B4/32Ga28/32Sb10/32As22/32 Three-Dimensional Semiconductor Superlattice

Self-assembly of BD -rich A x B 1-x C y D 1-y was studied for a lot of semiconductor alloys. An occurrence of identical clustersshould be due to a decrease of the bond energy, internal strain energy or both of them. An arrangement of the clusters is disordered since the contents of minority atoms are in the dilute or ultra dilute limits in the considered alloys. B 4/32 Ga 28/32 Sb 10/32 As 22/32 semiconductor alloy with the three-dimensional superlattice is presented. This superlattice should be stable against disordering due to its minimal free energy. The superlattice forms by the identical cubic units consisting of 64 atoms and is the three-dimensional semiconductor soft X-ray diffraction grating.

Equality and Freedom as Uncertainty in Groups

In this paper, I investigate a connection between a common characterisation of freedom and how uncertainty is managed in a Bayesian hierarchical model. To do this, I consider a distributed factorization of a group’s optimization of free energy, in which each agent is attempting to align with the group and with its own model. I show how this can lead to equilibria for groups, defined by the capacity of the model being used, essentially how many different datasets it can handle. In particular, I show that there is a “sweet spot” in the capacity of a normal model in each agent’s decentralized optimization, and that this “sweet spot” corresponds to minimal free energy for the group. At the sweet spot, an agent can predict what the group will do and the group is not surprised by the agent. However, there is an asymmetry. A higher capacity model for an agent makes it harder for the individual to learn, as there are more parameters. Simultaneously, a higher capacity model for the group, implemented as a higher capacity model for each member agent, makes it easier for a group to integrate a new member. To optimize for a group of agents then requires one to make a trade-off in capacity, as each individual agent seeks to decrease capacity, but there is pressure from the group to increase capacity of all members. This pressure exists because as individual agent’s capacities are reduced, so too are their abilities to model other agents, and thereby to establish pro-social behavioural patterns. I then consider a basic two-level (dual process) Bayesian model of social reasoning and a set of three parameters of capacity that are required to implement such a model. Considering these three capacities as dependent elements in a free energy minimization for a group leads to a “sweet surface” in a three-dimensional space defining the triplet of parameters that each agent must use should they hope to minimize free energy as a group. Finally, I relate these three parameters to three notions of freedom and equality in human social organization, and postulate a correspondence between freedom and model capacity. That is, models with higher capacity, have more freedom as they can interact with more datasets.

Discovering Differences and Similarities among Species Based on Numeric Features of microRNAs

Numeric features of microRNA (miRNA) are different from the other RNAs and play a key role in the course of miRNA recognition. Are there significant differences about such numeric features between different species? Are there some species specific about it? In order to answer questions, we implemented the Kolmogorov-Smirnov test for 32 species based on 132 numeric features of miRNAs. Results demonstrate that almost all kinds of miRNA secondary structures matching frequencies show highly similar, and this means that such secondary structure tend to be specific to miRNAs. Length of pre-miRNA, minimal free energy (MFE), and number of stacks show bigger difference between different species, and this means that such features tend to be species-specific. In order to discover differences and similarities among species based on numeric features of miRNAs, we design two tools-species difference map and feature difference map. By species difference map, we find that numeric features of miRNAs can represent class attribute of species and the map basically describe relationship between different species. By feature difference map, we find that there are huge difference about difference of every numeric feature between different species, and this means that strength of such difference is not uniform. Meantime, it means that numeric feature of miRNAs can represent different attributes of corresponding species. Our study present relationship between different species by brand new style-numeric features of miRNAs.

