Cell divisions are not essential for the direct conversion of fibroblasts into neuronal cells

Direct lineage conversion is a promising approach for disease modeling and regenerative medicine. Cell divisions play a key role in reprogramming of somatic cells to pluripotency, however their role in direct lineage conversion is not clear. Here we used transdifferentiation of fibroblasts into neuronal cells by forced expression of defined transcription factors as a model system to study the role of cellular division in the direct conversion process. We have shown that conversion occurs in the presence of the cell cycle inhibitors aphidicolin or mimosine. Moreover, overexpression of the cell cycle activator cMyc negatively influences the process of direct conversion. Overall, our results suggest that cell divisions are not essential for the direct conversion of fibroblasts into neuronal cells

The three-dimensional randomly dilute Ising model: Monte Carlo results

We perform a high-statistics simulation of the three-dimensional randomly dilute Ising model on cubic lattices $L^3$ with $L\le 256$. We choose a particular value of the density, x=0.8, for which the leading scaling corrections are suppressed. We determine the critical exponents, obtaining $\nu = 0.683(3)$, $\eta = 0.035(2)$, $\beta = 0.3535(17)$, and $\alpha = -0.049(9)$, in agreement with previous numerical simulations. We also estimate numerically the fixed-point values of the four-point zero-momentum couplings that are used in field-theoretical fixed-dimension studies. Although these results somewhat differ from those obtained using perturbative field theory, the field-theoretical estimates of the critical exponents do not change significantly if the Monte Carlo result for the fixed point is used. Finally, we determine the six-point zero-momentum couplings, relevant for the small-magnetization expansion of the equation of state, and the invariant amplitude ratio $R^+_\xi$ that expresses the universality of the free-energy density per correlation volume. We find $R^+_\xi = 0.2885(15)$.Comment: 34 pages, 7 figs, few correction

Cellular Automata Applications in Shortest Path Problem

Cellular Automata (CAs) are computational models that can capture the essential features of systems in which global behavior emerges from the collective effect of simple components, which interact locally. During the last decades, CAs have been extensively used for mimicking several natural processes and systems to find fine solutions in many complex hard to solve computer science and engineering problems. Among them, the shortest path problem is one of the most pronounced and highly studied problems that scientists have been trying to tackle by using a plethora of methodologies and even unconventional approaches. The proposed solutions are mainly justified by their ability to provide a correct solution in a better time complexity than the renowned Dijkstra's algorithm. Although there is a wide variety regarding the algorithmic complexity of the algorithms suggested, spanning from simplistic graph traversal algorithms to complex nature inspired and bio-mimicking algorithms, in this chapter we focus on the successful application of CAs to shortest path problem as found in various diverse disciplines like computer science, swarm robotics, computer networks, decision science and biomimicking of biological organisms' behaviour. In particular, an introduction on the first CA-based algorithm tackling the shortest path problem is provided in detail. After the short presentation of shortest path algorithms arriving from the relaxization of the CAs principles, the application of the CA-based shortest path definition on the coordinated motion of swarm robotics is also introduced. Moreover, the CA based application of shortest path finding in computer networks is presented in brief. Finally, a CA that models exactly the behavior of a biological organism, namely the Physarum's behavior, finding the minimum-length path between two points in a labyrinth is given.Comment: To appear in the book: Adamatzky, A (Ed.) Shortest path solvers. From software to wetware. Springer, 201

Spin and charge dynamics of chromium alloys

Both the spin- and charge-density waves of Cr alloys are produced by the Coulomb attraction between electrons and holes on nearly nested Fermi surfaces. Driven by quasi-particle transitions, transverse spin- wave and longitudinal phason modes are associated with rotational and translational symmetries of pure Cr and its dilute alloys. At low frequencies, both spin and charge phasons have a nearly linear dispersion with a mode velocity which approaches the spin-wave velocity as T approaches T{sub N} or as the mismatch between the Fermi surfaces increases

Targeted genomic integration of EGFP under tubulin beta 3 class III promoter and mEos2 under tryptophan hydroxylase 2 promoter does not produce sufficient levels of reporter gene expression

Neuronal tracing is a modern technology that is based on the expression of fluorescent proteins under the control of cell type-specific promoters. However, random genomic integration of the reporter construct often leads to incorrect spatial and temporal expression of the marker protein. Targeted integration (or knock-in) of the reporter coding sequence is supposed to provide better expression control by exploiting endogenous regulatory elements. Here we describe the generation of two fluorescent reporter systems: enhanced green fluorescent protein (EGFP) under pan-neural marker class III β-tubulin (Tubb3) promoter and mEos2 under serotonergic neuron-specific tryptophan hydroxylase 2 (Tph2) promoter. Differentiation of Tubb3-EGFP embryonic stem (ES) cells into neurons revealed that though Tubb3-positive cells express EGFP, its expression level is not sufficient for the neuronal tracing by routine fluorescent microscopy. Similarly, the expression levels of mEos2-TPH2 in differentiated ES cells was very low and could be detected only on messenger RNA level using polymerase chain reaction-based methods. Our data shows that the use of endogenous regulatory elements to control transgene expression is not always beneficial compared with the random genomic integration