The present study investigated differences in the pickup of information about the size and location of an obstacle in the path of locomotion. The main hypothesis was that information about obstacle location is most useful when it is sampled at a specific time during the approach phase, whereas information about obstacle size can be sampled at any point during the last few steps. Subjects approached and stepped over obstacles in a virtual environment viewed through a head-mounted display. In Experiment 1, a horizontal line on the ground indicating obstacle location was visible throughout the trial while information about obstacle height and depth was available only while the subject was passing through a viewing window located at one of four locations along the subject’s path. Subjects exhibited more cautious behavior when the obstacle did not become visible until they were within one step length, but walking behavior was at most weakly affected in the other viewing window conditions. In Experiment 2, the horizontal line indicating obstacle location was removed, such that no information about the obstacle (size or location) was available outside of the viewing window. Subjects adopted a more cautious strategy compared to Experiment 1 and differences between the viewing window conditions and the full vision control condition were observed across several measures. The differences in walking behavior and performance across the two experiments support the hypothesis that walkers have greater flexibility in when they can sample information about obstacle size compared to location. Such flexibility may impact gaze and locomotor control strategies, especially in more complex environments with multiple objects and obstacles. To successfully navigate through complex environments in the natural world, humans and other animals must adapt their movements to the conditions that are encountered not only at the present moment but also in the near future. For example, a safety conscious hiker faced with a choice of which of two routes to follow may prefer the one that is initially more difficult if the alternative option eventually leads to a steep slope that lacks secure footing. A hiker who lacks the foresight to account for the dangerous terrain that lies farther ahead will eventually be faced with an unfortunate dilemma: to turn back, thus making an energetically costly corrective adjustment, or to attempt to scale the unsafe slope without injury and at great energetic cost. Similarly, anticipation plays an important role over shorter time-scales as the hiker chooses where to place his or her feet. Sudden and unexpected changes to foot placement are energetically costly, and introduce instability that could lead to falling. Avoiding these sudden adjustments requires the advanced sampling of the visual information necessary for guiding gait in the presence upcoming obstacles in a feedforward manner.In the present study, we investigated the visual information that walkers use to guide the placement and trajectory of the feet when approaching and stepping over an obstacle. The specific aim was to determine whether there are differences in when visual information about obstacle location and visual information about obstacle size  are needed. We present the results of two experiments designed to test the hypothesis that there is a greater flexibility in the time at which walkers must sample information about obstacle size compared to obstacle location.Perhaps the most clear-cut example of visual information sampled at one point in time being used at a later time to negotiate obstacles is when guiding the trail foot over the obstacle . Once thInterestingly, walkers use visual information in a feedforward manner well before the obstacle passes out of view. Despite the demands for precision in placement of the feet in front of and behind the obstacle –4 and inAlthough the act of moving the legs over the obstacle does not appear to require continuous visual sampling, it remains possible that walkers rely on on-line control to guide placement of the feet in front of the obstacle, especially given that variability in foot placement before the obstacle can lead to an increase in the risk of tripping , 3, 16. Although visual occlusion leads to differences in the kinematics of the approach to and step over an obstacle, we contend that it would be premature draw the conclusion that walkers rely on on-line rather than feedforward control to guide placement of the feet on the last step before the obstacle for the following reasons. First, subjects were still capable of successfully crossing the obstacle under such conditions. They simply adopted a more cautious strategy. Second, Patla  Of course, there is a limit to how far in advance such information can be sampled. When vision was occluded three steps before reaching the obstacle, subjects cleared the obstacle but with a higher safety margin . OccludiFor the purposes of the present study, there are two important points to take away from the prior research. First, although it remains possible that visual information is used in an on-line manner during some phases of obstacle crossing , the evidence also indicates that walkers are capable of guiding the lead and trail legs over the obstacle without concurrent visual information. Second, walkers perform best when information about the obstacle is available up until (or shortly before) placement of the trail foot before the obstacle.The previous research summarized above suggests that walkers are most successful in obstacle crossing when they are able to sample information about the obstacle at a particular point during the approach—viz. while initializing and executing the last step before the obstacle. The information that walkers sample at this time appears to be that which specifies the location of the obstacle and is needed to guide placement of the trail foot before the obstacle. When crossing obstacles that have non-negligible height and depth, however, the walker must adapt his or her behavior to the dimensions of the obstacle as well as its location. Hence, in addition to information about obstacle location, walkers must also pick up information about the extent of the obstacle. That is, obstacle crossing requires the detection of both exproprioceptive information  and exteroceptive information  , 15. WheThe primary aim of this study was to test the hypothesis that there is flexibility in the time that information about obstacle size can be sampled. Studies of both humans  and non-Walkers may also be able to pick up information about obstacle size later in approach. Humans do not place their trailing foot closer or farther from the obstacle across variations in obstacle height . If otheOur approach was to instruct subjects to step over obstacles in a virtual environment viewed through a head-mounted display (HMD). To test the flexibility in when information may be sampled, we manipulated the availability of visual information about the obstacle’s size. In Experiment 1, the obstacle’s location was always visible by displaying a narrow line at the base of the front edge of the obstacle. In this regard, our approach is similar to that in previous studies in which information about obstacle dimensions was removed while information about obstacle location was made available throughout the approach  and the full vision control condition.Fifteen undergraduate students from Rensselaer Polytechnic Institute volunteered to participate in the study. Subjects reported that they had normal or corrected-to-normal vision and did not have any visual or motor impairments. The protocol was approved by the Institutional Review Board at Rensselaer Polytechnic Institute and all subjects gave informed consent prior to participation.The experiment was conducted in a 6.5 m × 9 m laboratory. The HMD was an nVis SX111 stereoscopic head mounted display , with a resolution of 1280 pixels × 1024 pixels  per eye and a diagonal field of view of 111 degrees. Head position and orientation were tracked using an Intersense IS-900 motion tracking system . Data from the tracking system were used to update the position and orientation of the simulated viewpoint. Movements of the rest of the body were tracked using a 14 camera Vicon motion-capture system running at 120 FPS. Subjects wore tight fitting, stretchable clothing to which 34 retro-reflective markers were affixed in accordance Vicon full-body marker template. Subjects completed the experiment barefoot in order to avoid any irregularities caused by differences in footwear. The four Vicon markers normally used to record head position in the full-body template were instead attached to the HMD. The cables from the HMD and tracking system were bundled together and held by the experimenter, who walked alongside the participant as he or she moved to ensure that the cables did not interfere with the subject’s movement.The virtual environment was created using Vizard Virtual Reality