Hybrid bio-robotics: from the nanoscale to the macroscale

[eng] Hybrid bio-robotics is a discipline that aims at integrating biological entities with synthetic materials to incorporate features from biological systems that have been optimized through millions of years of evolution and are difficult to replicate in current robotic systems. We can find examples of this integration at the nanoscale, in the field of catalytic nano- and micromotors, which are particles able to self-propel due to catalytic reactions happening in their surface. By using enzymes, these nanomotors can achieve motion in a biocompatible manner, finding their main applications in active drug delivery. At the microscale, we can find single-cell bio-swimmers that use the motion capabilities of organisms like bacteria or spermatozoa to transport microparticles or microtubes for targeted therapeutics or bio-film removal. At the macroscale, cardiac or skeletal muscle tissue are used to power small robotic devices that can perform simple actions like crawling, swimming, or gripping, due to the contractions of the muscle cells. This dissertation covers several aspects of these kinds of devices from the nanoscale to the macro-scale, focusing on enzymatically propelled nano- and micromotors and skeletal muscle tissue bio-actuators and bio-robots. On the field of enzymatic nanomotors, there is a need for a better description of their dynamics that, consequently, might help understand their motion mechanisms. Here, we focus on several examples of nano- and micromotors that show complex dynamics and we propose different strategies to analyze their motion. We develop a theoretical framework for the particular case of enzymatic motors with exponentially decreasing speed, which break the assumptions of constant speed of current methods of analysis and need different strategies to characterize their motion. Finally, we consider the case of enzymatic nanomotors moving in complex biological matrices, such as hyaluronic acid, and we study their interactions and the effects of the catalytic reaction using dynamic light scattering, showing that nanomotors with negative surface charge and urease-powered motion present enhanced parameters of diffusion in hyaluronic acid. Moving towards muscle-based robotics, we investigate the application of 3D bioprinting for the bioengineering of skeletal muscle tissue. We demonstrate that this technique can yield well-aligned and functional muscle fibers that can be stimulated with electric pulses. Moreover, we develop and apply a novel co-axial approach to obtain thin and individual muscle fibers that resemble the bundle-like organization of native skeletal muscle tissue. We further exploit the versatility of this technique to print several types of materials in the same process and we fabricate bio-actuators based on skeletal muscle tissue with two soft posts. Due to the deflection of these cantilevers when the tissue contracts upon stimulation, we can measure the generated forces, therefore obtaining a force measurement platform that could be useful for muscle development studies or drug testing. With these applications in mind, we study the adaptability of muscle tissue after applying various exercise protocols based on different stimulation frequencies and different post stiffness, finding an increase of the force generation, especially at medium frequencies, that resembles the response of native tissue. Moreover, we adapt the force measurement platform to be used with human-derived myoblasts and we bioengineer two models of young and aged muscle tissue that could be used for drug testing purposes. As a proof of concept, we analyze the effects of a cosmetic peptide ingredient under development, focusing on the kinematics of high stimulation contractions. Finally, we present the fabrication of a muscle-based bio-robot able to swim by inertial strokes in a liquid interface and a nanocomposite-laden bio-robot that can crawl on a surface. The first bio-robot is thoroughly characterized through mechanical simulations, allowing us to optimize the skeleton, based on a serpentine or spring-like structure. Moreover, we