Development of a Simulation Environment for the Learning of an Autonomous Navigation Algorithm for a 2-Meter Length Sailboat

[Resumen] Se presenta el desarrollo de un entorno virtual de simulación basado en Python Turtle para el entrenamiento de un algoritmo de aprendizaje por refuerzo destinado a la navegación autónoma de un velero de 2 metros de eslora. Este entorno de simulación permite entrenar el pilotaje autónomo en diferentes condiciones de viento y datos de navegación de la embarcación, en ausencia de obstáculos, por medio de la observación causa-efecto y una estrategia de recompensas que permiten al agente decidir las mejores acciones. La generación virtual de situaciones de navegación reduce las horas de pruebas de mar.[Abstract] The development of a virtual simulation environment based on Python Turtle is presented for the training of a reinforcement learning algorithm for the autonomous navigation of a 2 meters long sailboat. This simulation environment enables autonomous piloting to be trained in different wind conditions and navigation data from the vessel, in the absence of obstacles, through cause-effect observation and a reward strategy that allows the agent to decide the best actions. The virtual generation of navigation situations reduces the hours of sea trials.Los autores agradecen a la Facultat de Náutica de Barcelona la ayuda económica para la realización del proyecto Sensailorhttps://doi.org/10.17979/spudc.9788497498043

SPR analysis of LPS binding of mAbs 1A4 IgG3, IgG1 and IgG2b using a biotinylated LPS-streptavidin capture platform.

<p>Biotinylated LPS was immobilized on a streptavidin (SA) sensor chip. Sensorgrams were generated by injecting different concentrations of mAbs 1A4 IgG3 (10.4–333 nM), 1A4 IgG1(1.7–8 μM) or 1A4 IgG2b (0.1–3.3 μM) over the chip surface. Data shown is representative of three independent experiments with similar results. <b>Panel A,</b> a diagram depicting the antigen-antibody complex (and a proposed antibody-antibody interaction) formed on the chip surface. <b>Panel B</b> presents the sensorgram profile of 1A4 IgG3 (<b>left</b>) and the corresponding steady-state affinity determination (<b>right</b>). The sensorgrams for 1A4 IgG1 and IgG2b are presented in <b>Panels C and D</b>, respectively.</p

LPS binding affinity of each 1A4 subclass mAb analyzed by SPR using an antibody capture analysis approach.

<p>Anti-mouse antibody was covalently immobilized on a CM5 sensor chip. Each subclass of mAb 1A4 was injected individually over the chip surface, followed by injection of various concentrations of LPS (60–8,000 nM). Data shown is representative of three independent experiments with similar results. <b>Panel A</b> illustrates the complex formed on the chip surface. <b>Panel B</b> presents the sensorgrams (<b>top</b>) and steady-state binding analysis (<b>bottom</b>) of each 1A4 subclass variant.</p

Limit of detection of whole killed-cells <i>F</i>. <i>tularensis</i> (10⁴ CFU/mL) for each combination of 1A4 subclass variants as determined by antigen capture ELISA.

<p>Limit of detection of whole killed-cells <i>F</i>. <i>tularensis</i> (10⁴ CFU/mL) for each combination of 1A4 subclass variants as determined by antigen capture ELISA.</p

Competition ELISA for LPS binding of 1A4 subclass variants.

<p>Each 1A4 subclass mAb was assessed for its ability to compete with 1A4 IgG1 HRP conjugate for binding to LPS. Dose-response curve (%inhibition <i>vs</i> mAb concentration) of each 1A4 subclass variant was created by mixing HRP-labeled 1A4 IgG1 (fixed concentration) with various concentrations of unlabeled mAb (1A4 IgG3, IgG1 or IgG2b) before adding to microtiter plates pre-coated with LPS. Percent inhibition was calculated by % inhibition = [(OD₄₅₀ of 1A4 IgG1 HRP alone–OD₄₅₀ of 1A4 IgG1 HRP plus unlabeled mAb)/ OD₄₅₀ of 1A4 IgG1 HRP alone] x 100. The analysis was performed in quadruplicate and data shown are mean ± standard deviation. The IC₅₀ values were calculated from the sigmoidal dose-response curve model using SigmaPlot software.</p

Antibody-antibody interaction as determined by direct ELISA.

<p><b>Panel A</b>, the binding interactions between 1A4 IgG3 and each mAb subclass are demonstrated. <b>Panel B</b>, self-association of each subclass of the mAb 1A4 are shown. The experiments were carried out in quadruplicate. Data shown are mean ± standard deviation.</p

Specificity of mAbs 1A4 and FB11.

<p>Inactivated <i>F</i>. <i>tularensis</i> SCHU S4 (lane 1), SCHU S4 ΔwbtI (lacks <i>O</i>-antigen, lane 2), LVS (lane 3), <i>F</i>. <i>tularensis</i> subsp. <i>holarctica</i> (lane 4), subsp. <i>novicida</i> (lane 5), <i>F</i>. <i>philomiragia</i> (lane 6), purified <i>B</i>. <i>pseudomallei</i> LPS (lane 7) and <i>E</i>. <i>coli</i> LPS (lane 8) were separated on 12% SDS-PAGE gels and blotted onto nitrocellulose membranes. The membranes then were probed with mAbs 1A4 IgG3 (<b>Panel A</b>) and FB11 (<b>Panel B</b>). The existence of LPS in these samples was demonstrated by Pro-Q emerald 300 LPS staining (<b>Panel C</b>).</p