Making decisions with the future in mind: Developmental and comparative identification of mental time travel

Mechanisms that produce behavior which increase future survival chances provide an adaptive advantage. The flexibility of human behavior is at least partly the result of one such mechanism, our ability to travel mentally in time and entertain potential future scenarios. We can study mental time travel in children using language. Current results suggest that key developments occur between the ages of three to five. However, linguistic performance can be misleading as language itself is developing. We therefore advocate the use of methodologies that focus on future-oriented action. Mental time travel required profound changes in humans' motivational system, so that current behavior could be directed to secure not just present, but individually anticipated future needs. Such behavior should be distinguishable from behavior based on current drives, or on other mechanisms. We propose an experimental paradigm that provides subjects with an opportunity to act now to satisfy a need not currently experienced. This approach may be used to assess mental time travel in nonhuman animals. We conclude by describing a preliminary study employing an adaptation of this paradigm for children. (c) 2005 Elsevier Inc. All rights reserved.

Identification and molecular modeling of a novel, plant-like, human purple acid phosphatase

Purple acid phosphatases are a family of binuclear metallohydrolases that have been identified in plants, animals and fungi. Only one isoform of similar to 35 kDa has been isolated from animals, where it is associated with bone resorption and microbial killing through its phosphatase activity, and hydroxyl radical production, respectively. Using the sensitive PSI-BLAST search method, sequences representing new purple acid phosphatase-like proteins have been identified in mammals, insects and nematodes. These new putative isoforms are closely related to the similar to 55 kDa purple acid phosphatase characterized from plants. Secondary structure prediction of the new human isoform further confirms its similarity to a purple acid phosphatase from the red kidney bean. A structural model for the human enzyme was constructed based on the red kidney bean purple acid phosphatase structure. This model shows that the catalytic centre observed in other purple acid phosphatases is also present in this new isoform. These observations suggest that the sequences identified in this study represent a novel subfamily of plant-like purple acid phosphatases in animals and humans. (c) 2006 Elsevier B.V. All rights reserved.

Identification of a novel frameshift mutation in the giant muscle filament titin in a large Australian family with dilated cardiomyopathy

Dilated cardiomyopathy (DCM) is an etiologically heterogeneous cardiac disease characterized by left ventricular dilation and systolic dysfunction. Approximately 25-30% of DCM patients show a family history of mainly autosomal dominant inheritance. We and others have previously demonstrated that mutations in the giant muscle filament titin (TTN) can cause DCM. However, the prevalence of titin mutations in familial DCM is unknown. In this paper, we report a novel heterozygous 1-bp deletion mutation (c.62890delG) in TTN that cosegregates with DCM in a large Australian pedigree (A3). The TTN deletion mutation c.62890delG causes a frameshift, thereby generating a truncated A-band titin due to a premature stop codon (p.E20963KfsX10) and the addition of ten novel amino acid residues. The clinical phenotype of DCM in kindred A3 demonstrates incomplete penetrance and variable expressivity. Finally, protein analysis of a skeletal muscle biopsy sample from an affected member did not reveal the predicted truncated titin isoform although the aberrant mRNA was present, suggesting posttranslational modification and degradation of the truncated protein. The identification of a novel disease-causing mutation in the giant titin gene in a third large family with DCM indicates that mutations in titin may account for a significant portion of the genetic etiology in familial DCM.

Identification of candidate genes at the corticoseptal boundary during development

Cortical midline glia are critical to the formation of the corpus callosum during development. The glial wedge is a Population of midline glia that is located at the corticoseptal boundary and expresses repulsive/growth-inhibitory molecules that guide callosal axons as they cross the midline. The glial wedge are the first cells within the cortex to express GFAP and thus may express molecules specific for glial maturation. The corticoseptal boundary is a genetically defined boundary between the cingulate cortex (dorsal telencephalon) and the septum (ventral telencephalon). The correct dorso-ventral position of this boundary is vital to the formation of both the glial wedge and the corpus callosum. Our aim was to identify genes expressed specifically within the glial wedge that might be involved in either glial differentiation, formation of the corticoseptal boundary or development of the corpus callosum. To identify such genes we have performed a differential display PCR screen comparing RNA isolated from the glial wedge with RNA isolated from control tissues such as the neocortex and septum, of embryonic day 17 mouse brains. Using 200 different combinations of primers, we identified and cloned 67 distinct gene fragments. In situ hybridization analysis confirmed the differential expression of many of the genes, and showed that clones G24F3, G39F8 and transcription factor LZIP have specific expression patterns in the telencephalon of embryonic and postnatal brains. An RNase Protection Assay (RPA) revealed that the expression of G39F8, G24173 and LZIP increase markedly in the telencephalon at E16 and continue to be expressed until at least PO, during the period when the corpus callosum is forming. (c) 2005 Elsevier B.V. All rights reserved.