Toolkit  running on an Alienware Area-51 PC , and consisted of a flat, grass-textured ground plane underneath a black sky . Bamboo Subjects’ feet were also rendered in the virtual environment and visible through the HMD see . The dimPrior to the beginning of each session, the subject’s leg length was measured as the distance from the greater trochanter of the femur to the floor, and orientation of the virtual foot was aligned to match that of the real-world foot. Subjects were instructed to begin each trial by pressing a button on a hand-held wireless mouse while (1) standing within the visually indicated start box and (2) facing the end post. If these two conditions were met at the time of the button press, a virtual obstacle or line appeared along the subject’s walking path. At the same time, an auditory go-signal indicated that the subject was to begin walking towards the post at a comfortable speed, without stopping, and while stepping over the virtual obstacle along the way.On some trials, the obstacle was visible only when the subject’s head was within a spatially defined viewing window. When the subject’s head was outside of the viewing window, the obstacle was invisible and was replaced by a thin line on the ground at the location of the obstacle’s front edge. Collisions between the virtual feet and the obstacle were continuously monitored. If any part of either foot collided with any part of the obstacle, an auditory “thud” was played through the speakers.The height, depth, and location of the obstacle were manipulated as independent variables. The values of all three variables were specified in units of leg length (LL) to ensure that the kinematic demands of obstacle crossing would be similar across subjects whose body dimensions varied.Each experimental session included 126 trials, 46 of which were full vision trials and 80 of which were viewing window trials. Full vision trials varied in obstacle height , depth , and distance of the obstacle from the start box . Subjects also performed an additional full vision trial to ensure an even number of trials, which was necessary for us to run even and odd numbered trials in opposite directions in the laboratory. On this additional trial, the obstacle was placed at 3.6 LL and had a height and depth of 0.1 LL.On viewing window trials, information about the obstacle’s dimensions was only available when the subject’s head was inside one of four spatially defined windows. These windows spanned increments of 0.7 leg lengths from the obstacle . Obstacle height , depth , and location  also varied across trials. The selected values of height and depth correspond to the extremes of the ranges used on full vision trials. All independent variables were manipulated within-subjects. All trial types were pooled and presented in a different fully randomized order to each subject. The entire experimental session lasted approximately 45 minutes.Subjects completed two short practice blocks prior to beginning the experiment. The first practice block comprised 20 full vision trials: 2 obstacle heights  × 2 obstacle depths  × 5 obstacle locations . In the second practice block, subject completed eight trials, two in each viewing window condition, with both obstacle height and depth set to 0.1 LL. Practice trials were intended to provide familiarity with the virtual environment, and were not included in the analysis.In interpreting the findings below, it is often helpful to know where the subject was in the gait cycle while the obstacle was visible in each viewing window condition. The size of the viewing windows (0.7 LL) was chosen to roughly match average step length during steady state walking  so that Subsequent to data collection, Vicon Nexus was used to convert marker positions into joint positions. Data were filtered using a seventh-order low-pass Butterworth filter with a cutoff at 4 Hz. Steps were identified using a velocity based kinematic analysis presented in  and usedWe calculated two measures of obstacle crossing behavior: maximum foot elevation and obstacle clearance. Maximum foot elevation was estimated based on the height of the toe marker at the peak of its trajectory during the crossing step. Obstacle clearance was based on the distance from the toe marker to the top of the obstacle at the moment that the toe marker crossed the front face of the obstacle.Our approach to analyzing the data relied on mixed-effects regression modeling using the “nlme” package in R. For each dependent measure, we built a set of linear models with various combinations of the three independent variables  and in some cases their interactions as predictors. To keep the number of levels of height and depth consistent across all five viewing window conditions, we excluded data from the middle height and middle depth conditions in the full vision condition. Decisions about which models to build and compare were motivated by the theoretical predictions. For most dependent measures, we first compared a model with height as a predictor against the baseline (intercept-only) model. If the height model is superior to the baseline model, this is equivalent to finding that the main effect of height is statistically significant. To test the main hypothesis of the study, we then compared the model with height against a model with both height and viewing window  as predictors. If behavior (as measured by the dependent variable) is influenced by viewing window or the height × viewing window interaction, the more complex model will be superior to the simpler model. Thus, by comparing the two models, we can determine whether viewing window influenced behavior or if the effect of height varied across viewing window.Model fit alone is not an adequate metric for determining which of two models is better, since more complex models always better fit the data. Instead, we compared models using the log-likelihood ratio comparison (in the form of a chi-squared significant test) and the change in Akaike Information Criterion (AIC). AIC is a widely used metric of the quality of a model because it captures both the goodness of fit and the complexity of the model. When reporting the difference in AIC (dAIC) between two models, negative values indicate that the model of interest is superior to the comparison  model.After analyzing the effects of height and viewing window, we repeated the process for depth and viewing window. Thus, these were treated as separate analyses. Technically, it is possible to combine height, depth, and viewing window into a single analysis for each dependent measure. However, such an approach would have required consideration of models with higher-order interaction terms . This was not necessary because the height × depth interaction was not statistically significant for any of the dependent measures.Number of steps On average, subjects took 5.59 (SE = 0.13) steps to reach the obstacle in the full vision condition. The mean number of steps was slightly greater in each of the viewing window conditions  compared to full vision. Indeed, the model with viewing window as a predictor had a significantly lower AIC than the baseline model (χ2(4) = 13.83, p<.01, dAIC = -5.83). Adding height (χ2(1) = 1.19, p = .28, dAIC = -.81) or both height and height × viewing window (χ2(5) = 1.53, p = .91, dAIC = 8.47) did not significantly improve the model. However, the model with depth and viewing window was superior to the model with viewing window (χ2(1) = 6.02, p<.05, dAIC = -4.02), reflecting a tendency to occasionally take an additional step during approach to deep obstacles (M = 5.73) compared to shallow obstacles (M = 5.64). We attribute this difference to the tendency  to place the feet slightly closer to the obstacle when it is deeper.Walking speed In general, subjects gradually accelerated between steps N-4 and N-1, reaching peak walking speed at step N-1 before decelerating on the step over the obstacle (χ2(4) = 128.23, p<.01, dAIC = -120.28). Adding height (χ2(1) = 1.01, p = .31, dAIC = 0.97) or depth (χ2(1) = 0.22, p = .64, dAIC = 1.78) did not improve the model relative to the step-number model, suggesting that subjects did not walk at different speeds in different obstacle conditions. However, AIC decreased when viewing window and viewing window × step number were included (χ2(20) = 98.01, p<.01, dAIC = 58.01). Inspection of obstacle . The chaObstacle height For lead foot placement, the model with height as a predictor was not significantly different than the baseline model (χ2(1) = 0.21, p = .65, dAIC = 1.791) (see χ2(5) = 5.56, p = .35, dAIC = 4.44), nor did it improve when the model included height, viewing window, and the height × viewing window interaction (χ2(9) = 15.46, p = .07, dAIC = 2.54). Although the difference between the full model and the baseline model is close to statistically significant, the change in AIC is positive, consistent with a poorer model. Taken together, the analysis reveals no evidence that lead foot placement was influenced