Autonomous and Assisted Control for Synthetic Microbiology

The control of microbes and microbial consortia to achieve specific functions requires synthetic circuits that can reliably cope with internal and external perturbations. Circuits that naturally evolved to regulate biological functions are frequently robust to alterations in their parameters. As the complexity of synthetic circuits increases, synthetic biologists need to implement such robust control “by design”. This is especially true for intercellular signaling circuits for synthetic consortia, where robustness is highly desirable, but its mechanisms remain unclear. Cybergenetics, the interface between synthetic biology and control theory, offers two approaches to this challenge: external (computer-aided) and internal (autonomous) control. Here, we review natural and synthetic microbial systems with robustness, and outline experimental approaches to implement such robust control in microbial consortia through population-level cybergenetics. We propose that harnessing natural intercellular circuit topologies with robust evolved functions can help to achieve similar robust control in synthetic intercellular circuits. A “hybrid biology” approach, where robust synthetic microbes interact with natural consortia and—additionally—with external computers, could become a useful tool for health and environmental applications.

Virtual Reality Nature Experiences Involving Wolves on YouTube: Presence, Emotions, and Attitudes in Immersive and Nonimmersive Settings

As some nature experiences, such as viewing wild animals, may be difficult to implement in science education, immersive virtual reality (VR) technologies have become a promising tool in education. However, there is limited knowledge regarding the effectiveness of nature experiences in VR. In this study, 50 German university students (M = 23.76 years, SD = 3.73 years) from diverse disciplines were randomly assigned to an immersive (head-mounted display; Oculus Quest) or a nonimmersive setting (external computer screen; desktop computer) and individually watched two 360° videos from the social media site YouTube about wolves in their natural habitat. Besides measuring participants’ attitudes towards wolves, we investigated their feeling of presence in the virtual environments with the Spatial Presence Experience Scale (SPES) and the retrospective emotions of interest, joy, and fear with the Differential Affect Scale (M-DAS). The immersive head-mounted display induced higher levels of presence and interest compared to the nonimmersive external computer screen. While higher interest in the screen setting was associated with more positive attitudes towards wolves, such a correlation could not be found in the head-mounted display setting. Thus, our study found that immersive technology could induce interest in a nature experience related to the tested socio-scientific issue, even among people who did not already hold positive attitudes toward the issue. Overall, our findings suggest that 360° videos using immersive technology provide nature experiences with positive affective learning outcomes, even though the study focused on nature experiences in VR and was not an educational experience per se. As we were unable to assess the role of novelty of VR experiences, the application of VR technologies and its effects in larger teaching and learning settings needs to be evaluated in further studies.

Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones

Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones Abraham Esteban Gamarra Moreno1, Juan Gamarra Moreno2, Job Daniel Gamarra Moreno3 1 Universidad Nacional del Centro del Perú, Av. Mariscal Castilla N° 3909, Huancayo, Perú 2 Universidad Nacional Mayor de San Marcos, Calle Germán Amézaga N° 375, Lima, Perú 3 Universidad Continental, Av. San Carlos 1980, Huancayo, Perú Recibido el 16 de junio del 2018. Aceptado el 5 de julio del 2018 Resumen La inteligencia artificial es un área que intenta dotar de inteligencia a las máquinas y entre los tópicos que desarrolla están los sistemas expertos, la lógica difusa, los sistemas de planificación, los algoritmos de búsqueda, la computación evolutiva, redes neuronales artificiales entre otros. Los tópicos de la inteligencia artificial que utiliza este artículo son la visión artificial y las redes neuronales artificiales; además utiliza el microbot o robot móvil Mindstorms NXT, que tiene una capacidad limitada en el procesamiento, así como en el almacenamiento de información. La limitación del robot móvil se da porque no tiene a bordo un computador potente para procesar los algoritmos de visión artificial y de las redes neuronales artificiales; por lo que se utiliza un computador externo para realizar su control a través de la tecnología bluetooth. El procesamiento de los algoritmos de visión artificial y de redes neuronales artificiales se realiza en el computador externo y las acciones que ejecuta el robot móvil son enviadas a este, a través de la comunicación bluetooth. El artículo considera que existe autonomía en un robot móvil, cuando este realiza sus acciones sin intervención humana y los indicadores seleccionados para medir esta autonomía son la localización autónoma de los patrones a reconocer y el reconocimiento autónomo o clasificación de estos patrones. La implementación de la localización autónoma de los patrones a reconocer utiliza sensores ópticos, sensores ultrasónicos y el lenguaje de programación C#; así como el reconocimiento autónomo de patrones utiliza una cámara inalámbrica ubicada en el robot móvil, algoritmos de visión artificial, redes neuronales artificiales y el lenguaje de programación visual basic .NET. Los resultados muestran que el promedio del indicador porcentaje de patrones localizados en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 37.81% cuando no se usa la inteligencia artificial y es de 97.18% cuando se usa la inteligencia artificial. Además, el promedio del indicador porcentaje de patrones reconocidos en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 46.25% cuando no se usa la inteligencia artificial y es de 96.87% cuando se usa la inteligencia artificial. Descriptores: inteligencia artificial, visión artificial, redes neuronales, clasificación de patrones, sensores ópticos, sensores de ultrasonido, microbots, Mindstorms NXT. Abstract Artificial intelligence is an area that tries to equip the machines with intelligence and among the topics developed are expert systems, fuzzy logic, planning systems, search algorithms, evolutionary computation, artificial neural networks among others. The topics of artificial intelligence used in this article are artificial vision and artificial neural networks; also uses the microbot or mobile robot Mindstorms NXT, which has a limited capacity in the processing, as well as in the storage of information. The limitation of the mobile robot is because it does not have a powerful computer on board to process artificial vision algorithms and artificial neural networks; so an external computer is used to perform its control through bluetooth technology. The processing of artificial vision algorithms and artificial neural networks is done on the external computer and the actions performed by the mobile robot are sent to it, through bluetooth communication. The article considers that there is autonomy in a mobile robot, when it performs its actions without human intervention and the indicators selected to measure this autonomy are the autonomous localization of the patterns to be recognized and the autonomous recognition or classification of these patterns. The implementation of the autonomous localization of the patterns to be recognized uses optical sensors, ultrasonic sensors and the C # programming language; as well as the autonomous recognition of patterns uses a wireless camera located in the mobile robot, artificial vision algorithms, artificial neural networks and the visual basic .NET programming language. The results show that the average of the indicator percentage of patterns correctly located in the environment by the Mindstorms NXT mobile robot is 37.81% when artificial intelligence is not used and it is 97.18% when artificial intelligence is used. In addition, the average of the indicator percentage of patterns correctly recognized in the environment by the Mindstorms NXT mobile robot is 46.25% when artificial intelligence is not used and is 96.87% when using artificial intelligence. Keywords: artificial intelligence, artificial vision, artificial neural networks, pattern classification, optical sensors, ultrasound sensors, microbots.

Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones

Uso de la Inteligencia Artificial para Incrementar la Autonomía de un Robot Móvil Mindstorms NXT en Tareas de Clasificación de Patrones Abraham Esteban Gamarra Moreno1, Juan Gamarra Moreno2, Job Daniel Gamarra Moreno3 1 Universidad Nacional del Centro del Perú, Av. Mariscal Castilla N° 3909, Huancayo, Perú 2 Universidad Nacional Mayor de San Marcos, Calle Germán Amézaga N° 375, Lima, Perú 3 Universidad Continental, Av. San Carlos 1980, Huancayo, Perú Recibido el 16 de junio del 2018. Aceptado el 5 de julio del 2018 Resumen La inteligencia artificial es un área que intenta dotar de inteligencia a las máquinas y entre los tópicos que desarrolla están los sistemas expertos, la lógica difusa, los sistemas de planificación, los algoritmos de búsqueda, la computación evolutiva, redes neuronales artificiales entre otros. Los tópicos de la inteligencia artificial que utiliza este artículo son la visión artificial y las redes neuronales artificiales; además utiliza el microbot o robot móvil Mindstorms NXT, que tiene una capacidad limitada en el procesamiento, así como en el almacenamiento de información. La limitación del robot móvil se da porque no tiene a bordo un computador potente para procesar los algoritmos de visión artificial y de las redes neuronales artificiales; por lo que se utiliza un computador externo para realizar su control a través de la tecnología bluetooth. El procesamiento de los algoritmos de visión artificial y de redes neuronales artificiales se realiza en el computador externo y las acciones que ejecuta el robot móvil son enviadas a este, a través de la comunicación bluetooth. El artículo considera que existe autonomía en un robot móvil, cuando este realiza sus acciones sin intervención humana y los indicadores seleccionados para medir esta autonomía son la localización autónoma de los patrones a reconocer y el reconocimiento autónomo o clasificación de estos patrones. La implementación de la localización autónoma de los patrones a reconocer utiliza sensores ópticos, sensores ultrasónicos y el lenguaje de programación C#; así como el reconocimiento autónomo de patrones utiliza una cámara inalámbrica ubicada en el robot móvil, algoritmos de visión artificial, redes neuronales artificiales y el lenguaje de programación visual basic .NET. Los resultados muestran que el promedio del indicador porcentaje de patrones localizados en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 37.81% cuando no se usa la inteligencia artificial y es de 97.18% cuando se usa la inteligencia artificial. Además, el promedio del indicador porcentaje de patrones reconocidos en forma correcta en el entorno por el robot móvil Mindstorms NXT es de 46.25% cuando no se usa la inteligencia artificial y es de 96.87% cuando se usa la inteligencia artificial. Descriptores: inteligencia artificial, visión artificial, redes neuronales, clasificación de patrones, sensores ópticos, sensores de ultrasonido, microbots, Mindstorms NXT. Abstract Artificial intelligence is an area that tries to equip the machines with intelligence and among the topics developed are expert systems, fuzzy logic, planning systems, search algorithms, evolutionary computation, artificial neural networks among others. The topics of artificial intelligence used in this article are artificial vision and artificial neural networks; also uses the microbot or mobile robot Mindstorms NXT, which has a limited capacity in the processing, as well as in the storage of information. The limitation of the mobile robot is because it does not have a powerful computer on board to process artificial vision algorithms and artificial neural networks; so an external computer is used to perform its control through bluetooth technology. The processing of artificial vision algorithms and artificial neural networks is done on the external computer and the actions performed by the mobile robot are sent to it, through bluetooth communication. The article considers that there is autonomy in a mobile robot, when it performs its actions without human intervention and the indicators selected to measure this autonomy are the autonomous localization of the patterns to be recognized and the autonomous recognition or classification of these patterns. The implementation of the autonomous localization of the patterns to be recognized uses optical sensors, ultrasonic sensors and the C # programming language; as well as the autonomous recognition of patterns uses a wireless camera located in the mobile robot, artificial vision algorithms, artificial neural networks and the visual basic .NET programming language. The results show that the average of the indicator percentage of patterns correctly located in the environment by the Mindstorms NXT mobile robot is 37.81% when artificial intelligence is not used and it is 97.18% when artificial intelligence is used. In addition, the average of the indicator percentage of patterns correctly recognized in the environment by the Mindstorms NXT mobile robot is 46.25% when artificial intelligence is not used and is 96.87% when using artificial intelligence. Keywords: artificial intelligence, artificial vision, artificial neural networks, pattern classification, optical sensors, ultrasound sensors, microbots.

Optothermal Control of Local Fluid Temperature in Microfluidics

This paper outlines an optothermal approach to manipulate local fluid temperatures in microfluidic and lab-on-chip systems. The system has the ability to control the size, location and the source heat flux using an external computer and is not constrained by predefined geometries, complex fabrication or control system. The thermal performance is evaluated using a temperature-dependent fluorescent dye. Experiments demonstrate localized heating up to 35°C over ambient temperature for a heat source spanning a downstream length of 1.5 mm. Experimental results are compared with a 1D thermal analysis of the system based on the Taylor-Aris dispersion.

Performance of the Philips CM200 FEG

Since its introduction four years ago the Philips Field Emission 200 kV microscope has undergone a number of changes enhancing its performance. It has also become possible to quantify its performance more accurately.New developments are the introduction of the SuperTWIN-α and UltraTWIN objective lenses and the CompuStage goniometer. The SuperTWIN-α objective lens has a higher maximum tilt angle (±40° instead of ± 15°) while the point resolution remains 0.24 nm. The UltraTWIN lens has a point resolution of 0.19 nm and a maximum tilt of ±24°. Better specimens for determining the information limit have resulted in proven information limits of < 0.15 nm and ≤0.13 nm for the SuperTWIN-α and UltraTWIN lenses, respectively. The CompuStage, a five-axis motorised and computer-controlled goniometer, improves performance with regard to safety (no danger of damage to specimen holder or pole pieces), maximum accessible tilt, accuracy of movement, control and drift. The accessibility of the CompuStage to external computer control makes this goniometer ideal for developments in automation such as tomography or computer-assisted tilting for diffraction.