Evolutionary Representation Research Articles

Interspecies variation in plant surface water storage capacity in alpine meadows: Prediction based on plant ecological strategies

Rainfall interception by vegetation is essential in precipitation redistribution within ecosystems and greatly affects water services. However, the patterns of interspecies variation in plant surface water storage (PWS) remain unclear, which greatly limits our understanding of the relationship between species composition and vegetation rainfall interception. Thus, we aimed to examine whether the plant ecological strategy, which is an internal driving force and comprehensive characteristic of plant traits, can serve as a PWS predictor. The PWS of thirty-nine common plant species in natural, degraded, and restored alpine meadows within the Ruoergai wetland was compared, and the variability in PWS between these species was examined from both plant trait and ecological strategy perspectives. The results showed that PWS per plant (IWSC) ranged from 0.12 to 9.32 mL plant−1, and PWS based on projected area (PSCI) and fresh weight (PMS) ranged from 0.01 to 0.94 mm and 0.11 to 1.29 g g−1, respectively. The PSCI was comparatively lower in Cyperaceae, whereas the PMS was relatively lower in Polygonaceae, and Poaceae. The correlations between PWS and multidimensional plant traits are complex for predicting PWS but can be clarified by considering plant ecological strategies. Further regression analysis revealed that as an evolutionary representation of the crucially related plant traits, plant competition significantly positively affected both IWSC and PSCI, and stress tolerance obviously negatively affected these two parameters (P < 0.05). The correlation between plant strategy and PSCI implied that the increased dominance of competitive species in alpine meadows may intensify interception loss, thereby hindering rainfall allocation to soil and exacerbating water scarcity.

Predicting Protein-Protein Interactions via Random Ferns with Evolutionary Matrix Representation

Protein-protein interactions (PPIs) play a crucial role in understanding disease pathogenesis, genetic mechanisms, guiding drug design, and other biochemical processes, thus, the identification of PPIs is of great importance. With the rapid development of high-throughput sequencing technology, a large amount of PPIs sequence data has been accumulated. Researchers have designed many experimental methods to detect PPIs by using these sequence data, hence, the prediction of PPIs has become a research hotspot in proteomics. However, since traditional experimental methods are both time-consuming and costly, it is difficult to analyze and predict the massive amount of PPI data quickly and accurately. To address these issues, many computational systems employing machine learning knowledge were widely applied to PPIs prediction, thereby improving the overall recognition rate. In this paper, a novel and efficient computational technology is presented to implement a protein interaction prediction system using only protein sequence information. First, the Position-Specific Iterated Basic Local Alignment Search Tool (PSI-BLAST) was employed to generate a position-specific scoring matrix (PSSM) containing protein evolutionary information from the initial protein sequence. Second, we used a novel data processing feature representation scheme, MatFLDA, to extract the essential information of PSSM for protein sequences and obtained five training and five testing datasets by adopting a five-fold cross-validation method. Finally, the random fern (RFs) classifier was employed to infer the interactions among proteins, and a model called MatFLDA_RFs was developed. The proposed MatFLDA_RFs model achieved good prediction performance with 95.03% average accuracy on Yeast dataset and 85.35% average accuracy on H. pylori dataset, which effectively outperformed other existing computational methods. The experimental results indicate that the proposed method is capable of yielding better prediction results of PPIs, which provides an effective tool for the detection of new PPIs and the in-depth study of proteomics. Finally, we also developed a web server for the proposed model to predict protein-protein interactions, which is freely accessible online at http://120.77.11.78:5001/webserver/MatFLDA_RFs.

An evolutionary spectral representation for blind separation of biosignals

Blind source separation (BSS) methods are used to separate sources from a mixed observations with very little prior knowledge of the mixing coefficients or sources. In this paper we propose an evolutionary spectral representation to implement BSS. Introduced by Priestley, evolutionary spectral theory generalizes the definition of spectrum for nonstationary processes. Under certain conditions, the evolutionary spectrum at each instant of time can be estimated from a single realization of a process such that it is possible to study processes with changing spectral patterns. In particular we are interested in the problem of separation of individual biosignals from electrophysiological recordings mixed by volume conduction. As biosignals such as electrocardiogram and electroencephalogram recordings are prime examples of nonstationary signals, evolutionary spectral representations can be used for the analysis of them. Our proposed evolutionary spectral representation is based on the discrete prolate spheroidal sequences (DPSS). Also known as Slepian sequences, the DPSS are defined to be the sequences with maximum spectral concentration for a given duration and bandwidth. Using the relation between discrete evolutionary transform and evolutionary periodogram, we derive the Slepian evolutionary spectrum. After the evolutionary spectrum is computed, we implement it for the BSS problem and compare with the well known time-frequency methods (Wigner-Ville distribution and S-transform) for performance evaluation.

