Insect inspired view based navigation exploiting temporal information

Visual navigation is a key capability for robots. There is a family of insect-inspired algorithms that use panoramic images encountered during a training route to derive directional information from regions around the training route and thus subsequently visually navigate. As these algorithms do not incorporate information about the temporal order of training images, we describe one way this could be done to highlight this information’s utility. We benchmark our algorithms in a simulation of a real world environment and show that incorporating temporal information improves performance and reduces algorithmic complexity

Using deep autoencoders to investigate image matching in visual navigation

This paper discusses the use of deep autoencoder networks to find a compressed representation of an image, which can be used for visual naviga-tion. Images reconstructed from the compressed representation are tested to see if they retain enough information to be used as a visual compass (in which an image is matched with another to recall a bearing/movement direction) as this ability is at the heart of a visual route navigation algorithm. We show that both reconstructed images and compressed representations from different layers of the autoencoder can be used in this way, suggesting that a compact image code is sufficient for visual navigation and that deep networks hold promise for find-ing optimal visual encodings for this task

Using an insect mushroom body circuit to encode route memory in complex natural environments

Ants, like many other animals, use visual memory to follow extended routes through complex environments, but it is unknown how their small brains implement this capability. The mushroom body neuropils have been identified as a crucial memory circuit in the insect brain, but their function has mostly been explored for simple olfactory association tasks. We show that a spiking neural model of this circuit originally developed to describe fruitfly (Drosophila melanogaster) olfactory association, can also account for the ability of desert ants (Cataglyphis velox) to rapidly learn visual routes through complex natural environments. We further demonstrate that abstracting the key computational principles of this circuit, which include one-shot learning of sparse codes, enables the theoretical storage capacity of the ant mushroom body to be estimated at hundreds of independent images

How variation in head pitch could affect image matching algorithms for ant navigation

Desert ants are a model system for animal navigation, using visual memory to follow long routes across both sparse and cluttered environments. Most accounts of this behaviour assume retinotopic image matching, e.g. recovering heading direction by finding a minimum in the image difference function as the viewpoint rotates. But most models neglect the potential image distortion that could result from unstable head motion. We report that for ants running across a short section of natural substrate, the head pitch varies substantially: by over 20 degrees with no load; and 60 degrees when carrying a large food item. There is no evidence of head stabilisation. Using a realistic simulation of the ant’s visual world, we demonstrate that this range of head pitch significantly degrades image matching. The effect of pitch variation can be ameliorated by a memory bank of densely sampled along a route so that an image sufficiently similar in pitch and location is available for comparison. However, with large pitch disturbance, inappropriate memories sampled at distant locations are often recalled and navigation along a route can be adversely affected. Ignoring images obtained at extreme pitches, or averaging images over several pitches, does not significantly improve performance

A Hybrid Visual-Model Based Robot Control Strategy for Micro Ground Robots

This paper proposed a hybrid vision-based robot control strategy for micro ground robots by mediating two vision models from mixed categories: a bio-inspired collision avoidance model and a segmentation based target following model. The implemented model coordination strategy is described as a probabilistic model using finite state machine (FSM) that allows the robot to switch behaviours adapting to the acquired visual information. Experiments demonstrated the stability and convergence of the embedded hybrid system by real robots, including the studying of collective behaviour by a swarm of such robots with environment mediation. This research enables micro robots to run visual models with more complexity. Moreover, it showed the possibility to realize aggregation behaviour on micro robots by utilizing vision as the only sensing modality from non-omnidirectional cameras

Molecular profiles and urinary biomarkers of upper tract urothelial carcinomas associated with aristolochic acid exposure

