High hops on sand influenced by added mass effects

Various animals exhibit locomotive behaviors (like sprinting and hopping) involving transient bursts of actuation coupled to the ground through internal elastic elements. The performance of such maneuvers is subject to reaction forces on the feet from the environment. On substrates like dry granular media, the laws that govern these forces are not fully understood and can vary with foot size and shape, material compaction (measured by the volume fraction, f ) and kinematics of intrusion. To gain insight into how such interactions affect jumping on granular media, we study the performance of a self-actuated spring mass robot with a 7.62-cm flat circular foot. We compare performance between two jump strategies: a single-cycle sine-wave actuation (a “single jump”) and a counter-movement pull-up phase proceeded by a single jump (a “stutter jump”); both jump methods perform well on hard ground. We systematically vary F at fixed actuation parameters for both strategies, and find that both of these jumps perform similarly poorly in loose-packed granular media, reaching only 44% of the close-packed jump height. Introducing a delay time between the pull-up phase and the push-off phase of the stutter jump (the delayed stutter jump) results in significantly improved jump heights at low volume fraction, achieving 77% of the close packed height. A 1D simulation of the robot jumping on granular media reveals that the commonly used depth dependent and velocity dependent model of granular intrusion force is insufficient to reproduce experimental jump heights. To gain insight into the behavior of the granular media during these impulsive events, we image a foot through a transparent sidewall, recording high speed videos at different packing states (F = 0.58‑0.63). To monitor grain flow, we adapt particle image velocimetry techniques to perform a 2D particle tracking velocimetry analysis on these images. A region of grains moving with similar downward speed to the intruder emerges. Subsequently, we implement an added-mass model, an effect observed in fluids, to our granular jumping simulation and find agreement with experiment

Disengagement of motor cortex from movement control during long-term learning.

Motor learning involves reorganization of the primary motor cortex (M1). However, it remains unclear how the involvement of M1 in movement control changes during long-term learning. To address this, we trained mice in a forelimb-based motor task over months and performed optogenetic inactivation and two-photon calcium imaging in M1 during the long-term training. We found that M1 inactivation impaired the forelimb movements in the early and middle stages, but not in the late stage, indicating that the movements that initially required M1 became independent of M1. As previously shown, M1 population activity became more consistent across trials from the early to middle stage while task performance rapidly improved. However, from the middle to late stage, M1 population activity became again variable despite consistent expert behaviors. This later decline in activity consistency suggests dissociation between M1 and movements. These findings suggest that long-term motor learning can disengage M1 from movement control

Allele specific expression analysis identifies regulatory variation associated with stress-related genes in the Mexican highland maize landrace Palomero Toluqueño.

BackgroundGene regulatory variation has been proposed to play an important role in the adaptation of plants to environmental stress. In the central highlands of Mexico, farmer selection has generated a unique group of maize landraces adapted to the challenges of the highland niche. In this study, gene expression in Mexican highland maize and a reference maize breeding line were compared to identify evidence of regulatory variation in stress-related genes. It was hypothesised that local adaptation in Mexican highland maize would be associated with a transcriptional signature observable even under benign conditions.MethodsAllele specific expression analysis was performed using the seedling-leaf transcriptome of an F1 individual generated from the cross between the highland adapted Mexican landrace Palomero Toluqueño and the reference line B73, grown under benign conditions. Results were compared with a published dataset describing the transcriptional response of B73 seedlings to cold, heat, salt and UV treatments.ResultsA total of 2,386 genes were identified to show allele specific expression. Of these, 277 showed an expression difference between Palomero Toluqueño and B73 alleles under benign conditions that anticipated the response of B73 cold, heat, salt and/or UV treatments, and, as such, were considered to display a prior stress response. Prior stress response candidates included genes associated with plant hormone signaling and a number of transcription factors. Construction of a gene co-expression network revealed further signaling and stress-related genes to be among the potential targets of the transcription factors candidates.DiscussionPrior activation of responses may represent the best strategy when stresses are severe but predictable. Expression differences observed here between Palomero Toluqueño and B73 alleles indicate the presence of cis-acting regulatory variation linked to stress-related genes in Palomero Toluqueño. Considered alongside gene annotation and population data, allele specific expression analysis of plants grown under benign conditions provides an attractive strategy to identify functional variation potentially linked to local adaptation