by obstacle height, viewing window, or their interaction.791) see . This isχ2(1) = 1.95, p = .16, dAIC = 0.05), nor the model with height and viewing window (χ2(5) = 3.40, p = .64, dAIC = 6.60), nor the model with height, viewing window, height × viewing window (χ2(9) = 10.95, p = .28, dAIC = 7.05) was significantly better.Similarly, there was no evidence that trail foot placement was affected by either variable or their interaction. The model with the lowest AIC was the baseline model. Neither the model with height  = 13.13, p<.01, dAIC = -11.13). Adding viewing window did not significantly improve the model (χ2(4) = 7.03, p = .13, dAIC = 0.97), nor did adding both viewing window and the depth × viewing window interaction (χ2(8) = 8.96, p = .35, dAIC = 7.04). Thus, subjects placed their lead foot closer to the obstacle when it was deeper, but lead foot placement was not significantly affected by viewing window or the depth × viewing window interaction.ctor see . The depχ2(1) = 13.79, p<.01, dAIC = -11.79), but not significantly different from the model with viewing window (χ2(4) = 1.48, p = .83, dAIC = 6.52) or the model with viewing window and the depth × viewing window interaction (χ2(8) = 13.79, p = .09, dAIC = 2.22).A similar pattern of results was observed for trail foot placement. The model with the lowest AIC was the one with depth as a predictor. This model was significantly better than the baseline model , but subjects tended to place their feet slightly closer to the obstacle when it was deeper. However, neither lead nor trail foot placement were affected by the viewing window manipulation or its interactions with height or depth. The fact that subjects adapted foot placement to variations in obstacle depth in the VW-4 and VW-3 conditions provides compelling evidence that walkers are able to adapt to variations in obstacle dimensions even when the relevant information is sampled several steps in advance.The next set of analyses focuses on obstacle crossing and is organized by foot .Lead foot elevation and clearance Let us first consider the effects of obstacle height and viewing window on lead foot obstacle crossing (χ2(1) = 15.47, p<.01, dAIC = -13.47). This confirms that subjects were able to discriminate obstacles of different heights in the virtual environment and adapt their behavior as they do in the real world. Had subjects not been able to perceive variations in obstacle height , they could have successfully performed the task by elevating their feet to the same height . The increase in lead foot elevation with obstacle height suggests otherwise.crossing . The modχ2(8) = 20.05, p<.05, dAIC = -4.05), which suggests that the manipulation of obstacle height affected lead foot elevation differently depending on when the obstacle was visible. To unpack the interaction, we conducted four 2 (VW-# vs. Full) × 2  interaction contrasts using Dunnett’s correction. The interaction was significant (p < .05) in the VW-1 condition but not in any of the other viewing window conditions. This likely reflects adoption of a cautious strategy in the absence of information about obstacle height. If so, this would suggest that walkers are most successful in obstacle crossing when they can sample information about obstacle height prior to placement of the trail foot before the obstacle, which is consistent with the results of the analysis of walking speed.However, the model with the lowest AIC was the full model that included height, viewing window, and their interaction. This model was significantly better than the height-only model (χ2(1) = 49.82, p<.01, dAIC = -47.82)  = 26.18, p<.01, dAIC = -24.18), which is consistent with the findings of Patla and Rietdyk [χ2(4) = 8.77, p = .06, dAIC = -0.77) nor the full model (χ2(8) = 15.26, p = .06, dAIC = 0.74) were significantly better than the depth-only model. Thus, unlike the effect of obstacle height, the effect of obstacle depth did not significantly vary across viewing window conditions. The pattern of results was similar  = 6.42, p<.05, dAIC = -4.42). As with the lead foot, the increase in trail foot elevation was less than the change in obstacle height, resulting in a significant decrease in trail foot clearance (χ2(1) = 33.81, p<.01, dAIC = -31.81). Mean trail foot maximum elevation was greater for taller obstacles in each of the viewing conditions except for VW-1, mirroring the pattern of results observed with the lead foot. However, the model of maximum foot elevation with viewing window and height × viewing window was not significantly better than the height model (χ2(8) = 9.86, p = .27, dAIC = 6.14). In other words, the effect of obstacle height did not vary across viewing window conditions as it did for the lead foot. This is likely due to the greater variability in trail foot elevation.bstacles . This waχ2(1) = 12.02, p<.01, dAIC = -10.02) (see χ2(8) = 18.22, p<.05, dAIC = -2.22), suggesting that obstacle depth may have affected trail foot elevation differently across viewing window conditions. Specifically, inspection of Trail foot elevation also increased with obstacle depth (.02) see . The modχ2(1) = 3.60, p = .06, dAIC = -1.60) or viewing window χ2(4) = 9.23, p = .06, dAIC = -1.24) (see χ2(1) = 4.09, p<.05, dAIC = -2.09) (see Collision rates for the lead foot were below 2% on average and were not significantly affected by obstacle height (.24) see . Subject.09) see .χ2(1) = 7.72, p<.01, dAIC = -5.72) but was not significantly affected by viewing window (χ2(4) = 0.71, p = .95, dAIC = 7.29) or depth (χ2(1) = 0.04, p = .84, dAIC = 1.96) (see Trail foot collision rate increased with obstacle height (.96) see . The ratSeveral conclusions can be drawn from these analyses. First, when the obstacle was visible throughout the trial, subjects were able to adapt foot placement and trajectory to variations in the position, height, and depth of the obstacle, much like they do in the real world. The only measures on which performance noticeably differed were foot elevation during obstacle crossing and the frequency of collisions involving the trailing foot.Second, for most dependent variables, adding viewing window, height × viewing window, and/or depth × viewing window did not improve the model, suggesting that walking behavior was at most weakly affected by the absence of obstacle dimension information during most of the approach. The largest effects were observed when such information was not made available until subjects were within one step length of the obstacle. In this condition , the obstacle first appeared on average shortly before placement of the trailing foot before the obstacle see . The facThird, there was no evidence that walking behavior or obstacle crossing performance were affected when obstacle dimension information was available early in the approach and then removed . As in full vision, subjects adapted foot placement to obstacle depth but not height, elevated their feet to a greater height for taller and deeper obstacles, and maintained a comparable rate of collisions even in the VW-4 condition. This provides compelling evidence that walkers are able to sample information about obstacle size in advance and use that information several steps later to guide obstacle crossing.Taken together, the findings of Experiment 1 support the hypothesis that walkers have considerable flexibility in the timing of the sampling of information about obstacle size. Such information can be sampled early when the walker is several steps from the obstacle, which implies an ability to use information that was detected several steps earlier. Alternatively, information about obstacle size can be sampled later, shortly before placement of the trail foot before the obstacle, which reflects the fact that accommodating variations in obstacle size does not require adjustments until shortly before the step over the obstacle is initiated.In Experiment 1, our focus was on information about obstacle size and how such information can be sampled in a brief glance at any point between two and four steps in advance. Our hypothesis, which was motivated by previous research on obstacle crossing, is that such flexibility in the timing of visual sampling is specific to information about obstacle size. That is, information about obstacle location, unlike information about obstacle size, is most useful when it is sampled at a particular point in time during approach—shortly before placement of the trail foot before the obstacle. However, the difference is likely to be a matter of degree; that is, walkers may have some flexibility in terms of when they can sample information about obstacle location, but less flexibility than is afforded for information about obstacle size.The design of Experiment 1 does not allow us to compare such differences in flexibility because information about obstacle location was continuously available in all five conditions. The results of the previous studies summarized in the introduction provide some insight into the