STATIONARY CONNECTED CURVES IN HILBERT SPACES

In this article the structure of non-stationary curves which are stationary connected in Hilbert space is studied using triangular models of non-self-adjoint operator. The concept of evolutionary representability plays here an important role. It is proved that if one of two curves in Hilbert space is evolutionary representable and the curves are stationary connected, then another curve is evolutionary representable too. These curves are studied firstly. The structure of a cross-correlation function in the case when operator, defining the evolutionary representation, has one-dimensional non-Hermitian subspace (the spectrum is discreet and situated in the upper complex half-plane or has infinite multiplicity at zero (Volterra operator)) is studied.

Topology of viral evolution

The tree structure is currently the accepted paradigm to represent evolutionary relationships between organisms, species or other taxa. However, horizontal, or reticulate, genomic exchanges are pervasive in nature and confound characterization of phylogenetic trees. Drawing from algebraic topology, we present a unique evolutionary framework that comprehensively captures both clonal and reticulate evolution. We show that whereas clonal evolution can be summarized as a tree, reticulate evolution exhibits nontrivial topology of dimension greater than zero. Our method effectively characterizes clonal evolution, reassortment, and recombination in RNA viruses. Beyond detecting reticulate evolution, we succinctly recapitulate the history of complex genetic exchanges involving more than two parental strains, such as the triple reassortment of H7N9 avian influenza and the formation of circulating HIV-1 recombinants. In addition, we identify recurrent, large-scale patterns of reticulate evolution, including frequent PB2-PB1-PA-NP cosegregation during avian influenza reassortment. Finally, we bound the rate of reticulate events (i.e., 20 reassortments per year in avian influenza). Our method provides an evolutionary perspective that not only captures reticulate events precluding phylogeny, but also indicates the evolutionary scales where phylogenetic inference could be accurate.

Agent-based modeling of climate policy: An introduction to the ENGAGE multi-level model framework

Model-based support of climate policy is scientifically challenging because climate change involves linked physical and social systems that operate on multiple levels: local, national, and international. As a result, models must employ some strongly simplifying assumptions. The most frequently used models typically assume hyper-rational and homogenous human behavior. These ensure tractability but, as a trade-off, abstract away the effects of less-than-rational decision-making and actor heterogeneity on domestic policy effectiveness and the influence of domestic constituents on international policy agreement. In this paper, we introduce a multi-level model framework, called ENGAGE, that relaxes some common modeling assumptions by adopting an agent-based approach. ENGAGE is styled after the Putnam two-level game, in which negotiators at the international level are constrained by the heterogeneous policy preferences and power of constituents at the domestic level. We proceed to provide a detailed description and demonstration of the prototype domestic-level module. Domestic actors include firms and households who function as agents within an evolutionary representation of economic growth, energy technology, and climate change. This allows an evaluation of policies that accounts for agent decision-making and social and technological change. Ultimately, we plan to use the ENGAGE model to simulate the two-way dynamic feedback between international agreements and domestic policy outcomes.

Tree pruner: An efficient tool for selecting data from a biased genetic database

BackgroundLarge databases of genetic data are often biased in their representation. Thus, selection of genetic data with desired properties, such as evolutionary representation or shared genotypes, is problematic. Selection on the basis of epidemiological variables may not achieve the desired properties. Available automated approaches to the selection of influenza genetic data make a tradeoff between speed and simplicity on the one hand and control over quality and contents of the dataset on the other hand. A poorly chosen dataset may be detrimental to subsequent analyses.ResultsWe developed a tool, Tree Pruner, for obtaining a dataset with desired evolutionary properties from a large, biased genetic database. Tree Pruner provides the user with an interactive phylogenetic tree as a means of editing the initial dataset from which the tree was inferred. The tree visualization changes dynamically, using colors and shading, reflecting Tree Pruner actions. At the end of a Tree Pruner session, the editing actions are implemented in the dataset.Currently, Tree Pruner is implemented on the Influenza Research Database (IRD). The data management capabilities of the IRD allow the user to store a pruned dataset for additional pruning or for subsequent analysis. Tree Pruner can be easily adapted for use with other organisms.ConclusionsTree Pruner is an efficient, manual tool for selecting a high-quality dataset with desired evolutionary properties from a biased database of genetic sequences. It offers an important alternative to automated approaches to the same goal, by providing the user with a dynamic, visual guide to the ongoing selection process and ultimate control over the contents (and therefore quality) of the dataset.