compare the motion of symmetric and asymmetric designs, demonstrating that, although symmetric bio-robots can achieve some motion due to spontaneous symmetry breaking during its self-assembly, asymmetric bio-robots are faster and more consistent in their directionality. The nanocomposite-laden crawling bio-robot consisted of embedded piezoelectric boron nitride nanotubes that improved the differentiation of the muscle tissue due to a feedback loop of piezoelectric effect activated by the same spontaneous contractions of the tissue. We find that bio-robots with those nanocomposites achieve faster motion and stronger force outputs, demonstrating the beneficial effects in their differentiation. This research presented in this thesis contributes to the development of the field of bio-hybrid robotic devices. On enzymatically propelled nano- and micromotors, the novel theoretical framework and the results regarding the interaction of nanomotors with complex media might offer useful fundamental knowledge for future biomedical applications of these systems. The bioengineering approaches developed to fabricate murine- or human-based bio-actuators might find applications in drug screening or to model heterogeneous muscle diseases in biomedicine using the patient’s own cells. Finally, the fabrication of bio-hybrid swimmers and nanocomposite crawlers will help understand and improve the swimming motion of these devices, as well as pave the way towards the use of nanocomposite to enhance the performance of future actuators.[spa] La bio-robótica híbrida es una disciplina cuyo objetivo es la integración de entidades biológicas con materiales sintéticos para superar los desafíos existentes en el campo de la robótica blanda, incorporando características de los sistemas biológicos que han sido optimizadas durante millones de años de evolución natural y no son fáciles de reproducir artificialmente. Esta tesis cubre varios aspectos de este tipo de dispositivos desde la nanoescala a la macroescala, enfocándose en nano- y micromotores propulsados enzimáticamente y bio-actuadores y bio-robots basados en tejido muscular esquelético. En el campo de nanomotores enzimáticos, existe la necesidad de encontrar mejores modelos que puedan describir la dinámica de su movimiento para llegar a entender sus mecanismos de propulsión subyacentes. Aquí, nos enfocamos en diversos ejemplos de nano- y micromotores que muestran dinámicas de movimiento complejas y proponemos diferentes estrategias que se pueden utilizar para analizar y caracterizar este movimiento. Moviéndonos hacia robots basados en células musculares, investigamos la aplicación de la técnica de bioimpresión en 3D para la biofabricación de músculo esquelético. Demostramos que esta técnica puede producir fibras musculares funcionales y bien alineadas que puede ser estimuladas y contraerse con pulsos eléctricos. Investigamos la versatilidad de esta técnica para imprimir varios tipos de materiales en el mismo proceso y fabricamos bio-actuadores basados en músculo esquelético. Debido a los movimientos de unos postes gracias a las contracciones musculares, podemos obtener medidas de la fuerza ejercida, obteniendo una plataforma de medición de fuerzas que podría ser de utilidad para estudios sobre el desarrollo del músculo o para testeo de fármacos. Finalmente, presentamos la fabricación de un bio-robot basado en músculo esquelético capaz de nadar en la superficie de un líquido y un bio-robot con nanocompuestos incrustados que puede arrastrarse por una superficie sólida. El primer de ellos es minuciosamente caracterizado a través de simulaciones mecánicas, permitiéndonos optimizar su esqueleto, basado en una estructura tipo serpentina o muelle. El segundo bio-robot contiene nanotubos piezoeléctricos incrustados en su tejido, los cuales ayudan en la diferenciación del músculo debido a una retroalimentación basada en su efecto piezoeléctrico y activada por las contracciones espontáneas del tejido. Mostramos que estos bio-robots pueden generar un movimiento más rápido y una mayor generación de fuerza, demostrando los efectos beneficiales en la diferenciación del tejido

