Command and Control Systems for Search and Rescue Robots

The novel application of unmanned systems in the domain of humanitarian Search and Rescue (SAR) operations has created a need to develop specific multi-Robot Command and Control (RC2) systems. This societal application of robotics requires human-robot interfaces for controlling a large fleet of heterogeneous robots deployed in multiple domains of operation (ground, aerial and marine). This chapter provides an overview of the Command, Control and Intelligence (C2I) system developed within the scope of Integrated Components for Assisted Rescue and Unmanned Search operations (ICARUS). The life cycle of the system begins with a description of use cases and the deployment scenarios in collaboration with SAR teams as end-users. This is followed by an illustration of the system design and architecture, core technologies used in implementing the C2I, iterative integration phases with field deployments for evaluating and improving the system. The main subcomponents consist of a central Mission Planning and Coordination System (MPCS), field Robot Command and Control (RC2) subsystems with a portable force-feedback exoskeleton interface for robot arm tele-manipulation and field mobile devices. The distribution of these C2I subsystems with their communication links for unmanned SAR operations is described in detail. Field demonstrations of the C2I system with SAR personnel assisted by unmanned systems provide an outlook for implementing such systems into mainstream SAR operations in the future

Recovery and characterization of viral diversity from aquatic short- and long-read metagenomes

Viruses are the most abundant biological entities in marine ecosystems and play an essential role in global biogeochemical cycles. They have important ecological functions as drivers of bacterial populations through lytic infections and contribute to bacterial genetic diversification. Unfortunately, their study is severely limited by the difficulty to culture and isolate them in lab conditions. Culture-independent techniques such as metagenomics can complement culture-based approaches to capture more phage diversity. However, the vast majority of viral sequences recovered through these methods are uncharacterized and therefore do not provide any information about their interactions with the bacterial community, a phenomenon that has been named “viral dark matter”. In this thesis, several bioinformatic techniques are applied to both short- and long-read metagenomic datasets to recover biological information from marine viral sequences contained therein. A pipeline for recovering viral sequences based on a reference genome was developed and applied to the study of myophages infecting the alphaproteobacterial SAR11 clade, one of the most abundant bacterioplankton groups in surface marine and freshwater ecosystems. We were able to recover 22 new genomes which include the first genomes of myophages infecting LD12, the SAR11 freshwater clade. These sequences are underrepresented in datasets derived from the viral fraction, suggesting a bias of either technical or biological nature. Surprisingly, this family of phages code for an operon which resembles the secretion system type VIII operon in Escherichia coli. The function of this phage operon is still unknown. Next, a long-read dataset from the Mediterranean Sea was explored for viral contigs to contrast phage recovery between long- and short-read datasets. The analysis revealed that while long-read assemblies resulted in viral sequences of better quality, there was a sizable amount of intra-clade viral diversity that was not included in the assemblies. This viral diversity only found in long reads is even greater than previously thought. This untapped diversity could aid biotechnological efforts as evidenced by the discovery of new endolysins. Finally, a tool (Random Forest Assignment of Hosts, or RaFAH) for assigning hosts to phage sequences obtained from metagenomic datasets was created. The tool is based on a machine learning tool trained with phage protein clusters generated de novo. Benchmarking shows that RaFAH is on par with other state-of-the-art classifiers and is able to classify phage contigs at the level of Kingdom, which makes it the first classifier to accurately detect Archaea viruses from metagenomic samples. A feature importance analysis reveals that the protein clusters with the most predictive power are those involved in host recognition.Los bacteriófagos (”fagos”) son los organismos más abundantes en los ecosistemas marinos y tienen un papel esencial en los ciclos biogeoquímicos globales. Asimismo, influencian la evolución de las poblaciones bacterianas que infectan y contribuyen a la diversificación del acervo genético bacteriano. Desgraciadamente, su estudio se ve limitado por la dificultad de cultivar y aislar estos organismos en el laboratorio. El uso de técnicas que no requieren cultivo, como la metagenómica, pueden complementar el cultivo en laboratorio para recuperar una mayor diversidad de fagos. Sin embargo, la inmensa mayoría de secuencias virales recuperadas mediante metagenómica no pueden ser caracterizadas, por lo que no proporcionan ninguna información sobre sus interacciones con la comunidad bacteriana, un fenómeno que se ha nombrado “materia oscura viral”. En esta tesis se han utilizado múltiples procesos bioinformáticos en colecciones de metagenomas de lectura corta y larga para caracterizar las secuencias virales que contienen. Se ha desarrollado un procedimiento para recuperar secuencias virales a partir de un genoma de referencia y se ha aplicado al estudio de miofagos que infectan al clado SAR11 de las Alfaproteobacteria, uno de los grupos de bacterioplankton más abundantes en agua dulce y agua salada de superficie. Se consiguió recuperar 22 nuevos genomas que incluyen el primer genoma que infecta LD12, el subclado de SAR11 de agua dulce. Estos genomas están poco representados en colecciones obtenidas de la fracción viral, lo que sugiere que las afecta un sesgo técnico o biológico. Sorprendentemente, esta familia de fagos contiene un operón similar al sistema de secreción tipo VIII de Escherichia coli. La función de este operón es aún desconocida. Asimismo, se contrastó la recuperación de secuencias víricas entre colecciones de lectura corta y larga utilizando colecciones obtenidas en el mar Mediterráneo. Los resultados muestran que aunque los ensamblajes derivados de las lecturas largas producen secuencias virales de mejor calidad, en el proceso se pierde una gran cantidad de diversidad intraclado. Esta diversidad es mucho mayor de la recuperada con lecturas cortas, y podría explotarse para aplicaciones biotecnológicas, como el descubrimiento de nuevas endolisinas. Finalmente, se desarrolló un programa (Random Forest Assignment of Hosts, o RaFAH) para asignar hospedadores a secuencias virales obtenidas de colecciones metagenómicas. El programa se basa en el uso de algoritmos de machine learning entrenados con grupos de proteínas creados de novo. RaFAH muestra un rendimiento similar a otros clasificadores de secuencias y es capaz de clasificar secuencias víricas al nivel taxonómico de Reino, siendo así el primer clasificador capaz de detectar fagos que infectan arqueas con precisión. El análisis de importancia de rasgo revela que los grupos de proteínas con mayor poder predictivo son aquellos involucrados en el reconocimiento del hospedador

