Infantile parkinsonism is a severe progressive disease that is incurable and children affected die in adolescence. It begins in infancy with features similar to adult Parkinsons disease with severe difficulties with movement and unsteadiness. The faulty gene that causes this condition has been identified and its function understood. This makes it a potential condition that could be treated by gene therapy as no other treatments have worked or helped so far. The faulty gene is responsible for producing proteins involved in transporting a chemical in the brain that controls movements. In this condition the protein does not work effectively leading the severe movement difficulties and death in adolescence. I will research ways to deliver a normal form of the gene to a mouse model with the disease. An animal study is required as it is not safe to study these techniques directly in humans. There is a mouse model that simulates the human from of infantile parkinsonism condition well. This mouse does not produce any of form of the protein and shows all the signs with movement difficulties and reduced longevity seen in the human form of the condition. We would aim to give the mouse the normal gene through an injection into the blood stream. We would assess for improvements in movements, weight gain and lifespan to assess whether the gene treatment has helped. We would assess carefully for side effects and ability for the gene treatment to reach the brain in the mouse, the area where we want the treatment to work. We would study the mice to see if the normal function of the gene is restored and would see this with improvements in movements and longevity of the mice. We would assess the mice to ensure the gene treatment is safe and effective and assess the best age to give the treatment and ideal dosage. Further studies of the effects of the gene treatment will be performed by assessing tissue samples from the mice. Successful gene therapy study in mice will help us to proceed to establish future studies in humans. In the future these techniques may also be applicable to other incurable childhood brain disorders.

Robots have been able to serve the original promise to replace human counterparts in laborious, hazardous, and repetitive tasks mainly in the area of position control that includes tasks such as pick and place of components, arc welding, grinding known objects, and even in bipedal walking on fairly smooth and known grounds. However, robots still find it hard to carry out stable force control tasks on uncertain objects or walk on natural soft terrains (grass, sand, mud). Just like the difference between the way we use the left hand and the right hand can not be explained using their biomechanical basis alone, the answer to robotic survival in uncertain environments does not come from an attempt to build robots that resemble human bodies alone. From early 1980s, scientists have begun to believe that the secrets of stable interactions with natural compliant environments will come from an ability of the robot itself to be compliant. The original work of Neville Hogan on impedance control was based on this concept. Since then, a considerable body of literature can be found on how impedance control is applied in various force control applications such as rehabilitation, massaging, bipedal walking, exoskeletal robotics, and several other direct interactions with humans. However, still there is no answer to how impedance control should be adaptively managed to sustain stability when the coupled dynamics between the robot and the environment evolves metastable dynamics. The theory of Metastability states that an uncertain dynamics system can exhibit intermittent instability though it may stay stable most of the time. A human using a screw driver is one example, where the dynamic contact with the screw may stay stable most of the time, but exhibit intermittent slipping due to uncertainty in the friction between the screw and the surrounding medium. Even a human walker can fall down in rare situations due to the same phenomenon. However, an uncertain dynamic system can enhance stability if it can predict where it is likely to fail. A number of recent advances in metastable systems use the concept of mean first passage time (MFPT) as an indicator to assess the current control policy in an uncertain environment. MFPT is the expected time to the next failure situation given the current knowledge of the uncertain dynamics of the coupled dynamics of the robot and the environment.Therefore, this project aims at developing a unifying theory of impedance control for robots that are in dynamic contact with uncertain environments. The generic method that can start to perform stable hybrid position/force control on an uncertain environment with partially known dynamics and recursively build a robust internal model to perform stable position/force control on an environment that changed its stiffness, viscosity, and inertia. Then an algorithm will be developed to use a locally linearised model of the above coupled dynamic system to estimate the MFPT of the robot and the environment. This MFPT will then be used in a novel real-time algorithm to adapt a bank of candidate impedance parameter sets and adaptively choose the best parameter set to suit the environment in order to maximise the MFPT. Rigorous theoretical proofs of stability and experimental validation of methods will be given. The project will use a custom built experimental platform to evaluate and refine the fundamental theories and algorithms that will be developed in this project. The PI will closely collaborate with Shadow Robotics Company, a UK based SME who develops biomimetic robotic hands, and the robotics group led by Professor Darwin Caldwell at the Italian Institute of Technology, where the researchers strive to enable the humanoid robot i-Cub to interact with natural uncertain environments. Therefore, this project will benefit from a wealth of experiences the collaborators have already gathered on real robots interacting with natural environments.

