Protein tyrosine phosphatases in hypothalamic insulin and leptin signaling

The hypothalamus is critical to the coordination of energy balance and glucose homeostasis. It responds to peripheral factors, such as insulin and leptin, that convey to the brain the degree of adiposity and the metabolic status of the organism. The development of leptin and insulin resistance in hypothalamic neurons appears to have a key role in the exacerbation of diet-induced obesity. In rodents, this has been attributed partly to the increased expression of the tyrosine phosphatases Protein Tyrosine Phosphatase 1B (PTP1B) and T cell protein tyrosine phosphatase (TCPTP), which attenuate leptin and insulin signaling. Deficiencies in PTP1B and TCPTP in the brain, or specific neurons, promote insulin and leptin signaling and prevent diet-induced obesity, type 2 diabetes mellitus (T2DM), and fatty liver disease. Although targeting phosphatases and hypothalamic circuits remains challenging, recent advances indicate that such hurdles might be overcome. Here, we focus on the roles of PTP1B and TCPTP in insulin and leptin signaling and explore their potential as therapeutic targets

Edge and plane classification with a biomimetic iCub fingertip sensor

The exploration and interaction of humanoid robots with the environment through tactile sensing is an important task for achieving truly autonomous agents. Recently much research has been focused on the development of new technologies for tactile sensors and new methods for tactile exploration. Edge detection is one of the tasks required in robots and humanoids to explore and recognise objects. In this work we propose a method for edge and plane classification with a biomimetic iCub fingertip using a probabilistic approach. The iCub fingertip mounted on an xy-table robot is able to tap and collect the data from the surface and edge of a plastic wall. Using a maximum likelihood classifier the xy-table knows when the iCub fingertip has reached the edge of the object. The study presented here is also biologically inspired by the tactile exploration performed in animals

Sparse Bayesian Nonlinear System Identification using Variational Inference

IEEE Bayesian nonlinear system identification for one of the major classes of dynamic model, the nonlinear autoregressive with exogenous input (NARX) model, has not been widely studied to date. Markov chain Monte Carlo (MCMC) methods have been developed, which tend to be accurate but can also be slow to converge. In this contribution, we present a novel, computationally efficient solution to sparse Bayesian identification of the NARX model using variational inference, which is orders of magnitude faster than MCMC methods. A sparsity-inducing hyper-prior is used to solve the structure detection problem. Key results include: 1. successful demonstration of the method on low signal-to-noise ratio signals (down to 2dB); 2. successful benchmarking in terms of speed and accuracy against a number of other algorithms: Bayesian LASSO, reversible jump MCMC, forward regression orthogonalisation, LASSO and simulation error minimisation with pruning; 3. accurate identification of a real world system, an electroactive polymer; and 4. demonstration for the first time of numerically propagating the estimated nonlinear time-domain model parameter uncertainty into the frequency-domain

Supervisory control theory applied to swarm robotics

Currently, the control software of swarm robotics systems is created by ad hoc development. This makes it hard to deploy these systems in real-world scenarios. In particular, it is difficult to maintain, analyse, or verify the systems. Formal methods can contribute to overcome these problems. However, they usually do not guarantee that the implementation matches the specification, because the system?s control code is typically generated manually. Also, there is cultural resistance to apply formal methods; they may be perceived as an additional step that does not add value to the final product. To address these problems, we propose supervisory control theory for the domain of swarm robotics. The advantages of supervisory control theory, and its associated tools, are a reduction in the amount of ad hoc development, the automatic generation of control code from modelled specifications, proofs of properties over generated control code, and the reusability of formally designed controllers between different robotic platforms. These advantages are demonstrated in four case studies using the e-puck and Kilobot robot platforms. Experiments with up to 600 physical robots are reported, which show that supervisory control theory can be used to formally develop state-of-the-art solutions to a range of problems in swarm robotics

Robot Mapping and Localisation for Feature Sparse Water Pipes Using Voids as Landmarks

Robotic systems for water pipe inspection do not generally include navigation components for mapping the pipe network and locating damage. Such navigation systems would be highly advantageous for water companies because it would allow them to more effectively target maintenance and reduce costs. In water pipes, a major challenge for robot navigation is feature sparsity. In order to address this problem, a novel approach for robot navigation in water pipes is developed here, which uses a new type of landmark feature - voids outside the pipe wall, sensed by ultrasonic scanning. The method was successfully demonstrated in a laboratory environment and showed for the first time the potential of using voids for robot navigation in water pipes