Ability to form duplexes as a factor of intracellular microRNA distribution

Background. The regulation of the content of mature microRNAs (miRNAs) in different cell compartments – the nucleus (N) and the cytoplasm (C) – makes it possible to control their availability for participation in RNA-mediated interference processes. Structurally different miRNAs, processed from different precursors (pre-miRNA), can form duplexes between molecules containing complementary sequences. The appearance of such duplexes can be considered as one of the mechanisms of miRNA activity regulation in respect to their target mRNAs. Objectives. Analysis of the miRNA distribution between nucleus and cytoplasm depending on the energy of duplex formation. Materials and methods. Data on the content of different miRNAs in the nucleus and cytoplasm in two cell lines of different origin: 5-8F of human nasopharyngeal carcinoma (NPC) and postmitotic neurons of the cerebral cortex of rat – has been used. The miRNA sequences used for analysis were taken from the miRBase database, version 22. Bioinformatic analysis of miRNA sequences for detection of molecules capable of forming miRNA duplexes and determination of their minimal free energy (MFE) of formation was carried out with the help of programs: RegRNA, version 2.0, and RNAup. Results. For the first time, a comparative analysis of the intracellular distribution N/C of different miRNAs depending on the energy of duplex formation was performed. Results of bioinformatic analysis of miRNA sequencing in 5-8F cells of human nasopharyngeal carcinoma showed that miRNAs capable of forming high-energy, i. e. more stable, duplexes, accumulate in the cytoplasm, while miRNAs forming low-energy duplexes have a larger N/C value, i. e. the level of these miRNAs is higher in the nucleus. In addition, we show that N/C distribution of miRNAs capable of forming high-energy duplexes is independent from the presence of certain short motifs, that are supposedly associated with their nuclear localization. Conclusion. The revealed enrichment of the pool of cytoplasmic miRNAs by molecules capable of forming more energetically stable duplexes may represent an additional mechanism of regulating miRNA activity in respect to their target mRNAs due to the sequestration of miRNA duplexes in the cytoplasm preventing miRNA interaction with mRNAs.

Towards physical principles of biological evolution

Biological systems reach organizational complexity that far exceeds the complexity of any known inanimate objects. Biological entities undoubtedly obey the laws of quantum physics and statistical mechanics. However, is modern physics sufficient to adequately describe, model and explain the evolution of biological complexity? Detailed parallels have been drawn between statistical thermodynamics and the population-genetic theory of biological evolution. Based on these parallels, we outline new perspectives on biological innovation and major transitions in evolution, and introduce a biological equivalent of thermodynamic potential that reflects the innovation propensity of an evolving population. Deep analogies have been suggested to also exist between the properties of biological entities and processes, and those of frustrated states in physics, such as glasses. Such systems are characterized by frustration whereby local state with minimal free energy conflict with the global minimum, resulting in “emergent phenomena”. We extend such analogies by examining frustration-type phenomena, such as conflicts between different levels of selection, in biological evolution. These frustration effects appear to drive the evolution of biological complexity. We further address evolution in multidimensional fitness landscapes from the point of view of percolation theory and suggest that percolation at level above the critical threshold dictates the tree-like evolution of complex organisms. Taken together, these multiple connections between fundamental processes in physics and biology imply that construction of a meaningful physical theory of biological evolution might not be a futile effort. However, it is unrealistic to expect that such a theory can be created in one scoop; if it ever comes to being, this can only happen through integration of multiple physical models of evolutionary processes. Furthermore, the existing framework of theoretical physics is unlikely to suffice for adequate modeling of the biological level of complexity, and new developments within physics itself are likely to be required.

Component Thermodynamical Selection Based Gene Expression Programming for Function Finding

Gene expression programming (GEP), improved genetic programming (GP), has become a popular tool for data mining. However, like other evolutionary algorithms, it tends to suffer from premature convergence and slow convergence rate when solving complex problems. In this paper, we propose an enhanced GEP algorithm, called CTSGEP, which is inspired by the principle of minimal free energy in thermodynamics. In CTSGEP, it employs a component thermodynamical selection (CTS) operator to quantitatively keep a balance between the selective pressure and the population diversity during the evolution process. Experiments are conducted on several benchmark datasets from the UCI machine learning repository. The results show that the performance of CTSGEP is better than the conventional GEP and some GEP variations.

An Improved Estimation of Distribution Algorithms Based on the Minimal Free Energy

To systematically harmonize the conflict between selective pressure and population diversity in estimation of distribution algorithms, an improved estimation of distribution algorithms based on the minimal free energy (IEDA) is proposed in this paper. IEDA conforms to the principle of minimal free energy in simulating the competitive mechanism between energy and entropy in annealing process, in which population diversity is measured by similarity entropy and the minimum free energy is simulated with an efficient and effective competition by free energy component. Through solving some typical numerical optimization problems, satisfactory results were achieved, which showed that IEDA was a preferable algorithm to avoid the premature convergence effectively and reduce the cost in search to some extent.