Efectos de la edad y el sexo sobre la memoria espacial de ratas Wistar en el laberinto radial de 8 brazos

Trabajo de InvestigaciónEl presente estudio tuvo como objetivo evaluar el desempeño de 24 ratas Wistar en una tarea de memoria espacial, según las características de sexo y edad (ratas jóvenes y ratas adultas). Para este fin, se llevó a cabo una fase inicial de habituación de 10 minuto diarios en el laberinto radial de Olton, y una fase de entrenamiento de una tarea de memoria espacial durante 27 sesiones.INTRODUCCIÓN Y ASPECTOS GENERALES 1. RESUMEN 2. JUSTIFICACIÓN 3. MARCO TEÓRICO 4. MÉTODO 5. RESULTADOS 6. DISCUSIÓN Y CONCLUSIONES BIBLIOGRAFÍA ANEXOSPregradoPsicólog

A Computational Model of Afterimage Rotation in the Peripheral Drift Illusion Based on Retinal ON/OFF Responses

<div><p>Human observers perceive illusory rotations after the disappearance of circularly repeating patches containing dark-to-light luminance. This afterimage rotation is a very powerful phenomenon, but little is known about the mechanisms underlying it. Here, we use a computational model to show that the afterimage rotation can be explained by a combination of fast light adaptation and the physiological architecture of the early visual system, consisting of ON- and OFF-type visual pathways. In this retinal ON/OFF model, the afterimage rotation appeared as a rotation of focus lines of retinal ON/OFF responses. Focus lines rotated clockwise on a light background, but counterclockwise on a dark background. These findings were consistent with the results of psychophysical experiments, which were also performed by us. Additionally, the velocity of the afterimage rotation was comparable with that observed in our psychophysical experiments. These results suggest that the early visual system (including the retina) is responsible for the generation of the afterimage rotation, and that this illusory rotation may be systematically misinterpreted by our high-level visual system.</p></div

Inverse tissue mechanics of cell monolayer expansion

<div><p>Living tissues undergo deformation during morphogenesis. In this process, cells generate mechanical forces that drive the coordinated cell motion and shape changes. Recent advances in experimental and theoretical techniques have enabled <i>in situ</i> measurement of the mechanical forces, but the characterization of mechanical properties that determine how these forces quantitatively affect tissue deformation remains challenging, and this represents a major obstacle for the complete understanding of morphogenesis. Here, we proposed a non-invasive reverse-engineering approach for the estimation of the mechanical properties, by combining tissue mechanics modeling and statistical machine learning. Our strategy is to model the tissue as a continuum mechanical system and to use passive observations of spontaneous tissue deformation and force fields to statistically estimate the model parameters. This method was applied to the analysis of the collective migration of Madin-Darby canine kidney cells, and the tissue flow and force were simultaneously observed by the phase contrast imaging and traction force microscopy. We found that our monolayer elastic model, whose elastic moduli were reverse-engineered, enabled a long-term forecast of the traction force fields when given the tissue flow fields, indicating that the elasticity contributes to the evolution of the tissue stress. Furthermore, we investigated the tissues in which myosin was inhibited by blebbistatin treatment, and observed a several-fold reduction in the elastic moduli. The obtained results validate our framework, which paves the way to the estimation of mechanical properties of living tissues during morphogenesis.</p></div

Quantification of tissue flow speed and traction force strength.

<p>(A) Histogram of the flow speed in the tissue within 500 <i>μ</i>m from the tissue edge (left; n >100 from three independent experiments), and the flow speed plotted against the distance from the tissue edge (right; n >10 at each data point). (B) The traction force data are presented in the same way as the flow speed data in (A).</p

Velocities of the afterimage rotation on the light background.

<p>(A) Diagram of the human psychophysical experiment. In the gaze period (4 s), the still image of the FW stimulus, the reference rotating FW stimulus, and the fixation cross were presented on the light background, on which the locations of the still and reference FW stimuli were randomly allocated to the left or right. After the gaze period, the still FW stimulus disappeared, but the reference stimulus and the fixation cross remained. The observers were instructed to fixate on the fixation cross during this afterimage period (1 s) and then to report whether the perceived velocity of the afterimage of the absent FW stimulus was faster or slower than that of the reference stimulus. (B) Probability of seeing slower afterimage rotation than the reference rotation (the three observers: Y.H., Y.A., and T.M.). Each psychometric curve was fitted individually with a cumulative Gaussian function by means of a least squares method. The means (μ) of the Gaussian functions are given with their 95% confidence intervals. (C) Representative rotation velocity of the retinal ON/OFF model when changing the background luminance. The profile of the rotation velocity, which increased with time, is shown as a grey area. Three marks indicate the rotation velocities obtained in the psychophysical experiment (marks correspond to those in (B)).</p

Afterimage rotation in the peripheral drift illusion.

