Cardiff School of Technologies

TMC genes encode a broadly-conserved family of multipass integral membrane proteins in animals that includes eight proteins in humans and mice, one in Drosophila and two in C. elegans. The human TMC1 and TMC2 are deafness genes required for hair cell mechanotransduction; however, the molecular functions of these and other TMC proteins have not been determined. We have found that the C.elegans tmc-1 gene encodes a sodium-activated ion channel that functions specifically in salt taste chemosensation. tmc-1 is expressed in the ASH polymodal avoidance neurons, where it is required specifically for salt-evoked neuronal activity and behavioural avoidance of high concentrations of NaCl. Heterologous expression of C. elegansTMC-1 in mammalian cell culture demonstrates that it forms a cation channel that is activated by high extracellular sodium but not by other cations. We have also studied the physiological role of the C. elegansTMC-2. Expression of TMC-2 in mammalian cells induces mechanically activated currents. Behavioural responses to strong mechanical stimuli were severely reduced in tmc-2 mutants. Calcium imaging experiments in the PVD sensory neurons revealed a strong defect in tmc-2 knockout animals to harsh mechanical stimulation but not cold. Our results suggest that C. elegans TMC-1 and TMC-2 form ion channels gated by different sensory stimuli, suggesting that other TMC proteins most likely constitute channels. Since mammals contain several TMC proteins of unknown function, it is possible that one or more of these may play a role in salt taste or mechanotransduction.

After the successful mission of the Mars Pathfinder, in which the first semi-autonomous vehicle explored the Martian surface, other missions to Mars have been launched or are under development. Research on Mars robots provides an ideal test platform for the study of autonomous robotics, for example the ability of robots to navigate efficiently within unknown environments. We have developed a computer model of a planetary rover and Martian environment using the ODE physics engine. This simulation model is being used to investigate the design of autonomous neural controllers with different sensor configurations using the evolutionary robotics approach.

In a first study, we investigated the use of an infrared sensor system for planetary rover exploration in rough terrains. The control system for the robot consisted of an artificial neural network trained using evolutionary computation techniques. An adaptive threshold for the infrared sensors was evolved together with the neural control system to allow for the adaptation to various unforeseen environmental conditions. After each evolutionary process, the behavioural properties obtained were tested by measuring the generalisation capabilities of the rover when exposed to new environmental conditions, particularly rough terrain. In addition, the dynamics of the co-evolution between the controller and the threshold were analysed. These analyses showed that different pathways were exploited by the evolutionary process in order to adapt the sensing abilities and control system.

Ongoing work is focusing on the integration of infrared sensors with an active vision camera system. Active vision control systems have the ability to extract salient information from an environment in order to solve specific tasks. The objective of this work is to investigate the use of such systems for obstacle avoidance and navigation in complex environments. Experimental results suggest that evolved active vision systems provide a powerful, yet computationally efficient solution to advanced visual processing for navigation. Other experiments are investigating the use of simulated `islands’ to evolve several populations of robot controllers in parallel. This methodology aims to emulate the observed genetic differentiation that occurs in the natural world. The parallel nature of this paradigm enables the utilisation of distributed computational resources, drastically reducing the evolution time required, as confirmed by our preliminary experiments.

Current methods used to address the problem of autonomous navigation in planetary rovers rely on computationally expensive algorithms and elaborate 3D sensing strategies. This paper presents a low complexity alternative based on an evolved neural controller using continuous-value infrared sensors. A unified framework is presented for developing such controllers in virtual planetary rovers, utilising the latest advances in parallel optimisation by way of island model evolution. Preliminary results are presented demonstrating that this approach is capable of producing successful navigation and obstacle avoidance behaviours, showing that equivalent or better results can be achieved relative to traditional genetic algorithms.

Verification of the Java Orekit implementation of the Draper Semi-analytical Satellite Theory (DSST) is discussed. The Orekit library for space flight dynamics has been published under the open-source Apache license V2. The DSST is unique among analytical and semi-analytical satellite theories due to the scope of the included force models. However, the DSST has not been readily accessible to the wider Astrodynamics research community. Implementation of the DSST in the Orekit library is a comprehensive task because it involves the migration of the DSST to the object-oriented Java language and to a different functional decomposition strategy. The resolution of the code and documentation anomalies discovered during the verification process is the important product of this project.