Identification of the dominant translation start site in the attB1 sequence of the pET-DEST42 Gateway vector

Gateway technology is a powerful system for converting a single entry vector into a wide variety of expression vectors. We expressed recombinant influenza matrix protein M1 (FMP), a potent antigen for cytotoxic T cells, using the Gateway vector pET-DEST42 containing the FMP cDNA, and purified the expressed FMP as a single 32 kDa recombinant protein. N-terminal and internal protein sequencing, however, showed that the recombinant FMP contained an extra 10 amino acids fused to the N-terminal of native FMP. Further investigation of the DNA sequence adjacent to the 5'-FMP cDNA indicated that the TTG in the attB1 site (30bp upstream of the ATG in the 5'-FMP cDNA) behaved as a dominant translation start site, resulting in a 10 amino acid extension of the recombinant FMP. Thus, it is possible that recombinant proteins produced by this Gateway vector contain unexpected vector-derived peptides, which may affect experimental outcomes. (c) 2006 Elsevier Inc. All rights reserved.

Stochastic dynamic programming for election timing: A game theory approach

In this paper, we consider dynamic programming for the election timing in the majoritarian parliamentary system such as in Australia, where the government has a constitutional right to call an early election. This right can give the government an advantage to remain in power for as long as possible by calling an election, when its popularity is high. On the other hand, the opposition's natural objective is to gain power, and it will apply controls termed as "boosts" to reduce the chance of the government being re-elected by introducing policy and economic responses. In this paper, we explore equilibrium solutions to the government, and the opposition strategies in a political game using stochastic dynamic programming. Results are given in terms of the expected remaining life in power, call and boost probabilities at each time at any level of popularity.

Stochastic models for mainland-island metapopulations in static and dynamic landscapes

This paper has three primary aims: to establish an effective means for modelling mainland-island metapopulations inhabiting a dynamic landscape: to investigate the effect of immigration and dynamic changes in habitat on metapopulation patch occupancy dynamics; and to illustrate the implications of our results for decision-making and population management. We first extend the mainland-island metapopulation model of Alonso and McKane [Bull. Math. Biol. 64:913-958,2002] to incorporate a dynamic landscape. It is shown, for both the static and the dynamic landscape models, that a suitably scaled version of the process converges to a unique deterministic model as the size of the system becomes large. We also establish that. under quite general conditions, the density of occupied patches, and the densities of suitable and occupied patches, for the respective models, have approximate normal distributions. Our results not only provide us with estimates for the means and variances that are valid at all stages in the evolution of the population, but also provide a tool for fitting the models to real metapopulations. We discuss the effect of immigration and habitat dynamics on metapopulations, showing that mainland-like patches heavily influence metapopulation persistence, and we argue for adopting measures to increase connectivity between this large patch and the other island-like patches. We illustrate our results with specific reference to examples of populations of butterfly and the grasshopper Bryodema tuberculata.

Stochastic models for regulatory networks of the genetic toggle switch

Bistability arises within a wide range of biological systems from the A phage switch in bacteria to cellular signal transduction pathways in mammalian cells. Changes in regulatory mechanisms may result in genetic switching in a bistable system. Recently, more and more experimental evidence in the form of bimodal population distributions indicates that noise plays a very important role in the switching of bistable systems. Although deterministic models have been used for studying the existence of bistability properties under various system conditions, these models cannot realize cell-to-cell fluctuations in genetic switching. However, there is a lag in the development of stochastic models for studying the impact of noise in bistable systems because of the lack of detailed knowledge of biochemical reactions, kinetic rates, and molecular numbers. in this work, we develop a previously undescribed general technique for developing quantitative stochastic models for large-scale genetic regulatory networks by introducing Poisson random variables into deterministic models described by ordinary differential equations. Two stochastic models have been proposed for the genetic toggle switch interfaced with either the SOS signaling pathway or a quorum-sensing signaling pathway, and we have successfully realized experimental results showing bimodal population distributions. Because the introduced stochastic models are based on widely used ordinary differential equation models, the success of this work suggests that this approach is a very promising one for studying noise in large-scale genetic regulatory networks.