Superresolution encompasses a range of techniques for transcending the resolution limit of a sensor and earned the 2014 Nobel Prize in Chemistry (for superresolved fluorescence microscopy). Superresolution is analogous to biological hyperacuity of vision and touch where the discrimination is finer than the spacing between sensory receptors. Superresolution research in visual imaging has impacted science from cell biology to medical scanning 'in ways unthinkable in the mid-90s' (Editorial, Nature 2009). The success of this proposal will enable the widespread uptake of superresolution techniques in the domain of artificial tactile sensing, potentially impacting multiple application areas across robotics from autonomous quality control in manufacturing to sensorized grippers for autonomous manipulation to sensorized prosthetic hands and medical probes in healthcare. Proposed research More specifically, the development of robust and accurate artificial touch is required for autonomous robotic systems to interact physically with complex environments, underlying the future robotization of broad areas of manufacturing, food production, healthcare and assisted living that presently rely on human labour. Currently, there are many designs for tactile sensors and various methodologies for perception, from which general principles are emerging, such as taking inspiration from human touch (Dahiya et al 2012), using statistical approaches to capture sensor and environment uncertainty and combining tactile sensor control and perception (Prescott et al 2012). All application areas of robot touch are currently limited by the capabilities of tactile sensors. This first grant proposal aims to demonstrate that tactile superresolution can radically improve tactile sensor performance and thus potentially impact all areas of robotics involving physical interaction with complex environments. Visual superresolution has revolutionised the life sciences by enabling the imaging of nanoscale features within cells. Tactile superresolution has the potential to drive a step-change in tactile robotics, with applications from quality control and autonomous manipulators in manufacturing (Yousef et al 2011) to sensorized prosthetics and probes in healthcare. Proposed initial application domain Currently, across the entire automobile industry, gap and flush quality controls are made manually by human operators using their hands to check the alignment between vehicle parts. Experts in the industry have informed me that human hands are used because modern vision-based measuring technologies (such as laser scanners) do not robustly detect sub-millimetre misalignments between parts of differing reflectivity and refractivity. An automated system using robot touch would be more reliable, enable traceability of defects, and move production towards a fully automated paradigm. The proposed research will culminate in a pilot study demonstrating that tactile superresolution will enable readily available tactile sensors to make gap and flush measurements of the requisite sub-millimetre tolerance and how the sensors should be controlled during the tactile perception task. This will constitute a first step towards building a consortium between academic and industrial partners to develop a fully working prototype for test installation on a production line.

The functional intricacy of the central nervous system (CNS) arises from the complex anatomical and dynamic interactions between different types of neurones involved in specific networks. Hence, the encoding of information in neural circuits occurs as a result of interactions between individual neurones as well as through the interplay within both microcircuits (made of few neurones) and large scale networks involving thousands to millions of cells. One of the great challenges of neuroscience nowadays is to understand how these neural networks are formed and how they operate. Such challenge can be resolved only through simultaneous recording from thousands of neurones that become active during specific neuronal tasks. One of the experimental approaches to fulfil this goal is to use multielectrode arrays (MEAs) that consist of several channels (electrodes) that can each record (and/or stimulate) from few adjacent neurones within a particular area of the CNS. MEAs can be used in vitro to record from dissociated neuronal cultures or from brain slices or isolated retinas. These MEAs consist of assemblies of electrodes embedded in planar substrates. Typical commercial MEAs consist of 60-128 electrodes with a spacing of 100-200 um. Considering that a generic neurone in the mammalian CNS has a diameter of about 10 um, it is obvious that such MEAs cannot convey information on the activity of all neurones involved in a specific network, but rather just from a sample of these cells. To overcome this activity under-sampling, in this project, we will use the Active Pixel Sensor (APS) MEA, a novel type of MEA platform developed in a NEST-EU Project by our collaborator Luca Berdondini (Italian Institute of Technology, Genova). This MEA consists of 4,096 electrodes with