range of distances within which location information is most useful. However, any conclusions would be stronger if they were based on a more direct comparison using the same experimental paradigm. To this end, we conducted a second experiment that was similar to Experiment 1 but with the visibility manipulation applied to both size and location information. In other words, the obstacle was either visible or not. There was no persistent line indicating the position of the front edge of the obstacle. If information about obstacle location is most useful when it is sampled while the walker is within a narrow range of distances from the obstacle, then unlike in Experiment 1, differences between the viewing window conditions and the full vision condition should be widely observed.Nine undergraduate students from Rensselaer Polytechnic Institute volunteered to participate in the study. Subjects reported that they had normal or corrected-to-normal vision and did not have any visual or motor impairments. The protocol was approved by the Institutional Review Board at Rensselaer Polytechnic Institute and all subjects gave informed consent prior to participation.The methods were identical to those of Experiment 1, with the exception that the line at the front edge of the obstacle was removed in Experiment 2. As such, information about obstacle location, height, and depth was only available when the subject’s head was within the viewing window.As in Experiment 1, timing of the viewing windows was determined by the position of the subject’s head from the obstacle, defined in units of leg length. The actual time at which the appearance and disappearance of the obstacle were triggered with respect to the events in the gait cycle is presented in Whereas the viewing window manipulation had little effect on walking behavior during the approach phase in Experiment 1, differences between the VW conditions and the full vision condition were apparent in Experiment 2.Number of steps The number of steps that subjects took before crossing the obstacle was significantly affected by viewing window (χ2(4) = 95.77, p<.01, dAIC = -87.77). As depicted in χ2(1) = 0.05, p = .82, dAIC = 1.95 compared to the model with viewing window), but adding depth did (χ2(1) = 5.50, p<.05, dAIC = -3.50). As in Experiment 1, the mean number of steps was slightly greater during approaches to deeper obstacles.Walking speed The manipulation of viewing window also affected walking speed (see χ2(24) = 386.539, p<.001, dAIC = -338.539 compared to the baseline model). Adding either height (χ2(1) = 0.15, p = .70, dAIC = 1.85) or depth (χ2(1) = 1.30, p = .25, dAIC = 0.70) did not further improve the model. These analysis suggest that walking speed was consistent across changes in obstacle dimension but that the walking speed profile varied depending on when the obstacle was visible. As depicted in peed see . The modOn their own, these findings could be interpreted as evidence that subjects proceeded cautiously due to the absence of information about obstacle size, obstacle location, or both. However, in Experiment 1, which differed from Experiment 2 only in that information about obstacle location was visible throughout each trial, walking speed in the VW conditions was nearly identical to full vision on each step with the exception of step N-1. This suggests that it was the absence of obstacle location information (not size information) that led subjects in Experiment 2 to walk initially at a slower speed.Interestingly, walking speed was slower overall compared to Experiment 1 even in the full vision condition. We attribute this difference to the adoption of a more cautious strategy in the VW conditions of Experiment 2, and the fact that full vision trials were randomly interleaved with and less frequent than trials in which obstacle visibility was manipulated. Thus, the tendency to walk slower apparently carried over to full vision trials.Obstacle height As in Experiment 1, placement of the lead and trail feet before the obstacle on the last step before crossing was consistent across obstacle height (χ2(1) = 0.26, p = .61 dAIC = 1.74 for the lead foot; χ2(1) = 0.01, p = .96, dAIC = 2.00 for the trail foot; see χ2(4) = 116.51, p<.001, dAIC = -108.51) and trail foot (χ2(4) = 72.70, p<.001, dAIC = -64.70) placement.Contrasts with Dunnett’s correction between each VW condition and the full vision condition revealed significant differences (p < .05) in the VW-2 and VW-1 conditions for the lead foot and in the VW-1 condition for the trail foot see . Thus, iIn the VW-2 condition, subjects placed their lead foot significantly closer to the obstacle compared to full vision, but trail foot placement was not significantly different than full vision. These effects were observed for each combination of obstacle height and depth. This makes sense because in the VW-2 condition, the obstacle did not appear on average until shortly before the end of the lead foot’s last step before the obstacle see . Thus, sIn the VW-1 condition, both the lead and trail feet were placed significantly closer to the obstacle than in full vision. This also makes sense because in the VW-1 condition, the obstacle did not appear until after placement of the lead foot and shortly before placement of the trail foot.Obstacle depth Lead foot placement was not significantly affected by obstacle depth (χ2(1) = 2.41, p = .12, dAIC = -0.41) but trail foot placement was significantly closer when the obstacle was deeper (χ2(1) = 9.64, p<.01, dAIC = -7.64) (see χ2(4) = 110.00, p<.001, dAIC = -102.00) and trail foot (χ2(4) = 69.13, p<.001, dAIC = -61.13) placement..64) see . Adding Contrasts with Dunnett’s correction between each VW condition and the full vision condition revealed significant differences (p<.05) in the VW-2 and VW-1 conditions for the lead foot and in the VW-1 condition for the trail foot. It is noteworthy that subjects placed their trail foot closer to the obstacle when it was deeper in the VW-4 condition, as they did in the full vision condition see . This prThe results of the analysis of foot placement before the obstacle are not particularly surprising. Nevertheless, the contrast with the results of the same analysis for Experiment 1  highlighLead foot elevation and clearance As shown in χ2(1) = 4.60, p<.05, dAIC = -2.60) and deeper (χ2(1) = 6.34, p<.05, dAIC = -4.34). The tendency for subjects to increase the maximum foot elevation for taller and deeper obstacles was also found in Experiment 1. However, foot elevation in Experiment 2 was consistently greater than it was in Experiment 1 even in the full vision condition, despite the fact that trials in the full vision condition were identical in both experiments. This clearly reflects the adoption of a more cautious strategy due to the increased difficulty of the task in Experiment 2.χ2(4) = 33.46, p<.001, dAIC = -25.46). Further adding height × viewing window resulted in an increase in AIC, suggesting that the effect of obstacle height did not significantly vary across VW conditions. Contrasts with Dunnett’s correction between each VW condition and the full vision condition were significant (p<.05) only in the VW-1 condition. Thus, subjects’ ability to elevate their lead foot over short and tall obstacles was affected only when the obstacle did not become visible until the last step. This was also the case in Experiment 1. However, whereas lead foot elevation in VW-1 was greater than or comparable to that in full vision in Experiment 1 (see Adding viewing window as a predictor significantly improved the model of lead foot elevation relative to the height-only model (Trail foot elevation and clearance Trail foot elevation was significantly affected by both obstacle height and viewing window (χ2(5) = 25.93, p<.001, dAIC = -15.93 compared to the baseline model) but the interaction between these two variables was not statistically significant (χ2(4) = 8.09, p = .08, dAIC = -0.09) (see .09) see . Contrasχ2(5) = 28.38, p<.001, dAIC = -18.38 compared to the baseline model) but the interaction between depth and viewing window was not statistically significant (χ2(4) = 5.26, p = .26, dAIC = 2.74).The analysis of the effects of depth and viewing window on trail foot elevation revealed a similar pattern of results see ; that isTaken together, this analysis suggests that walkers are able to properly elevate their trail foot over obstacles of varying heights and depths as long as visual information about the obstacle was available between roughly two and three step lengths in advance. If such information is available before or after this portion of the approach phase, the walker’s ability to properly elevate the trail foot will be degraded. Furthermore, because these effects were observed in Experiment 2 but not in Experiment 1, we can conclude that the degradation is due to the absence of information about obstacle location specifically.