Signal Reconstruction from Nonuniformly Spaced Samples Using Evolutionary Slepian Transform-Based POCS

We consider the reconstruction of signals from nonuniformly spaced samples using a projection onto convex sets (POCSs) implemented with the evolutionary time-frequency transform. Signals of practical interest have finite time support and are nearly band-limited, and as such can be better represented by Slepian functions than by sinc functions. The evolutionary spectral theory provides a time-frequency representation of nonstationary signals, and for deterministic signals the kernel of the evolutionary representation can be derived from a Slepian projection of the signal. The representation of low pass and band pass signals is thus efficiently done by means of the Slepian functions. Assuming the given nonuniformly spaced samples are from a signal satisfying the finite time support and the essential band-limitedness conditions with a known center frequency, imposing time and frequency limitations in the evolutionary transformation permit us to reconstruct the signal iteratively. Restricting the signal to a known finite time and frequency support, a closed convex set, the projection generated by the time-frequency transformation converges into a close approximation to the original signal. Simulation results illustrate the evolutionary Slepian-based transform in the representation and reconstruction of signals from irregularly-spaced and contiguous lost samples.

On Interrelations between the Evolutionary Theory and the Orthodoxy (a conceptual analysis)

The economy is evolving and at the same time evaluates (measures) itself in the course of this evolution. This feature makes it possible to solve in perspective the problem of synthesis between the evolutionary theory and the orthodoxy. Problems confronting such a synthesis of the two theories are considered in the paper. In particular, it has been shown that there exists a non-equilibrium profit in the framework of equilibrium prices which reflects the degree of non-equilibrium between production needs and possibilities. Also, it has been shown that an evolutionary representation of the economy may be provided by means of so called macrogenerations. The formation of prices (being a function of the orthodoxy) depends on the behavior of macrogenerations.

Automating the drug scheduling of cancer chemotherapy via evolutionary computation

This paper presents the optimal control of drug scheduling in cancer chemotherapy using a distributed evolutionary computing software. Unlike conventional methods that often require gradient information or hybridization of different approaches in drug scheduling, the proposed evolutionary optimization methodology is simple and capable of automatically finding the near-optimal solutions for complex cancer chemotherapy problems. It is shown that different number of variable pairs in evolutionary representation for drug scheduling can be easily implemented via the software, since the computational workload is shared and distributed among multiple computers over the Internet. Simulation results show that the proposed evolutionary approach produces excellent control of drug scheduling in cancer chemotherapy, which are competitive or equivalent to the best solutions published in literature.

Gene expression and fast construction of distributed evolutionary representation.

The gene expression process in nature produces different proteins in different cells from different portions of the DNA. Since proteins control almost every important activity in a living organism, at an abstract level, gene expression can be viewed as a process that evaluates the merit or "fitness" of the DNA. This distributed evaluation of the DNA would not be possible without a decomposed representation of the fitness function defined over the DNAs. This paper argues that, unless the living body was provided with such a representation, we have every reason to believe that it must have an efficient mechanism to construct this distributed representation. This paper demonstrates polynomial-time computability of such a representation by proposing a class of efficient algorithms. The main contribution of this paper is two-fold. On the algorithmic side, it offers a way to scale up evolutionary search by detecting the underlying structure of the search space. On the biological side, it proves that the distributed representation of the evolutionary fitness function in gene expression can be computed in polynomial-time. It advances our understanding about the representation construction in gene expression from the perspective of computing. It also presents experimental results supporting the theoretical performance of the proposed algorithms.

Evolutionary Collision Free Optimal Trajectory Planning for Mobile Robots

We present an evolutionary trajectory planning method for mobile robots following a novel evolution strategy (NES) algorithm. The 2-D trajectory planning problem of a mobile robot among polygonal obstacles is formulated as a constrained time-optimum control problem considering motion. Unlike traditional evolutionary representation, special representation of individuals and crossover are used for the evolutionary search. Swapping crossover, insertion, and deletion mutations are used as background operators for maximum evolutionary algorithm flexibility. Polygonal obstacles in the world coordinate frame are modeled as circles from visibility and sensor modeling concepts. An appropriate cost (fitness) function is constructed as the only link between the evolutionary algorithm and environment. Our proposed evolution is effective for collision-free optimum trajectory planning in robot simulation within a heavily constrained (obstacle) environment.