Estimating phenotypic features (physical and biochemical traits) in a biological organism from their genomic sequence alone and/or environmental conditions has major applications in anthropological paleontology and criminal forensics, for example. To what extent do genomic sequences generally and causally determine phenotypic features of organisms, environmental conditions aside? We present results of two studies, one in blackfly (Insecta:Diptera:Simuliidae) larvae in two species (Simulium ignescens and S. tunja) with four phenotypic features, including the area and spot pattern of the cephalic apotome (in the form of a latin cross on the dorsal side of the head), the postgenal cleft (area under the head on the ventral side) and general body color in larva specimens; the second in strains of Arabidopsis thaliana. They establish that a substantial component of these phenotypic features (over 75 percent) are at least logically inferable, if not causally determined, by genomic fragments alone, despite the fact that these phenotypic features are not 100 percent determined entirely by genetic traits. These results suggest that it is possible to infer the genetic contribution in the determination of specific phenotypic features of a biological organism, without recourse to the causal chain of metabolomics and proteomic events leading to them from genomic sequences.

DNA has emerged as a new computational resource for data encoding and processing. The fundamental problem of DNA Codeword Design (CWD) calls for finding effective ways to encode and process data in DNA. The problem has shown to be of interest in other areas as well, including computational memories, self-assembly and phylogenetic analysis, among others. In prior work, a framework to analyze this problem has been developed and simple versions of CWD have been shown to be NP-complete using any single reasonable metric that approximates the Gibbs energy, thus practically making it very difficult to find a general procedure for finding optimal efficient encodings. We present a Self-adaptive Evolutionary Algorithm for CWD (SaEA-CWD) as an extension of the Hybrid Adaptive Evolutionary algorithm (HAEA). SaEA-CWD is a parameter adaptation technique that automatically adapts the rates of its genetic operator applications to exploit structural properties of the search space to improve the speed and quality of the solutions. An implementation and preliminary results are evaluated in spaces where searches are already prohibitive to ordinary methods (such as 8- and 10-mers) due to the combinatorial explosion of the solution DNA space. Applications to other problems are suggested, such as a general technique for dimensionality reduction based on SaEA-CWD.

Anthropomorphic representations of software agents (avatars) are being used as user interfaces in order to enhance communication bandwidth and interaction with human users. They have been traditionally programmed and controlled by ontologies designed according to intuitive and heuristic considerations. More recently, recurrent neural nets have been trained as neurocontrollers for emotional displays in avatars, on a continuous scale of negative, neutral, and positive feedback, that are meaningful to users in the context of tutoring sessions on a particular domain (computer literacy). We report on a new neurocontrol developed as a recurrent network to autonomously and dynamically generate and synchronize the movements of facial features such as lips, eyes, eyebrows, and gaze in order to produce facial displays that convey high information content on nonverbal behavior to untrained human users. The neurocontrol is modular and can be easily integrated with semantic processing modules of larger agents that operate in real-time, such as videoconference systems, tutoring systems, and more generally, user interfaces coupled with affective computing modules for naturalistic communication. A novel technique, cascade inversion, provides an alternative to backpropagation through time where the latter may fail to learn recurrent neural nets from previously learned modules playing a role in the final solution.

The computer game Defend and Gather was created to evaluate two neural-based agents' ability to learn how to play and win the game. The agents navigate an environment to find resources and defeat enemies. Traditional game agents are often neither challenging enough to human opponents over time, nor scalable to environments not anticipated at the time the agents were originally programmed. We show that neural- based agents have the ability to learn from their human counterparts or from the environment, thus remaining competitive over time. The neural-based agents developed for Defend and Gather have the ability to formulate tactics within increasingly difficult environments involving more sophisticated enemies and can win the game over seventy-five percent of the time.

In this paper, we provide an evaluation for our proposed real-time virtual videophone using talking heads over low-bandwidth wireless networks, in which a person's face is substituted with a talking head to save bandwidth. Results show large enough gains in communication bandwidth to make the architecture feasible.

Associative memories based on DNA-affinity have been proposed based in L. M. Adleman (1994) and E. Baum (1995). Previously, we have quantified the quality of retrieval of genomic information in simulation as stated in M. Garzon et al. (2003). Here, the ability of two types of DNA-based memories to store abiotic data and retrieve semantic information is evaluated for soundness and compared to state-of-the-art symbolic methods available, such as LSA (latent semantic analysis) of T. K. Landauer et al. Their ability is poor when performed without a proper compaction procedure. However, when the corpus is summarized through a selection protocol based on PCR or a training procedure on J. Chen et al. (2004) to extract useful information, their performance is much closer to that of LSA, according to human expert ratings. These results are expected to improve and scale up when actual DNA molecules are employed in real test tubes, currently a feasible goal.

We show that fuzzy neural nets can rate the emotional feedback message implicit in human facial expressions at a level comparable to human performance. We also identify primitive and derived features in facial expressions and animations. We also report on a partial solution to the difficult inverse problem of generating naturalistic facial expressions from summary abstract descriptions of the emotional feedback to be conveyed.

Biomolecular computing (BMC) aims to capture the innumerable advantages that biological molecules have gained in the course of millions of years of evolution to perform computation unfeasible on conventional electronic computers. While biomolecules have resolved fundamental problems as a parallel computer system that we are just beginning to decipher, BMC still suffers from our inability to harness these properties to bring biomolecular computations to levels of reliability, efficiency and scalability that are now taken for granted with conventional solid-state based computers. The authors explore an alternative approach to exploiting these properties by building virtual test tubes in software that would capture the fundamental advantages of biomolecules, in the same way that evolutionary algorithms capture in silico the key properties of Darwinian evolution. We use a previously built tool, Edna, to explore the capabilities of the new paradigm.

Molecular computing is a discipline that aims at harnessing individual molecules at nanoscales for computational purposes. The best-studied molecules for this purpose to date have been DNA and bacteriorhodopsin. Biomolecular computing allows one to realistically entertain, for the first time in history, the possibility of exploiting the massive parallelism at nanoscales inherent in natural phenomena to solve computational problems. The implementation of evolutionary algorithms in biomolecules would bring full circle the biological analogy and present an attractive alternative to meet large demands for computational power. The paper presents a review of the most important advances in biomolecular computing in the last few years. Major achievements to date are outlined, both experimental and theoretical, and major potential advances and challenges for practitioners in the foreseeable future are identified. A list of sources and major events in the field has been compiled in the Appendix, although no exhaustive survey of the expanding literature is intended.

info:eu-repo/semantics/published

DNA computing holds out the promise of important and significant connections between computers and living systems, as well as promising massively parallel computations. Before these promises are fulfilled, however, important challenges related to err

For a given recurrent neural network, a discrete-time model may have asymptotic dynamics different from the one of a related continuous-time model. In this article, we consider a discrete-time model that discretizes the continuous-time leaky integrat or model and study its parallel, sequential, block-sequential, and distributed dynamics for symmetric networks. We provide sufficient (and in many cases necessary) conditions for the discretized model to have the same cycle-free dynamics of the corresponding continuous-time model in symmetric networks.

A polynomial time isomorphism test for a class of groups, properly containing the class of abelian groups, is presented. Isomorphism testing of group presentations for (a subclass of) the same class of groups is shown to be (graph) isomorphism complete. These seem to be the first known isomorphism complete problems in group theory. Subexponential tests are presented as well for rings and algebras.