Crowdsourcing the identification of organisms: a case-study of iSpot

Accurate species identification is fundamental to biodiversity science, but the natural history skills required for this are neglected in formal education at all levels. In this paper we describe how the web application ispotnature.org and its sister site ispot.org.za (collectively, “iSpot”) are helping to solve this problem by combining learning technology with crowdsourcing to connect beginners with experts. Over 94% of observations submitted to iSpot receive a determination. External checking of a sample of 3,287 iSpot records verified > 92% of them. To mid 2014, iSpot crowdsourced the identification of 30,000 taxa (>80% at species level) in > 390,000 observations with a global community numbering > 42,000 registered participants. More than half the observations on ispotnature.org were named within an hour of submission. iSpot uses a unique, 9-dimensional reputation system to motivate and reward participants and to verify determinations. Taxon-specific reputation points are earned when a participant proposes an identification that achieves agreement from other participants, weighted by the agreers’ own reputation scores for the taxon. This system is able to discriminate effectively between competing determinations when two or more are proposed for the same observation. In 57% of such cases the reputation system improved the accuracy of the determination, while in the remainder it either improved precision (e.g. by adding a species name to a genus) or revealed false precision, for example where a determination to species level was not supported by the available evidence. We propose that the success of iSpot arises from the structure of its social network that efficiently connects beginners and experts, overcoming the social as well as geographic barriers that normally separate the two

PipeSLAM: Simultaneous Localisation and Mapping in Feature Sparse Water Pipes using the Rao-Blackwellised Particle Filter

Water, a valuable resource, is usually distributed through urban environments by buried pipes. These pipes are difficult to access for inspection, maintenance and repair. This makes in-pipe robots an appealing technology for inspecting water pipes and localising damage prior to repair from above ground. Accurate localisation of damage is of critical importance because of the costs associated with excavating roads, disrupting traffic and disrupting the water supply. The problem is that pipes tend to be relatively featureless making robot localisation a challenging problem. In this paper we propose a novel simultaneous localisation and mapping (SLAM) algorithm for metal water pipes. The approach we take is to excite pipe vibration with a hydrophone (sound induced vibration), which leads to a map of pipe vibration amplitude over space. We then develop a SLAM algorithm that makes use of this new type of map, where the estimation method is based on the Rao-Blackwellised particle filter (RBPF), termed PipeSLAM. The approach is also suited to SLAM in plastic water pipes using a similar type of map derived from ultrasonic sensing. We successfully demonstrate the feasibility of the approach using a combination of experimental and simulation data

Edge and plane classification with a biomimetic iCub fingertip sensor

The exploration and interaction of humanoid robots with the environment through tactile sensing is an important task for achieving truly autonomous agents. Recently much research has been focused on the development of new technologies for tactile sensors and new methods for tactile exploration. Edge detection is one of the tasks required in robots and humanoids to explore and recognise objects. In this work we propose a method for edge and plane classification with a biomimetic iCub fingertip using a probabilistic approach. The iCub fingertip mounted on an xy-table robot is able to tap and collect the data from the surface and edge of a plastic wall. Using a maximum likelihood classifier the xy-table knows when the iCub fingertip has reached the edge of the object. The study presented here is also biologically inspired by the tactile exploration performed in animals

Serum galectins as potential biomarkers of inflammatory bowel diseases

The inflammatory bowel diseases (IBD), which include mainly Crohn's disease (CD) and ulcerative colitis (UC), are common chronic inflammatory conditions of the digestive system. The diagnosis of IBD relies on the use of a combination of factors including symptoms, endoscopy and levels of serum proteins such as C-reactive protein (CRP) or faecal calprotectin. Currently there is no single reliable biomarker to determine IBD. Galectins are a family of galactoside-binding proteins that are commonly altered in the circulation of disease conditions such as cancer and inflammation. This study investigated serum galectin levels as possible biomarkers in determining IBD and IBD disease activity. Levels of galectins-1, -2, -3, -4, -7 and -8 were analysed in 208 samples from ambulant IBD patients (97 CD, 71 UC) patients and 40 from healthy people. Disease activity was assessed using Harvey-Bradshaw Index for CD and simple clinical colitis activity index for UC. The relationship of each galectin in determining IBD and IBD disease activity were analysed and compared with current IBD biomarker CRP. It was found that serum level of galectin-1 and -3, but not galectins-2, -4, -7 and -8, were significantly higher in IBD patients than in healthy people. At cut-off of 4.1ng/ml, galectin-1 differentiated IBD from healthy controls with 71% sensitivity and 87% specificity. At cut-off of 38.5ng/ml, galectin-3 separated IBD from healthy controls with 53% sensitivity and 87% specificity. None of the galectins however were able to distinguish active disease from remission in UC or CD. Thus, levels of galectins-1 and -3 are significantly elevated in both UC and CD patients compared to healthy people. Although the increased galectin levels are not able to separate active and inactive UC and CD, they may have the potential to be developed as useful biomarkers for IBD diagnosis either alone or in combination with other biomarkers