χ2(1) = 3.51, p = .06, dAIC = -1.51) or depth (χ2(1) = 2.39, p = .12, dAIC = -0.39), but the addition of viewing window as a predictor did improve the model (χ2(4) = 11.93, p<.05, dAIC = -3.93). This effect is likely to be a consequence of the reduced foot placement distance before the obstacle in the VW-1 condition.As in the full vision condition of Experiment 1, collisions were rare for the lead foot but more frequent for the trail foot see . Lead foχ2(1) = 5.49, p<.05, dAIC = -3.49) but was not affected by the manipulation of obstacle depth (χ2(1) = 1.55, p = .21, dAIC = 0.45). The manipulation of viewing window also affected trail foot collision rate (χ2(4) = 15.64, p<.01, dAIC = -7.64), in particular in the VW-1 and (to a lesser degree) the VW-4 conditions.Trail foot collision rate was significantly greater for taller obstacles  in Experiment 2 compared to Experiment 1. Taken together, the results indicate that obstacle crossing performance degrades when information about obstacle location is not available when it is needed to guide placement of the feet in front of the obstacle. This contrasts with the information about obstacle size, which can be sampled at any point during the last few steps before placement of the lead foot before the obstacle, as demonstrated in Experiment 1. Thus, walkers have less flexibility in when they can sample information about obstacle location compared to information about obstacle size.To successfully cross an obstacle in the path of locomotion, humans must adapt their gait to both the location of the obstacle and its dimensions . Walkers are most successful in obstacle crossing when they can sample information about location shortly before placement of the trail foot in front of the obstacle, which makes sense given that precise foot placement plays a key role in minimizing the risk of tripping , 3, 16. The ability to use information about obstacle size that was detected several steps in advance was demonstrated in Experiment 1, in which behavior in the VW-4 condition was similar to that observed in the full vision condition on measures of number of steps, walking speed, lead and trail foot placement, maximum foot elevation, foot clearance, and number of collisions. On measures where behavior was affected by manipulations of obstacle height and depth in the full vision condition, behavior was similarly affected in the VW-4 condition. In both conditions, subjects placed their feet slightly closer to the obstacle when it was deeper but not when it was taller, and elevated their feet to a greater height when the obstacle was taller and when it was deeper. Although behavior in the VW-4 condition was also similar to the full vision condition in Experiment 2, when the line marking obstacle location was absent, performance in both conditions was degraded compared to the corresponding conditions in Experiment 1. Thus, the ability to use information that was sampled in advance is specific to information about obstacle size.Why are walkers able to use information about obstacle size that is sampled in advance but need information about obstacle location later during the approach? One possibility is that size and position  differ in their stability. Information about properties that do not vary from moment to moment, such as obstacle size, may be sampled well in advance because their stability makes it possible for that information to be useful at a later time. In contrast, information about properties that vary from moment to moment, such as the walker’s position relative to the obstacle, must be sampled around the time that it is needed for making gait adjustments. A similar account has been proposed to explain why the transport and manipulation components of reaching and grasping are affected differently when there is a delay between viewing the object to be picked up and initiating movement of the hand (see ). Of couThe ability to use information about obstacle size later in the approach was apparent in the similarity of behavior in the VW-2 and full vision conditions of Experiment 1. Although the obstacle did not appear until shortly before placement of the lead foot, subjects generally elevated their feet to a greater height for taller and deeper obstacles as they did in the full vision condition. Nevertheless, some of these effects were weaker than they were in the full vision condition, suggesting that subjects’ ability to adapt to changes in obstacle dimensions was slightly diminished in this condition. When the obstacle did not appear until the last step before crossing (VW-1 condition), subjects were even less able to tailor foot elevation to variations in obstacle height and depth. Thus, the last point at which subjects are able to adapt gait to obstacle size appears to be shortly before placement of the trail foot in front of the obstacle.Why are walkers able to adapt to changes in obstacle size even when the relevant information is not made available until shortly before obstacle crossing? Even when information about obstacle size is available throughout the approach, walkers apparently do not make any adaptations to gait that are specific to obstacle size prior to the step over the obstacle.  As such, not knowing the dimensions of the obstacle prior to the initiation of obstacle crossing does not impair performance in the way that not knowing the location of the obstacle does.The findings of the present study demonstrate that walkers are capable of adapting gait to variations in obstacle height and depth even if information about these characteristics is sampled several steps in advance. One might wonder about the significance of this ability during real-world obstacle crossing given that walkers must sample information about obstacle location later during the approach anyway (to guide foot placement in front of the obstacle), and could sample information about obstacle dimensions at the same time. In other words, what is the functional significance of estimates of obstacle dimension based on information that is detected well in advance if those estimates are updated later during the approach, when information about obstacle location must be sampled?Although walkers need information about obstacle location when they are closer to the obstacle, they do not necessarily need to look at the obstacle to pick up that information. In many complex environments, there are other objects that are visible in the upper visual field that could be used to keep track of one’s position relative to the obstacle without actually looking at it. For example, if the obstacle is a fallen branch that lies on a hiking path near the base of a tree, the walker could perceive his or her changing position relative to the branch without looking down or relying on peripheral vision by detecting information about the relative position of the tree, much like subjects in the study by Rietdyk and Rhea  relied oThis could be especially useful when there are multiple obstacles in the path of locomotion. In such situations, walkers must shift their gaze to future obstacles before stepping over the obstacle that is most immediately in front of them . The abiThe present study is one of a small number on obstacle crossing to be conducted in a virtual environment (VE) viewed through a head-mounted display (HMD). VEs offer a potentially powerful tool for investigations of visually guided locomotion over complex terrain, as they enable researchers to manipulate visual information in a manner that is not possible in the real world. Of course, there are also some drawbacks. Misperceptions of size and egocentric distance , latDespite these differences, subjects were consistent in the placement of their lead and trail feet in front of the obstacle, elevated their feet to a greater height for taller and deeper obstacles, and avoided making obstacle contact with the lead foot on the vast majority of trials. Thus, in many respects, obstacle crossing behavior in the present study was similar to that observed in the real world. Given the potential for using VEs in both basic research on locomotion as well as in the diagnosis and treatment of disorders that affect mobility , further research on obstacle crossing behavior in virtual environments, as well as comparisons of behavior in real and virtual environments, would make a valuable contribution.Although walkers need information about both the location and size of an upcoming obstacle to guide successful crossing, only information about obstacle location is needed at a specific point in time during approach. The success with which walkers are able to negotiate obstacles is less dependent upon when information about obstacle height and depth is sampled. They can sample information about obstacle size several steps in advance and use that information to adapt the placement and trajectory of the feet during obstacle crossing. Alternatively, they can sample such information as late as the last step before obstacle crossing and still elevate their feet to different heights depending on the dimensions of the obstacle.