Assessing spatiotemporal correlations from data for short-term traffic prediction using multi-task learning

Traffic flow prediction is a fundamental problem for efficient transportation control and management. However, most current data-driven traffic prediction work found in the literature have focused on predicting traffic from an individual task perspective, and have not fully leveraged the implicit knowledge present in a road-network through space and time correlations. Such correlations are now far easier to isolate due to the recent profusion of traffic data sources and more specifically their wide geographic spread. In this paper, we take a multi-task learning (MTL) approach whose fundamental aim is to improve the generalization performance by leveraging the domain-specific information contained in related tasks that are jointly learned. In addition, another common factor found in the literature is that a historical dataset is used for the calibration and the assessment of the proposed approach, without dealing in any explicit or implicit way with the frequent challenges found in real-time prediction. In contrast, we adopt a different approach which faces this problem from a point of view of streams of data, and thus the learning procedure is undertaken online, giving greater importance to the most recent data, making data-driven decisions online, and undoing decisions which are no longer optimal. In the experiments presented we achieve a more compact and consistent knowledge in the form of rules automatically extracted from data, while maintaining or even improving, in some cases, the performance over single-task learning (STL).Peer ReviewedPostprint (published version

Improving adaptation and interpretability of a short-term traffic forecasting system

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyse the situation in a commercial real-time prediction system with its current problems and limitations. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. We test this predictive system in different real-work scenarios, and evaluate its performance integrating a multi-task learning paradigm for the sake of the traffic prediction task.Peer ReviewedPostprint (published version

Biohybrid robotics: From the nanoscale to the macroscale

Biohybrid robotics is a field in which biological entities are combined with artificial materials in order to obtain improved performance or features that are difficult to mimic with hand-made materials. Three main level of integration can be envisioned depending on the complexity of the biological entity, ranging from the nanoscale to the macroscale. At the nanoscale, enzymes that catalyze biocompatible reactions can be used as power sources for self-propelled nanoparticles of different geometries and compositions, obtaining rather interesting active matter systems that acquire importance in the biomedical field as drug delivery systems. At the microscale, single enzymes are substituted by complete cells, such as bacteria or spermatozoa, whose self-propelling capabilities can be used to transport cargo and can also be used as drug delivery systems, for in vitro fertilization practices or for biofilm removal. Finally, at the macroscale, the combinations of millions of cells forming tissues can be used to power biorobotic devices or bioactuators by using muscle cells. Both cardiac and skeletal muscle tissue have been part of remarkable examples of untethered biorobots that can crawl or swim due to the contractions of the tissue and current developments aim at the integration of several types of tissue to obtain more realistic biomimetic devices, which could lead to the next generation of hybrid robotics. Tethered bioactuators, however, result in excellent candidates for tissue models for drug screening purposes or the study of muscle myopathies due to their three-dimensional architecture

Adarules: Learning rules for real-time road-traffic prediction

Traffic management is being more important than ever, especially in overcrowded big cities with over-pollution problems and with new unprecedented mobility changes. In this scenario, road-traffic prediction plays a key role within Intelligent Transportation Systems, allowing traffic managers to be able to anticipate and take the proper decisions. This paper aims to analyze the situation in a commercial real-time prediction system with its current problems and limitations. We analyze issues related to the use of spatiotemporal information to reconstruct the traffic state. The analysis unveils the trade-off between simple parsimonious models and more complex models. Finally, we propose an enriched machine learning framework, Adarules, for the traffic state prediction in real-time facing the problem as continuously incoming data streams with all the commonly occurring problems in such volatile scenario, namely changes in the network infrastructure and demand, new detection stations or failure ones, among others. The framework is also able to infer automatically the most relevant features to our end-task, including the relationships within the road network, which we call as “structure learning”. Although the intention with the proposed framework is to evolve and grow with new incoming big data, however there is no limitation in starting to use it without any prior knowledge as it can starts learning the structure and parameters automatically from data. (Part of special issue: 20th EURO Working Group on Transportation Meeting, EWGT 2017, 4-6 September 2017, Budapest, Hungary)Peer ReviewedPostprint (published version

From the news to the social representation, incidence in the educational environment of the Solarte Obando School

En la presente investigación, planteada desde la perspectiva exógena del docente de la escuela Solarte Obando, su objetivo es analizar la incidencia que tienen, en el ambiente educativo, las representaciones sociales recreadas por los medios de comunicación de la masacre en la vereda Alto Remanso en el municipio de Puerto Leguízamo. La investigación es de carácter cualitativo y enfoque interpretativista, haciendo uso del interaccionismo interpretativista como diseño. La sistematización y análisis se generó desde un planteamiento sujeto a los objetivos específicos y sus herramientas de recolección de información. Como resultado general se confirmó la influencia de los medios en la constitución de representaciones sociales, que cuentan con una ruta generativa y de interiorización a través del ambiente educativo hasta el aula de clase, dichas representaciones son respuestas a zonas de interés con que los medios dan tratamiento a la información y que son el escenario de confrontación entre versiones de legitimación y reivindicación. De esta forma el ambiente educativo, la escuela y su práctica y saber pedagógico se debaten entre la dialéctica de la legitimación estatal y los marcos de discriminación-estigmatización que transforman los sentidos colectivos de estos territorios hacia la desconfianza; sin embargo, estas representaciones preestablecidas fundan también los procesos de memoria histórica, paz y reconstrucción social.I.E.R. Over Antonio MoralesRadio Nacional de ColombiaMagíster en Educación Inclusiva e InterculturalMaestríaThis research originated from the perspective of a teacher at Solarte Obando school, taking into account external factors. Its objective is to analyze the impact of social representations, as portrayed by the media, on the educational environment following the massacre in the village of Alto Remanso in Puerto Leguizamo. The study was conducted using a qualitative approach, employing interpretative interactionism as the underlying design framework. Systematization and analysis were carried out based on specific objectives and data collection tools. The overall findings confirmed the media's influence on the formation of social representations, which then permeate the educational environment and reach the classroom through a generative and internalized process. These representations serve as responses to the media's focal points of legitimization and claims, which often form the backdrop for conflicting versions of events. Consequently, the educational environment, the school, pedagogical practices, and knowledge become entangled in a dialectic between state legitimization and frameworks of discrimination and stigmatization. This results in a transformation of the collective meanings associated with these territories, leading to a sense of mistrust. However, the established representations also intersect with processes of historical memory, peacebuilding, and social reconstruction