Proceedings of the 8th Python in Science conference

International audienceThe SciPy conference provides a unique opportunity to learn and affect what is happening in the realm of scientific computing with Python. Attendees have the opportunity to review the available tools and how they apply to specific problems. By providing a forum for developers to share their Python expertise with the wider commercial, academic, and research communities, this conference fosters collaboration and facilitates the sharing of software components, techniques and a vision for high level language use in scientific computing

A real-time packet scheduling system for a 6LoWPAN industrial application

Nowadays, the industrial Wireless Sensor Networks (WSN) are crucial for the monitoring and control of the modern smart factory floor that is relying on them for critical applications and tasks that were performed by wired systems in the past. For this reason, it is required that the transmission mechanisms of wireless sensor networks are efficient and robust and that they guarantee realtime responses with low data losses. Furthermore, it is required that they utilize common networking standards, such as the Internet Protocol (IP), that provides interoperability with already existing infrastructures and offers widely tested security and transmission control protocols. The theoretical part of this document focuses on the description of the current panorama of the industrial WSN, its applications, design challenges and standardizations. It describes the 6LoWPAN standard and the wireless transmission technology that it uses for its lower layers, the IEEE 802.15.4 protocol. Later, it describes the principles behind the wireless scheduling, a state-of-the-art in the IEEE 802.15.4 scheduled channel access and the features of the most used operating systems for WSN. The practical part presents the real-time packet scheduling system for a 6LoWPAN industrial application proposed by this thesis work that adapts the HSDPA scheduling mechanisms to the IEEE 802.15.4 beacon-enabled mode. The system implemented manages the channel access by allocating Guaranteed Time Slots to sensor nodes according to the priority given by three scheduling algorithms that can be selected according to the traffic condition of the network. The system proposed was programmed using Contiki OS. It is based on the eSONIA 6LoWPAN firmware developed for the European Research Project and it was deployed on the FAST WSN for testing. The results, discussion and conclusions are documented at the final sections of this part