The role of p53 as “a guardian of the genome” has been well established in somatic cells. However, its role in pluripotent stem cells remains much more elusive. Here, we discuss research progress in understanding the role of p53 in pluripotent stem cells and in pluripotent stem cell-like cancer stem cells. The p53 protein, which plays a key role in embryonic stem cells, was first discovered in 2005. Landmark studies of p53-related reprogramming elucidated this protein’s importance in induced pluripotent stem cells in 2009. The p53-related safety concerns in pluripotent stem cells have been raised in stem cell-based therapy although the use of iPSCs in therapeutic application is promising. Because cancer stem cells have profiles similar to those of pluripotent stem cells, we also describe potential strategies for studies in cancer stem cells and cancer treatments. The new discoveries of p53 family proteins in pluripotent stem cells have made possible stable progress in stem cell transplantation efficiency and safety, as well as treatment strategies targeting cancer stem cells based on pluripotent stem cell technology. Although just a fairy tale, the theme of the story can be interpreted to mean “regeneration medicine”, one of the hottest research topics today. The existence of endogenous pluripotent stem cells and embryonic stem cells (ESCs) holds great promise for regenerative medicine. Induced pluripotent stem cells (iPSCs) were first generated from mouse somatic cells in 2006 through viral insertion of four transcription factors: Oct4, Sox2, Klf4, and cMyc .A year later, it was observed that human adult cells were also capable of being converted into pluripotent stem cells under similar conditions , 3. Due However, despite the great potential of pluripotent stem cell-based cell therapy, some important issues regarding safety concerns have been raised, especially their ability to induce tumorigenesis and teratoma formation . To addrThe protein p53 has been described as “a guardian of the genome” that protects somatic cells from tumorigenesis. However, whether p53 plays the same role in pluripotent stem cells is much more elusive. While efficiency in iPSC generation has been achieved, tumor-related safety studies involving iPSCs are limited. In fact, some iPSC generation methods may even undermine iPSC safety with respect to tumorigenesis. The current use of iPSCs in clinical trials is therefore limited due to tumorigenic risks. We thus discuss the role of p53 in pluripotent stem cells for clinical application and in pluripotent stem cell-like cancer cells, and how pluripotent stem cell technology can be used for cancer treatment.While the tumor-suppressor p53 activation in somatic cells during stress results in cell-cycle arrest and apoptosis, its activities in pluripotent stem cells remain unexplored. Different roles of p53 in ESCs, however, continue to be discovered. At the end of 2004, p53 was found to play a key role in the differentiation of mouse embryonic stem cells , and thiWhen DNA damage occurs in ESCs, phosphorylation of p53 at serine 315 occurs leading to high expression of the p53 protein. In particular, two sequences within the Nanog promoter region are regulated by p53 . Our extUnder normal conditions, with an intact p53 signaling pathway, ESCs can be differentiated by shifting culture conditions from mouse ESC (mESC) medium with mouse leukemia inhibitory factor (Lif) to mouse embryonic fibroblast (mEF) medium supplemented with retinoic acid (RA) Fig.  24]. Un. Un24]. In contrast, the differentiated cells could be de-differentiated or reprogrammed back to ESC status when the p53 switch is turned off in humanized p53S315A knock-in mouse ESCs Fig. . This meIn contrast to the findings noted above, recent studies have revealed that p53 might be involved in negative feedback pathways in the absence of DNA damage in mouse ESCs. For example, Abdelalim and Tooyama found that p53 was required for mouse ESC self-renewal . In addiThe detailed mechanism of the pluripotency switch by p53 protein isoforms was elegantly discovered by Ungewitter and Scrable in 2010 . This stThese two p53 homologues have specific functional differences from p53 and from one another. Mice lacking p63 are born with developmental defects of the limbs and skin caused by impaired ectodermal differentiation during embryogenesis , while pTypically, iPSCs are generated through insertion of viral genome into the sequences of a set of four pluripotency-associated genes: Oct4, Sox2, cMyc, and Klf4 –3. Such However, iPSC generation was initially a very slow and inefficient process, lasting approximately 2 weeks for mouse cells and 4 weeks for human cells. Furthermore, the original conversion of somatic cells into iPSCs occurred at an extremely low rate: approximately 0.01% in human  and 0.1%p53 expression not only reduces iPSC generation efficiency, but its deletion also replaces the reprogramming factors. It has been reported that deletion of c-Myc from the four-factor gene cocktail could generate iPSCs but that the yield is dramatically low-efficient iPSCs. However, a greatly improved reprogramming efficiency (by 10%) of parental cells generated into iPSCs was seen if p53 was switched off . More reThe p53 pathway switches on when DNA damage occurs; hence, only cells having no DNA damage will be able to undergo reprogramming , 43. p53All iPSC generation proteins  are expressed either individually or collectively in many cancer cell types , 45. HowAccordingly, a recent study found that the production of pluripotent stem cells, which is similar to the creation of the so-called “carcinogenic foci,” can also be used as an experimental model for the study of cancer development and generation of possible treatment strategies . SimilarSome studies have linked iPSCs and pluripotent stem-like cancer cells. Kitajima et al. observed that deleting retinoblastoma protein (Rb) and N-ras genes in combination with oncogenic Trp53 mutations resulted in cancer stem-like cells, showing elevated expression of embryonic genes, carcinogenic identities, and sensitivity to cancer stem cell target compounds . In manyIf these data are representative of tumor or cancer stem cell initiation, then strategies based on pluripotent stem cell technology investigating cancer stem cells might be developed for cancer study and treatment Fig. . In Fig.Shown as a red sphere, the tumor stem cell can freely transfer from a point in the developmental hill to some other point, including pluripotent stem-like and metastasis-like status. Once the normal activity of p53 is reactivated, the pluripotent stem-like status can be restricted and the cancer stem cell loses most of its stem cell properties. Previous findings have shown that activation of p53 by nutlin, a small-molecule antagonist of MDM2, leads to the rapid differentiation of human ESCs . BecauseA well-designed paper published a discovery that the undifferentiated state was induced by Rb-p53 double inactivation in mouse somatic cells in 2015 . Using iThe protein p53 has long been established as a key factor for the safety of adult cells. However, recent studies of pluripotent stem cells, including ESCs and iPSCs, have found that p53 is also a key player in pluripotent stem cell differentiation and in reprogramming to generate induced pluripotency. Given what is currently known regarding the generation of stem cells, p53-related activities must be carefully assessed in pluripotent stem cell-based research and applications.Pluripotent stem cells, including ESCs and iPSCs, are capable of transforming into malignant teratomas, carcinomas, or CSCs once p53 is not controlled. In contrast, the activation of p53 alone or in association with other factors is capable of inhibiting cancer via inhibition of stem cell-related mechanisms. As such, the use of p53 is a potential therapeutic strategy in the management of cancer by maintaining normal and genetically safe cells.Reactivation of p53 might control and rescue cells from switching into cancer stem cells with malignant pluripotency. However, due to other p53 protein isoforms, such as the deleted p53 isoform mentioned above , we shouAs discussed above, undifferentiated reprogramming resulting from Rb-p53 double inactivation might be a major target of the cancer stem cell treatment strategy. The triple abnormality RN6 cells showed not only pluripotency but also cancer formation potential. RN6 cells with pluripotency and tumorigenesis have already been found to be sensitive to agents that target against cancer stem cells. Targeting the pluripotency as a whole to deal with cancer stem cells, similar to the cell RN6 in which the p53 proteins might be at the center of the regulation target, would be a good strategy. Since p53 switches off pluripotency during differentiation plays major roles in pluripotent stem cells and in pluripotent cancer cells, the activated p53 switch in cancer stem cell management should be applied to cancer treatment.


