail path search problems, rarely for NP-hard problems with multi-lateral path solutions result, such as the minimum spanning tree problem. In this paper, we present a new fast DNA algorithm for solving the MST problem using DNA molecular operations. For an undirected graph with n vertex and m edges, we reasonably design flexible length DNA strands representing the vertex and edges, take appropriate steps and get the solutions of the MST problem in proper length range and O(3m + n) time complexity. We extend the application of DNA molecular operations and simultaneity simplify the complexity of the computation. Results of computer simulative experiments show that the proposed method updates some of the best known values with very short time and that the proposed method provides a better performance with solution accuracy over existing algorithms.     

Electrically tunable organic bioelectronics for spatial and temporal manipulation of neuron-like pheochromocytoma (PC-12) cells

Background

Organic bioelectronic devices consisting of alternating poly(3,4-ethylenedioxythiophene) (PEDOT) and reduced graphite oxide (rGO) striped microelectrode arrays were fabricated by lithography technology. It has been demonstrated that the organic bioelectronic devices can be used to spatially and temporally manipulate the location and proliferation of the neuron-like pheochromocytoma cells (PC-12 cells).

Methods

By coating an electrically labile contact repulsion layer of poly(l-lysine-graft-ethylene glycol) (PLL-g-PEG) on the PEDOT electrode, the location and polarity of the PC-12 cells were confined to the rGO electrodes.

Results

The outgrowth of spatially confined bipolar neurites was found to align along the direction of the 20 μm wide electrode. The location of the PC-12 cells can also be manipulated temporally by applying electrical stimulation during the neurite differentiation of PC-12 cells, allowing the PC-12 cells to cross over the boundary between the PEDOT and the rGO regions and construct neurite networks in an unconfined manner where the contact repulsive coating of PLL-g-PEG was removed.

Conclusions

This adsorption and desorption of the PLL-g-PEG without and with electrical stimulation can be attributed to the tunable surface properties of the PEDOT microelectrodes, whose surface charge can switch from being negative to positive under electrical stimulation.

General significance

The electrically tunable organic bioelectronics reported here could potentially be applied to tissue engineering related to the development and regeneration of mammalian nervous systems. The spatial and temporal control in this device would also be used to study the synapse junctions of neuron–neuron contacts in both time and space domains. This article is part of a Special Issue entitled Organic Bioelectronics — Novel Applications in Biomedicine.     

Fitting dynamic growth models of biological phenomena from sample observations through Gaussian diffusion processes

This paper addresses the building of stochastic models that adequately describe dynamic phenomena and, in particular, those that occur in the Biosciences. In this context, the empirical fitting of a Gaussian diffusion process from sample data of a dynamic growth phenomenon is considered. In order to do this, a methodology based on approximations to its mean and variance functions is presented. Finally, several applications based on simulated and real data have been carried out.     

Process,correlation and parameter fitting in species distribution models: a response to Kriticos et al

In a recent article (Dormann et al., 2012, Journal of Biogeography, 39, 2119–2131), we compared different approaches to species distribution modelling and depicted modelling approaches along an axis from purely ‘correlative’ to ‘forward process‐based’ models. In their correspondence, Kriticos et al. (2013, Journal of Biogeography, doi: 10.1111/j.1365‐2699.2012.02791.x ) challenge this view, claiming that our continuum representation neglects differences among models and does not consider the ability of fitted process‐based models to combine the advantages of both process‐based and correlative modelling approaches. Here we clarify that the continuum view resulted from recognition of the manifold differences between models. We also reinforce the point that the current trend towards combining different modelling approaches may lead not only to the desired combination of the advantages but also to the accumulation of the disadvantages of those approaches. This point has not been made sufficiently clear previously.