Autonomous navigation and robust obstacle avoidance are prerequisites for the successful operation of a planetary rover. Typical approaches to tackling this problem rely on complex and computationally expensive navigation strategies based upon the creation of 3D maps of the environment. In contrast, this research proposes a simple artificial neural network relying on infrared sensory input as the control structure. This paper presents a unified framework for designing such control structures for a simulated rover, taking advantage of code parallelisation and the latest advances in global optimisation research. In particular, it details a 3D physics-based simulation of a planetary rover and a tool set for performing the optimisation of ANN parameters within the island model. This paper also presents preliminary results showing that the aforementioned framework can parallelise the controller design process without any loss in performance over traditional methods, and will outline research directions, which aim to take full advantage of this technique's potential.

Connectomics – the study of neural connectivity – is primarily concerned with the mapping and characterisation of wired synaptic links; however, it is well established that long-distance chemical signalling via extrasynaptic volume transmission is also critical to brain function. As these interactions are not visible in the physical structure of the nervous system, current approaches to connectomics are unable to capture them.

This work addresses the problem of missing extrasynaptic interactions by demonstrating for the first time that whole-animal volume transmission networks can be mapped from gene expression and ligand-receptor interaction data, and analysed as part of the connectome. Complete networks are presented for the monoamine systems of Caenorhabditis elegans, along with a representative sample of selected neuropeptide systems.

A network analysis of the synaptic (wired) and extrasynaptic (wireless) connectomes is presented which reveals complex topological properties, including extrasynaptic rich-club organisation with interconnected hubs distinct from those in the synaptic and gap junction networks, and highly significant multilink motifs pinpointing locations in the network where aminergic and neuropeptide signalling is likely to modulate synaptic activity. Thus, the neuronal connectome can be modelled as a multiplex network with synaptic, gap junction, and neuromodulatory layers representing inter-neuronal interactions with different dynamics and polarity. This represents a prototype for understanding how extrasynaptic signalling can be integrated into connectomics research, and provides a novel dataset for the development of multilayer network algorithms.

The scientific and technological rationale of this project was to integrate the island model paradigm for parallelisation with the automatic controller design methodology of Evolutionary Robotics. This integration allows the investigation of the conditions and contexts in which the utilisation of the island algorithm can enhance the robustness, generality of autonomous robotics controllers.

Connectomics has focused primarily on the mapping of synaptic links in the brain; yet it is well established that extrasynaptic volume transmission, especially via monoamines and neuropeptides, is also critical to brain function and occurs primarily outside the synaptic connectome. We have mapped the putative monoamine connections, as well as a subset of neuropeptide connections, in C. elegans based on new and published gene expression data. The monoamine and neuropeptide networks exhibit distinct topological properties, with the monoamine network displaying a highly disassortative star-like structure with a rich-club of interconnected broadcasting hubs, and the neuropeptide network showing a more recurrent, highly clustered topology. Despite the low degree of overlap between the extrasynaptic (or wireless) and synaptic (or wired) connectomes, we find highly significant multilink motifs of interaction, pinpointing locations in the network where aminergic and neuropeptide signalling modulate synaptic activity. Thus, the C. elegans connectome can be mapped as a multiplex network with synaptic, gap junction, and neuromodulator layers representing alternative modes of interaction between neurons. This provides a new topological plan for understanding how aminergic and peptidergic modulation of behaviour is achieved by specific motifs and loci of integration between hard-wired synaptic or junctional circuits and extrasynaptic signals wirelessly broadcast from a small number of modulatory neurons.

Despite  knowing  the  connectivity  of  the C.  elegans nervous  system,  its  computational properties  remain  poorly  understood.  This  is  especially  true  for gap  junctions,  which  while forming  890  electrical  synapses  in C.  elegans,  have  been  largely  unexplored  in  terms  of processing.

Previous  research  has  demonstrated  a  role  for  gap  junctions  in  coincidence  detection  [1]; however,  the  mechanisms  underlying  this  function  have  yet  to  be  fully  elucidated.  Our hypothesis  is  that  gap  junctions  facilitate  shunting inhibition by leaking current to inactive neurons in the network, thus requiring concomitant activation. To test this hypothesis, we have expressed    a    light-activated    cation    channel    (channelrhodopsin)    in    two    classes    of electrically-coupled  neurons,  OLQ  and  CEP, which  are  known  to  modulate  FLP-mediated reversals via RIH. Our hypothesis predicts that simultaneously activating these neurons should induce  reversals,  whereas  the  stimulation  of  an individual neuron should not. We are now performing experiments to test this.