<p>(A) The Fraser-Wilcox (FW) stimulus, which was used by Faubert and Herbert <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115464#pone.0115464-Fraser1" target="_blank">[2]</a>, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0115464#pone.0115464-Faubert1" target="_blank">[3]</a>. (B) Illusory clockwise (light-to-dark) rotation is seen in the afterimage when the FW stimulus disappears from the light background. (C) Illusory counterclockwise (dark-to-light) rotation in the afterimage is seen on the dark background.</p

The direction of the afterimage rotation was examined by varying the background luminance.

<p>(A) Diagram of the human psychophysical experiment. In the gaze period (4 s), the FW stimulus and the fixation cross were both presented on the background with a specific luminance. After the gaze period, both the FW stimulus and the fixation cross disappeared, but the background was not changed. The participants were instructed not to move their eyes during this afterimage period (1 s). (B) Probability of seeing clockwise rotation (by the three observers: Y.H., S.S., and R.O.). Each psychometric curve was individually fitted with a cumulative Gaussian function by means of a least squares method. The means (μ) of the Gaussian functions are given with their 95% confidence intervals. (C) Representative rotation velocity of the retinal ON/OFF model when changing the background luminance (original). Those from the modified model based on Weber's law were also plotted (<i>a</i> = 1, <i>a</i> = 0.5 and <i>a</i> = 0.25, dashed lines). The relative luminance levels that gave a zero-rotation velocity were 0.38, 0.27 and 0.12 for <i>a</i> = 1, <i>a</i> = 0.5 and <i>a</i> = 0.25 respectively, and 0.5 for the original.</p

The retinal ON/OFF model produced afterimage rotation of the FW stimulus on the light background.

<p>(A) Input. A quarter of the FW stimulus suddenly disappeared and was replaced by the light background at 0 s. (B) Output after the disappearance of the FW stimulus. Red lines indicate the focus lines for which the ON- and OFF-type responses showed comparable values. (C) Time courses of input. Each marked line corresponds to the input time-series (in terms of the luminance level) at the marked position in (A). (D) Time courses of outputs from the ON- and OFF-type units (top). Each colored line represents the output in response to the input with the same color as in (C). Red lines and points indicate the focus lines and points. Schematic drawings of outputs at 0 s (a), 0.15 s (b) and 0.45 s (c) (bottom). Focus lines rotated slightly counterclockwise from 0 s (thin red lines) to 0.15 s (red lines) (b), then prominently rotated clockwise from 0.15 s (thin red lines) to 0.45 s (red lines) (c). The red marked points in (c) correspond to the three red points in the top panel.</p

Spatial distribution of the estimated elastic moduli.

<p>Notice that each estimate comes from the movie patch with 294<i>μ</i>m-by-294<i>μ</i>m size. (A) The heatmap of estimated values of bulk modulus is overlaid on the corresponding phase contrast image of cell monolayer (top), and the mean and S.E. are plotted against the distance from the edge (bottom), in which no dependence on the distance has been observed. (B) The shear modulus results are presented in the same way as the bulk modulus results in (A). No dependence on the distance from the edge either.</p

Overview of the inference algorithm.

<p>(A) A relation between our model and the obtained data. In a migrating cell monolayer, cells exert traction forces on the underlying substrate, in order to propel themselves forward. Since the cells adhere to the neighboring cells, the traction force introduces mechanical stress to the tissue and induces tissue flow. We simultaneously observed the velocity field in the tissue by the phase contrast imaging (top), and the traction force by measuring the displacement of fluorescent beads embedded into the soft substrate (bottom). The continuum-mechanical model, Eqs <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006029#pcbi.1006029.e004" target="_blank">4</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1006029#pcbi.1006029.e005" target="_blank">5</a>, quantitatively relates the tissue flow to the traction force by considering stress as the intermediate variable. At each time point, the model describes the stress evolution under the flow, and thus predicts the traction force at the next time step. Following this, the model receives the error feedback based on the difference between predicted and observed traction forces, so that the model state (the stress) is calibrated. (B) Schematic representation of the inference algorithm. Based on the procedure outlined in (A), the algorithm estimates the tissue stress tensor in space and time. Afterward, the model parameters are updated to reproduce the estimated stress dynamics. The procedure is repeated until the convergence.</p