Successes and failures in assessing acquired language disorders using an Internet-based telerehabilitation system

We have used a telerehabilitation system (eREHAB) to remotely assess acquired language disorders via the Internet. The system was used to establish a 128 kbit/s videoconference between two sites and allowed a remote language assessment to be conducted using the standardized Boston Diagnostic Aphasia Examination (BDAE). The system had the capacity to display text and images, and could play pre-recorded instructions to the participant via various built-in tools. A touch screen allowed tasks involving picture identification to be completed easily. Eighteen participants with a diagnosis of an acquired language disorder were simultaneously assessed using the eREHAB system, and in the traditional face-to-face manner by two speech pathologists. There was very high agreement between the two assessors, with weighted kappa scores of 0.8–1.0 for 88% of the sub-tests of the BDAE. There was also high agreement (80–100%) and high kappa scores (0.67–0.90) between assessors on the six rating scales relating to language characteristics. The agreement between the two assessors for the diagnosis of the type of aphasia was 83%. Limitations of the system related mainly to problems inherent in IP videoconferencing. The inability to maintain the preferred speed of 128 kbit/s for the duration of the videoconference and the resultant increase in video and audio breakup and latency affected the clinician’s ability to administer the BDAE with the same ease and accuracy as in face-to-face administration. These difficulties were exacerbated when participants presented with a moderate to severe language disorder, auditory comprehension deficits or significant hearing loss. Despite these limitations, a valid assessment of language disorder was found to be feasible via this telerehabilitation application.

Stochastic delay differential equations for genetic regulatory networks

Time delay is an important aspect in the modelling of genetic regulation due to slow biochemical reactions such as gene transcription and translation, and protein diffusion between the cytosol and nucleus. In this paper we introduce a general mathematical formalism via stochastic delay differential equations for describing time delays in genetic regulatory networks. Based on recent developments with the delay stochastic simulation algorithm, the delay chemical masterequation and the delay reaction rate equation are developed for describing biological reactions with time delay, which leads to stochastic delay differential equations derived from the Langevin approach. Two simple genetic regulatory networks are used to study the impact of' intrinsic noise on the system dynamics where there are delays. (c) 2006 Elsevier B.V. All rights reserved.

Stochastic modelling and simulations for solute transport in porous media

A stochastic model for solute transport in aquifers is studied based on the concepts of stochastic velocity and stochastic diffusivity. By applying finite difference techniques to the spatial variables of the stochastic governing equation, a system of stiff stochastic ordinary differential equations is obtained. Both the semi-implicit Euler method and the balanced implicit method are used for solving this stochastic system. Based on the Karhunen-Loeve expansion, stochastic processes in time and space are calculated by means of a spatial correlation matrix. Four types of spatial correlation matrices are presented based on the hydraulic properties of physical parameters. Simulations with two types of correlation matrices are presented.

Parallel implementation of stochastic simulation for large scale cellular processes

Experimental and theoretical studies have shown the importance of stochastic processes in genetic regulatory networks and cellular processes. Cellular networks and genetic circuits often involve small numbers of key proteins such as transcriptional factors and signaling proteins. In recent years stochastic models have been used successfully for studying noise in biological pathways, and stochastic modelling of biological systems has become a very important research field in computational biology. One of the challenge problems in this field is the reduction of the huge computing time in stochastic simulations. Based on the system of the mitogen-activated protein kinase cascade that is activated by epidermal growth factor, this work give a parallel implementation by using OpenMP and parallelism across the simulation. Special attention is paid to the independence of the generated random numbers in parallel computing, that is a key criterion for the success of stochastic simulations. Numerical results indicate that parallel computers can be used as an efficient tool for simulating the dynamics of large-scale genetic regulatory networks and cellular processes

Customer information system data pre-processing with feature selection techniques for non-technical losses prediction in an electricity market

Non-technical losses (NTL) identification and prediction are important tasks for many utilities. Data from customer information system (CIS) can be used for NTL analysis. However, in order to accurately and efficiently perform NTL analysis, the original data from CIS need to be pre-processed before any detailed NTL analysis can be carried out. In this paper, we propose a feature selection based method for CIS data pre-processing in order to extract the most relevant information for further analysis such as clustering and classifications. By removing irrelevant and redundant features, feature selection is an essential step in data mining process in finding optimal subset of features to improve the quality of result by giving faster time processing, higher accuracy and simpler results with fewer features. Detailed feature selection analysis is presented in the paper. Both time-domain and load shape data are compared based on the accuracy, consistency and statistical dependencies between features.