Combining the controlled-illumination system of Stirman et al. [2], with Q binary expression [3], and brainbow recombination [4], we are also developing a system for cell-specific targeting of optogenetic  reagents,  with  the  aim  of  surveying  further  circuits  in C.  elegans at  the  level  of individual neurons.

References:
[1] Chatzigeorgiou, M. & Schafer, W.R.  Neuron 70, 299-309 (2011). [2] Stirman, J.N., et al. Nat Meth 8, 153-158 (2011). [3] Wei, X., et al. Nat Meth 9, 391-395 (2012). [4] Hampel, S., et al. Nat Meth 8, 253-259 (2011).

Socio-cultural markers such as attitudes, behaviours, ideas, aspirations and interests were collected and correlated to previously identified social, psychological and quantitative predictors of lifespan.  Attitudes to whether a person wanted to live for as long as possible and if so in what condition were also surveyed.

The aim of the study was to identify trends, correlations and multivariate, interdisciplinary markers of health that contain word and phrase based socio-cultural markers as well as other known longevity markers. This would lead to enhanced accuracy in predictions of current health states and longevity predictions.

Text-data analysis was chosen to find novel longevity markers, as text-data is easily surveyed, available, digitised and sequence-based.  As more than an analogy to The Human Genome Project, The Human Memome Project sets out to record personalised text-sequence data for individuals to be able to correlate particular text-sequences to specific health markers associated with longevity. Data was gathered from over 300 participants, 25 countries, 6 continents, in an 18-70-age range.

We have attempted to use varied statistical analysis and computational modelling techniques to build optimal sets of attitudes, behaviours and interests that correlate the most with longevity and find text-sequences that are correlated the most or least with longevity.

This project presents a proof-of-principle experiment that could be developed into advanced technology platforms, big data analysis, predictive analytics and personal health tools for the healthcare, preventative medicine and global, local or personal, real-time longevity risk analysis.

The Human Memome Project also presents a key engagement with the crowd sourcing, citizen science and quantified self communities for research into longevity.

With an increasing dependence on space systems in all areas of society, the need for accurate and robust Space Situational Awareness (SSA) is paramount. Despite this, software tools have been slow to progress; many systems remain in legacy languages, based on deprecated design principles. The Draper Semianalytic Satellite Theory (DSST) is one such system, though especially suited to the problem of SSA due to its high computational efficiency.

Originally implemented in Fortran 77 in the proprietary R&D Goddard Trajectory Determination System (GTDS), DSST has remained inaccessible to most researchers. Recent work has been undertaken to reimplement DSST in the open source ORbit Extrapolation KIT (OREKIT), a modern and operationally tested astrodynamics library.

Tests to verify OREKIT DSST’s accuracy and stability will be presented, including direct comparisons with the GTDS implementation.

It is hoped that this, and other additions to OREKIT, will provide a modern, verified, and freely available framework for SSA and space flight dynamics, encouraging standardisation and international collaboration on the development of new and existing algorithms.

Current methods used to address the problem of autonomous navigation in planetary rovers rely on computationally expensive algorithms and elaborate 3D sensing strategies. This paper presents a low complexity alternative based on an evolved neural controller using continuous-value infrared sensors. A unified framework is presented for developing such controllers in virtual planetary rovers, utilising the latest advances in parallel optimisation by way of island model evolution. Preliminary results are presented demonstrating that this approach is capable of producing successful navigation and obstacle avoidance behaviours, showing that equivalent or better results can be achieved relative to traditional genetic algorithms.

Socio-cultural markers such as attitudes, behaviours, ideas, aspirations and interests were collected and correlated to previously identified social, psychological and quantitative predictors of lifespan. Attitudes to whether a person wanted to live for as long as possible and if so in what condition were also surveyed. The aim of the study was to identify trends, correlations and multivariate, interdisciplinary markers of health that contain word and phrase based socio-cultural markers as well as other known longevity markers. This would lead to enhanced accuracy in predictions of current health states and longevity predictions. Text-data analysis was chosen to find novel longevity markers, as text-data is easily surveyed, available, digitized and sequence-based. As more than an analogy to The Human Genome Project, The Human Memome Project sets out to record personalised text-sequence data for individuals to be able to correlate particular text-sequences to specific health markers ...