Recurrent upper tract urothelial carcinomas (UTUCs) arise in the context of nephropathy linked to exposure to the herbal carcinogen aristolochic acid (AA). Here we delineated the molecular programs underlying UTUC tumorigenesis in patients from endemic aristolochic acid nephropathy (AAN) regions in Southern Europe. We applied an integrative multiomics analysis of UTUCs, corresponding unaffected tissues and of patient urines. Quantitative microRNA (miRNA) and messenger ribonucleic acid (mRNA) expression profiling, immunohistochemical analysis by tissue microarrays and exome and transcriptome sequencing were performed in UTUC and nontumor tissues. Urinary miRNAs of cases undergoing surgery were profiled before and after tumor resection. Ribonucleic acid (RNA) and protein levels were analyzed using appropriate statistical tests and trend assessment. Dedicated bioinformatic tools were used for analysis of pathways, mutational signatures and result visualization. The results delineate UTUC-specific miRNA:mRNA networks comprising 89 miRNAs associated with 1,862 target mRNAs, involving deregulation of cell cycle, deoxyribonucleic acid (DNA) damage response, DNA repair, bladder cancer, oncogenes, tumor suppressors, chromatin structure regulators and developmental signaling pathways. Key UTUC-specific transcripts were confirmed at the protein level. Exome and transcriptome sequencing of UTUCs revealed AA-specific mutational signature SBS22, with 68% to 76% AA-specific, deleterious mutations propagated at the transcript level, a possible basis for neoantigen formation and immunotherapy targeting. We next identified a signature of UTUC-specific miRNAs consistently more abundant in the patients' urine prior to tumor resection, thereby defining biomarkers of tumor presence. The complex gene regulation programs of AAN-associated UTUC tumors involve regulatory miRNAs prospectively applicable to noninvasive urine-based screening of AAN patients for cancer presence and recurrence

Snapshot navigation in the wavelet domain

Many animals rely on robust visual navigation which can be explained by snapshot models, where an agent is assumed to store egocentric panoramic images and subsequently use them to recover a heading by comparing current views to the stored snapshots. Long-range route navigation can also be explained by such models, by storing multiple snapshots along a training route and comparing the current image to these. For such models, memory capacity and comparison time increase dramatically with route length, rendering them unfeasible for small-brained insects and low-power robots where computation and storage are limited. One way to reduce the requirements is to use a compressed image representation. Inspired by the filter bank-like arrangement of the visual system, we here investigate how a frequency-based image representation influences the performance of a typical snapshot model. By decomposing views into wavelet coefficients at different levels and orientations, we achieve a compressed visual representation that remains robust when used for navigation. Our results indicate that route following based on wavelet coefficients is not only possible but gives increased performance over a range of other models

Using the robot operating system for biomimetic research

Biomimetics seeks to reveal the methods by which natural systems solve complex tasks and abstract principles for development of novel technological solutions. If these outcomes are to either explain behaviour, or be applied in commercial settings, they must be verified on robot platforms in natural environments. Yet development and testing of hypothesis in real robots remains sufficiently challenging for many in this highly cross-disciplinary research field that it is often omitted from biomimetic studies. Here we ..

Mutational Signatures in Cancer (MuSiCa): a web application to implement mutational signatures analysis in cancer samples

Background: Mutational signatures have been proved as a valuable pattern in somatic genomics, mainly regarding cancer, with a potential application as a biomarker in clinical practice. Up to now, several bioinformatic packages to address this topic have been developed in different languages/platforms. MutationalPatterns has arisen as the most efficient tool for the comparison with the signatures currently reported in the Catalogue of Somatic Mutations in Cancer (COSMIC) database. However, the analysis of mutational signatures is nowadays restricted to a small community of bioinformatic experts. Results: In this work we present Mutational Signatures in Cancer (MuSiCa), a new web tool based on MutationalPatterns and built using the Shiny framework in R language. By means of a simple interface suited to non-specialized researchers, it provides a comprehensive analysis of the somatic mutational status of the supplied cancer samples. It permits characterizing the profile and burden of mutations, as well as quantifying COSMIC-reported mutational signatures. It also allows classifying samples according to the above signature contributions. Conclusions: MuSiCa is a helpful web application to characterize mutational signatures in cancer samples. It is accessible online at http://bioinfo.ciberehd.org/GPtoCRC/en/tools.html and source code is freely available at https://github.com/marcos-diazg/music