Topology preserving stratification of tissue neoplasticity using Deep Neural Maps and microRNA signatures

Background Accurate cancer classification is essential for correct treatment selection and better prognostication. microRNAs (miRNAs) are small RNA molecules that negatively regulate gene expression, and their dyresgulation is a common disease mechanism in many cancers. Through a clearer understanding of miRNA dysregulation in cancer, improved mechanistic knowledge and better treatments can be sought. Results We present a topology-preserving deep learning framework to study miRNA dysregulation in cancer. Our study comprises miRNA expression profiles from 3685 cancer and non-cancer tissue samples and hierarchical annotations on organ and neoplasticity status. Using unsupervised learning, a two-dimensional topological map is trained to cluster similar tissue samples. Labelled samples are used after training to identify clustering accuracy in terms of tissue-of-origin and neoplasticity status. In addition, an approach using activation gradients is developed to determine the attention of the networks to miRNAs that drive the clustering. Using this deep learning framework, we classify the neoplasticity status of held-out test samples with an accuracy of 91.07%, the tissue-of-origin with 86.36%, and combined neoplasticity status and tissue-of-origin with an accuracy of 84.28%. The topological maps display the ability of miRNAs to recognize tissue types and neoplasticity status. Importantly, when our approach identifies samples that do not cluster well with their respective classes, activation gradients provide further insight in cancer subtypes or grades. Conclusions An unsupervised deep learning approach is developed for cancer classification and interpretation. This work provides an intuitive approach for understanding molecular properties of cancer and has significant potential for cancer classification and treatment selection.

Petri Net Toolbox for Multi-Robot Planning under Uncertainty

Currently, there is a lack of developer-friendly software tools to formally address multi-robot coordination problems and obtain robust, efficient, and predictable strategies. This paper introduces a software toolbox that encapsulates, in one single package, modeling, planning, and execution algorithms. It implements a state-of-the-art approach to representing multi-robot systems: generalized Petri nets with rewards (GSPNRs). GSPNRs enable capturing multiple robots, decision states, action execution states and respective outcomes, action duration uncertainty, and team-level objectives. We introduce a novel algorithm that simplifies the model design process as it generates a GSPNR from a topological map. We also introduce a novel execution algorithm that coordinates the multi-robot system according to a given policy. This is achieved without compromising the model compactness introduced by representing robots as indistinguishable tokens. We characterize the computational performance of the toolbox with a series of stress tests. These tests reveal a lightweight implementation that requires low CPU and memory usage. We showcase the toolbox functionalities by solving a multi-robot inspection application, where we extend GSPNRs to enable the representation of heterogeneous systems and system resources such as battery levels and counters.

A CNN-Based System for Mobile Robot Navigation in Indoor Environments via Visual Localization with a Small Dataset

Deep learning has made great advances in the field of image processing, which allows automotive devices to be more widely used in humans’ daily lives than ever before. Nowadays, the mobile robot navigation system is among the hottest topics that researchers are trying to develop by adopting deep learning methods. In this paper, we present a system that allows the mobile robot to localize and navigate autonomously in the accessible areas of an indoor environment. The proposed system exploits the Convolutional Neural Network (CNN) model’s advantage to extract data feature maps for image classification and visual localization, which attempts to precisely determine the location region of the mobile robot focusing on the topological maps of the real environment. The system attempts to precisely determine the location region of the mobile robot by integrating the CNN model and topological map of the robot workspace. A dataset with small numbers of images is acquired from the MYNT EYE camera. Furthermore, we introduce a new loss function to tackle the bounded generalization capability of the CNN model in small datasets. The proposed loss function not only considers the probability of the input data when it is allocated to its true class but also considers the probability of allocating the input data to other classes rather than its actual class. We investigate the capability of the proposed system by evaluating the empirical studies based on provided datasets. The results illustrate that the proposed system outperforms other state-of-the-art techniques in terms of accuracy and generalization capability.

Topological Navigation and Localization in Tunnels—Application to Autonomous Load-Haul-Dump Vehicles Operating in Underground Mines

Mobile robots are no longer used exclusively in research laboratories and indoor controlled environments, but are now also used in dynamic industrial environments, including outdoor sites. Mining is one industry where robots and autonomous vehicles are increasingly used to increase the safety of the workers, as well as to augment the productivity, efficiency, and predictability of the processes. Since autonomous vehicles navigate inside tunnels in underground mines, this kind of navigation has different precision requirements than navigating in an open environment. When driving inside tunnels, it is not relevant to have accurate self-localization, but it is necessary for autonomous vehicles to be able to move safely through the tunnel and to make appropriate decisions at its intersections and access points in the tunnel. To address these needs, a topological navigation system for mining vehicles operating in tunnels is proposed and validated in this paper. This system was specially designed to be used by Load-Haul-Dump (LHD) vehicles, also known as scoop trams, operating in underground mines. In addition, a localization system, specifically designed to be used with the topological navigation system and its associated topological map, is also proposed. The proposed topological navigation and localization systems were validated using a commercial LHD during several months at a copper sub-level stoping mine located in the Coquimbo Region in the northern part of Chile. An important aspect to be addressed when working with heavy-duty machinery, such as LHDs, is the way in which automation systems are developed and tested. For this reason, the development and testing methodology, which includes the use of simulators, scale-models of LHDs, validation, and testing using a commercial LHD in test-fields, and its final validation in a mine, are described.