The transcription factor p63 is a master regulator of epidermal development. Mutations in p63 give rise to human developmental diseases that often manifest epidermal defects. In this review, we summarize major p63 isoforms identified so far and p63 mutation-associated human diseases that show epidermal defects. We discuss key roles of p63 in epidermal keratinocyte proliferation and differentiation, emphasizing its master regulatory control of the gene expression pattern and epigenetic landscape that define epidermal fate. We subsequently review the essential function of p63 during epidermal commitment and transdifferentiation towards epithelial lineages, highlighting the notion that p63 is the guardian of the epithelial lineage. Finally, we discuss current therapeutic development strategies for p63 mutation-associated diseases. Our review proposes future directions for dissecting p63-controlled mechanisms in normal and diseased epidermal development and for developing therapeutic options. TP63 gene, belongs to the p53 gene family. Distinct from the leading member of the gene family, p53, that plays an important role in tumor suppression, the role of p63 in cancer is not fully understood. However, p63 has been shown to be a key regulator of epidermal development. This has been demonstrated in various animal models and by p63 mutation-associated human diseases. For example, complete deletion of p63 in mice results in the absence of the epidermis and epidermal related appendages, as well as defects in other epithelial-related tissues [TP63 cause several developmental disorders, and many of these diseases manifest skin abnormalities [The transcription factor p63, encoded by the  tissues –3. In humalities –7. Classmalities –10. Sevemalities –16. AmonDifferent p63 isoforms have been shown to play roles in various cells and tissues, such as the epidermis, oocytes, muscles and cochlea. As such, this review discusses p63 isoforms identified so far, with a focus on isoforms that are expressed in epidermal cells. Subsequently, we dedicate most of this review to the most studied topic in p63 biology: the role of p63 in epidermal development and related diseases. Specifically, we provide an overview of p63 mutation-associated developmental diseases with epidermal phenotypes. We discuss in-depth key molecular and cellular mechanisms by which p63 controls gene regulation to define epidermal identity, and speculate that the affected cell fate contributes to various disease states. Finally, we review current efforts and future perspectives in developing therapeutic strategies for treating these diseases.ΔN  in 1997, as it is homologous to p53 in rat epithelial tissues . One yeaTP63 transcript was detected, rather than being degraded completely via the nonsense-mediated decay mechanism that is common for stop mutations. This incidental observation led to the identification of an alternative translational start site downstream of all stop mutations. This alternative translational start site gives rise to the ΔΔN protein variant in AEC/RHS patient keratinocytes, and it is also present in keratinocytes of healthy controls, although at a lower level than the ΔN isoform. At the C-terminal end, two other isoforms, δ and ε, were identified by bioinformatic analyses using the Alternative Splicing Prediction Data Base, ASPicDB (http://www.caspur.it/ASPicDB/index.php) and validated by reverse transcription quantitative PCR (RT-qPCR) in several cell lines including keratinocytes, HEK293 cells and in some tissues, such as the muscle and the brain [In recent years, several novel isoforms have been reported. At the N-terminal end of the p63 protein, an alternative translational start site located in the 4th exon gives rise to a ΔΔN isoform that lacks the first 26 amino acids of the ΔN isoform in epidermal keratinocytes Fig. . This ishe brain . The δ ihe brain . In the he brain . IsoformExpression patterns and functions of TA and ΔN isoforms during epidermal commitment and subsequent stratification have been under debate, although it is generally agreed that the ΔN isoform is highly expressed in basal epithelial cells. One report showed that the TA isoform was expressed first and subsequently required to initiate epidermal stratification, but it was counter-balanced by the ΔN isoform during stratification . In contAs recently developed genomic approaches allow detecting active promoters and expressed exons, these techniques greatly facilitate the identification of isoform expression at the transcript level. Several independent analyses using RNA-seq, ChIP-seq of RNA polymerase II, DNA hypersensitivity sites (DHS)-seq and cap analysis gene expression (CAGE) , 22, 29 As for p63 C-terminal isoforms, the α isoform is the predominant one in most p63-expressing cells , althougTP63 have until now only been associated with ectodermal-related disorders manifested with three hallmark defects: ectodermal dysplasia, limb malformation and orofacial clefting [ΔN domain or C-terminal TID or SAM domain of the ΔNp63α isoform [Although p63 isoforms are expressed in a range of tissues of different germ layer origins, germline mutations of clefting . These d isoform  Fig. 1)TP63 haveΔN and C-terminal SAM domains of p63. For ADULT syndrome, the amino acid R298 in the DNA-binding domain is the most significant hot spot of mutations [Among these disorders, EEC syndrome is the prototype of p63 mutation-associated disorders, as it exhibits defects of all three hallmarks. About 30% of EEC patients have skin defects, and the skin is often thin and dry and sometimes resembles dermatitis. Other affected tissues with ectodermal origin include hair, teeth, nails, and lacrimal ducts Table . These putations . Rather utations . Anotherutations .Table 1PAmong all p63 mutation-associated syndromes, the most severe skin phenotypes are observed in AEC/RHS syndromes Table . About 7TP63 mutations. This notion is supported by several lines of evidence. First, TP63 mutations associated with human syndromes are heterozygous, and mostly missense mutations [TP63 gene was lost in a larger genomic deletion region, the patient did not present any ectoderm dysplasia phenotype [TP63 mutations in human disease, two knock-in mouse models carrying Trp63 (encoding p63 in mice) mutations recapitulating EEC (R279H in human) [R279HN. Heterozygous Trp63R279HN mice exhibited phenotypes that are similar to those observed in EEC syndrome patients. This includes cleft palate, anomalies of the distal limbs, defective tooth morphogenesis, and dystrophic nails. Although no major skin abnormalities were detected in Trp63R279HN mice, primary keratinocytes from these mice exhibit reduced proliferation and increased senescence. Furthermore, an increased level of p63 mRNA and protein was detected in Trp63R279HN mice, which is consistent with observations reported in the epidermis of p63 EEC patients [It is generally believed that dominant negative or gain-of-function is the disease mechanism of utations . In a dihenotype . Second,henotype , 38, 39.n human)  and AEC n human)  were reppatients . These ppatients .Due to the striking epidermal phenotype of p63 knockout mouse models and of p63 mutation-associated human diseases, the molecular and cellular role of p63 in epidermal keratinocytes has been extensively studied. It has been shown that p63 is important in both keratinocyte proliferation and differentiation , 44. HigThe identification of p63 target genes has been a major effort in the field, either through dedicated candidate gene studies or through genome-wide explorative approaches. These studies identify the genomic binding regions of p63 and of p63-regulated genes most often by either knock-down or overexpression of p63 , 55, as As a master regulator of epidermal development, p63 regulates transcriptional programs not only through its target genes but also via higher order regulation. In recent years, a number of studies have shown that p63 plays a role in epigenetic regulation in epidermal keratinocytes via several independent mechanisms. p63 can recruit HDAC1/2 to control the repression of cell cycle arrest genes, such as p21 and 14-3-3σ . p63 wasIn addition to regulating chromatin factors and nuclear envelop-associated components involved in higher order gene regulation in keratinocytes, it has become evident that p63 plays a master role in regulating enhancers. It should be noted that many studies discussed below took unbiased genomic approaches rather than p63-centered approaches, yet they came to the conclusion that p63 is the most significant transcription factor that controls epidermal enhancers. Bao et al. showed that genomic regions of p63-bound enhancers detected in keratinocytes are nucleosome-enriched and inaccessible in cells where p63 is not expressed Fig. a, and thIn summary, p63 is likely to regulate gene expression in epidermal keratinocytes at several levels: it regulates direct target genes involved in keratinocyte proliferation and differentiation; it directly regulates chromatin and epigenetic factors; and it might act as a pioneer factor to shape the chromatin and enhancer landscape, and thereby regulate global gene expression.Consistent with the recent proposed pioneer role of p63 in epidermal development, classical studies in mice nearly two decades ago showed that p63 is essential for proper epidermal commitment , 3. Two Recently, spatio-temporal expression of p63 during normal mouse embryonic development was analyzed . p63 expAlthough the essential role of p63 in epidermal commitment has been established, the