Compound heterozygous RMND1 gene variants associated with chronic kidney disease, dilated cardiomyopathy and neurological involvement: a case report

Background Nuclear gene mutations are being increasingly recognised as causes of mitochondrial disease. The nuclear gene RMND1 has recently been implicated in mitochondrial disease, but the spectrum of pathogenic variants and associated phenotype for this gene, has not been fully elucidated. Case presentation An 11-month-old boy presented with renal impairment associated with a truncal ataxia, bilateral sensorineural hearing loss, hypotonia, delayed visual maturation and global developmental delay. Over a 9-year period, he progressed to chronic kidney disease stage V and developed a dilated cardiomyopathy. Abnormalities in renal and muscle biopsy as well as cytochrome c oxidase activity prompted genetic testing. After exclusion of mitochondrial DNA defects, nuclear genetic studies identified compound heterozygous RMND1 (c.713A>G, p. Asn238Ser and c.565C>T, p.Gln189*) variants. Conclusion We report RMND1 gene variants associated with end stage renal failure, dilated cardiomyopathy, deafness and neurological involvement due to mitochondrial disease. This case expands current knowledge of mitochondrial disease secondary to mutation of the RMND1 gene by further delineating renal manifestations including histopathology. To our knowledge dilated cardiomyopathy has not been reported with renal failure in mitochondrial disease due to mutations of RMND1. The presence of this complication was important in this case as it precluded renal transplantation

A bio-inspired goal-directed visual navigation model for aerial mobile robots

Reliably navigating to a distant goal remains a major challenge in robotics. In contrast, animals such as rats and pigeons can perform goal-directed navigation with great reliability. Evidence from neural science and ethology suggests that various species represent the spatial space as a topological template, with which they can actively evaluate future navigation uncertainty and plan reliable/safe paths to distant goals. While topological navigation models have been deployed in mobile robots, relatively little inspiration has drawn upon biology in terms of topological mapping and active path planning. In this paper, we propose a novel bio-inspired topological navigation model, which consists of topological map construction, active path planning and path execution, for aerial mobile robots with visual landmark recognition and compass orientation capability. To mimic the topological spatial representation, the model firstly builds the topological nodes based on the reliability of visual landmarks, and constructs the edges based on the compass accuracy. Then a reward diffusion algorithm akin to animals’ path evaluation process is developed. The diffusion process takes the topological structure and landmark reliability into consideration, which helps the agent to construct the path with visually reliable nodes. In the path execution process, the agent combines orientation guidance and landmark recognition to estimate its position. To evaluate the performance of the proposed navigation model, a systematic series of experiments were conducted in a range of challenging and varied real-world visual environments. The results show that the proposed model generates animal-like navigation behaviours, which avoids travelling across large visually aliased areas, such as forest and water regions, and achieves higher localization accuracy than navigating on the shortest paths.</p

Learning to fuse multiscale features for visual place recognition

Efficient and robust visual place recognition is of great importance to autonomous mobile robots. Recent work has shown that features learned from convolutional neural networks achieve impressed performance with efficient feature size, where most of them are pooled or aggregated from a convolutional feature map. However, convolutional filters only capture the appearance of their perceptive fields, which lack the considerations on how to combine the multiscale appearance for place recognition. In this paper, we propose a novel method to build a multiscale feature pyramid and present two approaches to use the pyramid to augment the place recognition capability. The first approach fuses the pyramid to obtain a new feature map, which has an awareness of both the local and semi-global appearance, and the second approach learns an attention model from the feature pyramid to weight the spatial grids on the original feature map. Both approaches combine the multiscale features in the pyramid to suppress the confusing local features while tackling the problem in two different ways. Extensive experiments have been conducted on benchmark datasets with varying degrees of appearance and viewpoint variations. The results show that the proposed approaches achieve superior performance over the networks without the multiscale feature fusion and the multiscale attention components. Analyses on the performance of using different feature pyramids are also provided.</p

Bio-inspired Multi-scale Visual Place Recognition for the Aerial Vehicle Navigation

Inspired by the discoveries in neuroscience, the method of visual place recognition develops toward using multiple homogenous spatial scales. We present a novel multi-scale place recognition algorithm mimicking the rodent map with multi-scale, discrete and overlapped characteristics. This visual system that can perform place recognition in the aerial environment without any constraint. We present a parallel and multi-channel processing network that can recognize places with a spatial scale and combine the output from these parallel processing channels. This recognizing network can utilize a multi-scale matching that builds associations between robotic activity and places at different spatial scales. Using two aerial datasets, the results demonstrate universal improvements achieved with multi-scale recognition approach. A systematic series of flight simulation experiments are conducted for analyzing the effect on the recognition and localization performance of varying matching scales. Finally, we present insights of further work in robotic navigation.</p