Entre tortugas, canales y árboles talados. Aproximación arqueológica a los procesos industriales manifiestos en Tortuguero, Costa Rica (1871-1950)

Como parte de los resultados emanados del proyecto de investigación «Arqueología Industrial: Estado del arte y primer inventario nacional» —código 219-B8-077— de la Universidad de Costa Rica, se presentan los siguientes avances de la temporada de campo 2018 respecto al sitio arqueológico Tortuguero —sigla L-324 Tg—. Este trabajo aborda el estudio del pasado reciente —finales del siglo XIX hasta 1970— de una localidad ubicada al norte del litoral Caribe de Costa Rica, ello a partir de sus restos materiales y desde la perspectiva de la arqueología industrial.UCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Ciencias Sociales::Escuela de Antropologí

Proyecto de Investigación 219-B8-077 Arqueología Industrial: Estado del Arte y primer inventario nacional

El documento corresponde al informe final de la investigación correspondiente al proyecto Pry01-2018 (219-B8-077) Arqueología Industrial estado del arte y primer inventario nacional. Se llevó a cabo en la Escuela de Antropología y estuvo inscrito en la Vicerrectoría de Investigación de la Universidad de Costa Rica entre los años 2018-2020. Se aborda parte de la producción científica de la región Centroamericana, México y el Caribe relacionada con la investigación de contextos arqueológicos industriales. Asimismo, se resumen los resultados del inventario nacional y del trabajo arqueológico exploratorio de dos contextos en Costa Rica relacionados con el patrimonio industrial, cuya temporalidad se asocia a finales del siglo XIX e inicios del XX.UCR::Vicerrectoría de Docencia::Ciencias Sociales::Facultad de Ciencias Sociales::Escuela de Antropologí

Learning Terrain Dynamics: A Gaussian Process Modeling and Optimal Control Adaptation Framework Applied to Robotic Jumping

The complex dynamics characterizing deformable terrain presents significant impediments toward the real-world viability of locomotive robotics, particularly for legged machines. We explore vertical, robotic jumping as a model task for legged locomotion on presumed-uncharacterized, nonrigid terrain. By integrating Gaussian process (GP)-based regression and evaluation to estimate ground reaction forces as a function of the state, a 1-D jumper acquires the capability to learn forcing profiles exerted by its environment in tandem with achieving its control objective. The GP-based dynamical model initially assumes a baseline rigid, noncompliant surface. As part of an iterative procedure, the optimizer employing this model generates an optimal control strategy to achieve a target jump height. Experiential data recovered from execution on the true surface model are applied to train the GP, in turn, providing the optimizer a more richly informed dynamical model of the environment. The iterative control-learning procedure was rigorously evaluated in experiment, over different surface types, whereby a robotic hopper was challenged to jump to several different target heights. Each task was achieved within ten attempts, over which the terrain's dynamics were learned. With each iteration, GP predictions of ground forcing became incrementally refined, rapidly matching experimental force measurements. The few-iteration convergence demonstrates a fundamental capacity to both estimate and adapt to unknown terrain dynamics in application-realistic time scales, all with control tools amenable to robotic legged locomotion

Lift-off dynamics in a simple jumping robot

We study vertical jumping in a simple robot comprising an actuated mass-spring arrangement. The actuator frequency and phase are systematically varied to find optimal performance. Optimal jumps occur above and below (but not at) the robot's resonant frequency $f_0$. Two distinct jumping modes emerge: a simple jump which is optimal above $f_0$ is achievable with a squat maneuver, and a peculiar stutter jump which is optimal below $f_0$ is generated with a counter-movement. A simple dynamical model reveals how optimal lift-off results from non-resonant transient dynamics.Comment: 4 pages, 4 figures, Physical Review Letters, in press (2012