The 2-3 November 2015 flood of the Sió River (NE Iberian Peninsula): a flash flood that turns into a mudflow downstream

Historical and recent evidence shows that many floods in the interior of Catalonia (NE Iberian Peninsula) usually have such a great sediment load that can even alter the hydraulic behaviour of the flow. This is especially true in catchments with a great proportion of agricultural soils, which are the main source of sediment. The night of 2-3 November 2015 torrential rains fell on the headwaters of the Sió River catchment (508 km2); the subsequent flood caused four deaths and many damages along the stream. The hydrological, hydraulic and sedimentary characteristics of this recent flood have been analysed in order to gain a better insight on the characteristics of the major historical floods in the same catchment. The rainfall height on the headwaters was between 139 and 146 mm in ten hours, with a maximum intensity of about 50 mm h-1. In the rest of the catchment it rained much less (22-71 mm). The agricultural soils in the headwaters show evidence of intense erosion by laminar and concentrated Hortonian overland flow in their superficial layer (Ap1; 10 cm), which uncovered the more compact underlying layer (Ap2). The peak flow in the headwaters (Oluges) was 90 m3 s-1 (that is, a specific peak flow near 1 m3 s-1 km-2) and it diminished downstream: 40 m3 s-1 in the centre of the catchment (Oluges + 27 km) and 15 m3 s-1 in the outlet (Oluges + 54 km). The suspended sediment load was 10-15% in volume in the headwaters and, judging from recorded images and eyewitnesses, it increased as the flow moved downstream, turning the flash flood into a mudflow. This concentration gain was most probably caused by the flood wave’s water loss due to the dryness of the riverbed and translated in an increased viscosity that ultimately altered the hydraulic behaviour of the flow, slowing it down. This process of water loss has been observed in flash floods in dry riverbeds in arid and semiarid areas such as Negev (Israel) and Atacama (Chile). Historical floods in neighbouring catchments (Ondara and Corb Rivers) are known to have had hyperconcetrated flows.Peer ReviewedPostprint (published version

combining First Principles with deep neural networks

JP acknowledges PhD grant SFRD/BD14610472019, This work has received funding from the European Union's Horizon 2020 research and innovation program under grant agreement no 101000733 (PROMICON).Numerous studies have reported the use of hybrid semiparametric systems that combine shallow neural networks with First Principles for bioprocess modeling. Here we revisit the general bioreactor hybrid model and introduce some deep learning techniques. Multi-layer networks with varying depths were combined with First Principles equations in the form of deep hybrid models. Deep learning techniques, namely the adaptive moment estimation method (ADAM), stochastic regularization and depth-dependent weights initialization were evaluated in a hybrid modeling context. Modified sensitivity equations are proposed for the computation of gradients in order to reduce CPU time for the training of deep hybrid models. The methods are illustrated with applications to a synthetic dataset and a pilot 50 L MUT+ Pichia pastoris process expressing a single chain antibody fragment. All in all, the results point to a systematic generalization improvement of deep hybrid models over its shallow counterpart. Moreover, the CPU cost to train the deep hybrid models is shown to be lower than for the shallow counterpart. In the pilot 50L MUT+ Pichia pastoris data set, the prediction accuracy was increased by 18.4% and the CPU decreased by 43.4%.publishersversionpublishe