near cellular resolution (21x21 um, 42 um centre-to-centre separation, covering an active area of 2.5 mm x 2.5 mm), where recording is possible from all channels at the same time. We will use the APS MEA to record spontaneous waves of activity that are present in the neonatal vertebrate retina. These waves occur during a short period of development during perinatal weeks and they are known to play an important role in guiding the precise wiring of neural connections in the visual system, both at the retinal and extra-retinal levels. The APS-MEA, thanks to its unmet size and resolution, will enable us to reach new insights into the precise dynamics of these waves as never achieved before. Recordings from such large scale networks at near cellular resolution generate extremely rich datasets with the drawback that these datasets are very large and difficult to handle, thus necessitating the development of new powerful analytical tools enabling to decode in a fast, efficient and user-friendly way how cellular elements interact in the network. The development of such computational tools is the central goal of this project, while the experimental work on the retina defines a challenging and unique scientific context. The tools we plan to develop will yield parameters that will help us reach better understanding of network function, from the temporal firing patterns of individual neurones to how activity precisely propagates within the network. We will also develop novel tools for easier visualisation of the dynamical behaviour of the activity within the network. These tools will be developed in a language that could be easily utilized by other investigators using the same recording system or other platforms of their choice. Finally, to ensure that these tools are accessible to the wide neurophysiology community, they will be deployed on CARMEN (Code Analysis, Repository and Modelling for e-Neuroscience), a new internet-based neurophysiology sharing resource designed for facilitating worldwide communication between collaborating neurophysiologists.

All the cells composing the body contain the genetic information necessary for the life of each individual. Genetic information is encoded by the deoxyribonucleic acid (DNA), which represents the genomes and exerts its function via making proteins. A crucial intermediate in this process is ribonucleic acid (RNA) which is specifically called messenger RNA (mRNA). Recently, it has been shown that besides mRNA, RNA that does not produce proteins exists and its production and function in the cells are equally important to the ones employed by mRNA. Because the latter class of RNA is not created by the cells to produce proteins, but instead to have a regulative role, it is called noncoding RNA (ncRNA). Similarly to mRNA, the production and function of ncRNA ensure an accurate cellular function and consequently a healthy life. NcRNA role is to precisely regulate mRNAs when they produce proteins. This means that the production of proteins by mRNA can increase or slow down depending of particular circumstances, and this effect is mediated by the interaction between mRNA and ncRNAs. NcRNAs can be divided into different classes, depending on their length. MicroRNAs belong to the short ncRNAs class and several studies performed on microRNAs, in the last decade, have demonstrated that these short RNA molecules are important regulator of cancer development. Moreover, specific drugs can now be developed to specifically target microRNAs in the body to fight cancer or other diseases. MicroRNA function by interacting with small, specific region of mRNA to slow down protein production. In the last few years, other important discoveries, in science, are remarkably impacting our understanding of the way that the cells function as well as about what goes wrong when the cells pathologically change and form cancers. One of these new techniques is called CRISPR-CAS9 and can be used, in vitro and in vivo, to precisely edit the DNA of the cells accordingly with our needs. Because DNA generate RNA the new modification created at the level of DNA, by using CRISPR-CAS9 in the cells, will be consequently transferred to the RNA. Herein, we want to use CRISPR-CAS9 to globally disrupt the mRNA portions predicted to interact with microRNAs. Because disruption of these mRNA regions will impede functional microRNA-mRNA interaction, the mRNA will consequently produce a higher amount of protein that can be deleterious and induce cancer. In principle, mRNA that produce proteins important for cancer, can also be modulated by microRNAs. By performing specific laboratory tests and by studying changes in the growth of tumour cells seeded on specific dishes and treated with CRISPR-CAS9 we can finally identify important microRNA-mRNA interactions that can be exploited for future therapeutic intervention. By using both experimental techniques and computational approaches, this work would permit us to understand the important mRNA-microRNA interactions that regulate cancer development and this new knowledge could then be used to develop new drugs to fight cancer.