gene networks and regulatory mechanisms of epidermal commitment that are upstream and downstream of p63 are not yet fully understood. A closer look into molecular pathways during epidermal development reveals the interaction of p63 and bone morphogenetic protein (BMP) signaling . The ΔNpTaken together, in vivo and in vitro studies have demonstrated that p63 is essential for epidermal commitment. The molecular pathways and gene regulatory mechanisms that act during epidermal commitment remain to be elucidated. It will be particularly interesting to investigate whether p63 functions as a pioneer factor during this process, either alone or most probably together with other co-regulators, and how p63 mutations affect this process.Somatic cell transdifferentiation is a process in which transient ectopic expression of lineage master regulators can induce conversion into a different somatic cell fate without passing through a pluripotent configuration –81. GiveTwo studies have also suggested that p63 plays a role in cell reprogramming to a pluripotent cell state. In one study, the authors reported that p63 enables reprogramming to pluripotency in mouse embryonic fibroblasts . In thisSo far, gene regulatory programs during these cell fate conversions have not yet been identified. The precise role of p63 in gene regulation during cellular reprogramming, especially towards an epithelial fate or during a switch between different epithelial fates, is important for understanding its master regulatory function controlling epithelial cell identity. Beyond exploring the fundamental biological role of p63, the examples discussed in this review also hold great potential for developing therapeutic options, e.g. converting human epidermal keratinocytes to LSCs for cornea regeneration or to other rare epithelial types.Other than cosmetic surgery and correction , there iMET was shown to interact with the mutant p53 core domain and promote its folding to the wild-type conformation [Initially designed to treat cancers, the small compound APR-246/PRIMA-1ormation , 90. Thiormation . Given tormation , the potormation , 93. Sheormation . As p63 In addition to small compounds, small interfering RNAs (siRNA) have also been explored for their therapeutic potential in treating p63 mutation-associated diseases. Novelli et al. reported the identification of a panel of siRNAs specifically targeting the p63 EEC R304W mutant transcripts. These R304W-targeting siRNAs can rescue defects during corneal epithelial differentiation of hiPSCs carrying the R304W mutation . SimilarIn addition to approaches discussed in this review, rapidly emerging technological tools, such as genome editing, have provided new possibilities for developing therapeutic options for p63 mutation-associated diseases. Further in vivo studies and clinical trials are necessary to validate these therapeutic options and eventually to treat patients.Since the discovery of the transcription factor p63 nearly two decades ago, its master regulatory role in the development of the epidermis has been extensively studied in cellular and animal models and at normal and diseased states. Although many isoforms have been identified for the p63 protein, the ΔNp63α is the most abundant isoform in epidermal cells and is probably responsible for the bulk of the functional p63 activity. p63 plays its key role in epidermal commitment and terminal differentiation, not only by regulating its direct target genes, but also by shaping the chromatin state, in particular, the enhancer landscape. Recent in-depth molecular analyses suggest that p63 functions as one of the pioneer factors for epidermal cell identity. This model is supported by classical cellular and in vivo studies as well as recent transdifferentiation studies where p63 is required for epithelial cell lineages. Testing this pioneer factor model during epidermal commitment at the molecular level and identifying key regulators that cooperate with p63 are critical topics for future research. It is plausible that p63 mutations involved in human disorders may deregulate gene expression by disrupting the pioneer factor function and thereby the epithelial epigenetic landscape, subsequently giving rise to epidermal phenotypes. Current and ongoing efforts to understand the role of p63 in normal and diseased conditions are not only important for the fundamental insights into gene regulation and epidermal development, but also for the development of novel therapeutic strategies to treat patients with p63 mutation-associated and other related diseases. Although therapeutic development for these diseases is still at its infancy, encouraging results of small molecule compounds and siRNA approaches have opened new possibilities, and should be extended further to eventually benefit patients.


MiR122 and miR33a/b* expression was analyzed by qRT-PCR. Additionally, miR122 and miR33b* circulating levels were analyzed in 122 women. Hepatic miR33b* expression was increased in MO compared to ModO and controls, whereas miR122 expression was decreased in the MO group compared to ModO. In obese cohorts, miR33b* expression was increased in nonalcoholic steatohepatitis (NASH). Regarding circulating levels, MO patients with NASH showed higher miR122 levels than MO with simple steatosis (SS). These circulating levels are good predictors of histological features associated with disease severity. MO is associated with altered hepatic miRNA expression. In obese women, higher miR33b* liver expression is associated with NASH. Moreover, multiple correlations between miRNAs and the expression of genes related to lipid metabolism were found, that would suggest a miRNA-host gene circuit. Finally, miR122 circulating levels could be included in a panel of different biomarkers to improve accuracy in the non-invasive diagnosis of NASH.Specific miRNA expression profiles have been shown to be associated with nonalcoholic fatty liver disease (NAFLD). We examined the correlation between the circulating levels and hepatic expression of miR122 and miR33a/b*, the key lipid metabolism-related gene expression and the clinicopathological factors of obese women with NAFLD. We measured  Nonalcoholic fatty liver disease (NAFLD), the hepatic consequence or precursor of type 2 diabetes and metabolic syndrome, is the most common chronic liver disease in developed countries ,2,3. TheIn recent years, microRNAs (miRNAs) have emerged as important post-transcriptional regulators of lipid metabolism and other pathological conditions . These sThus, the aim of the present study was to analyze the liver expression of miR33a/b* and miR122 in women in relation to the degree of obesity and also to the absence/presence of NAFLD. We assessed the relationship between the liver expression of these miRNAs and the expression of the main genes involved in lipid metabolism in these cohorts, and compared the potential of the circulating levels of these miRNAs with the gold-standard method, liver biopsy, as the diagnostic and prognostic biomarkers of differentiation between simple steatosis and steatohepatitis in obese patients.Here, we present the main results obtained in response to our four objectives.First, we describe the main characteristics of our cohort, including anthropometric, biochemical  parameters and, finally, parameters of liver histology.2), moderately obese (ModO) (BMI 32–38 kg/m2), and morbidly obese (MO) subjects (BMI > 40 kg/m2). Biochemical analyses indicate that ModO and MO women had significantly higher levels of homeostatic model assessment 2-insulin resistance (HOMA2-IR), glucose, insulin, HbA1c, and triglycerides than the control group. High-density lipoprotein cholesterol (HDL-C) was significantly lower in the ModO and MO patients than in the control group. However, ModO women had significantly higher levels of fasting glucose than the MO group. Secondly, we classified the ModO and MO cohorts according to the liver pathology into normal liver (NL), simple steatosis (SS) and nonalcoholic steatohepatitis (NASH). Their characteristics are also described in Patients’ baseline characteristics, given in The first objective of the present study was to analyze the liver expression of miR33a/b* and miR122 in women in relation to the degree of obesity. First of all, we want to clarify that, in the present work, we included the study of miR33b* liver expression instead of miR33b because the first accumulates to higher steady-state levels in the liver than miR33b and, moreover, it has been little studied.p = 0.012 and p = 0.041; p = 0.003; p = 0.425 and p = 0.674).Our results indicate that the gene expression of miR33b* was significantly greater in MO women than in ModO and control women , in order to facilitate the understanding.p < 0.001). Later, we classified the NAFLD cohort into SS and NASH. miR33b* expression was significantly higher in NASH and SS than in NL . Furthermore, the gene expression of miR33b* was significantly higher in NASH than in NL .In addition, we explored the association between miRNAs expression in liver and histopathological features. We found that miR33b* correlated positively with hepatocyte ballooning and lobular inflammation : p < 0.001; ATP binding cassette transporters A1 (ABCA1): p = 0.018; ATP binding cassette transporters G1 (ABCG1): p < 0.001, while the expression of the genes involved in lipid oxidation was diminished in both ModO and MO women compared to the normal-weight controls : p = 0.033, carnitine O-octanoyltransferase (CROT): p = 0.003; The results of the liver expression of genes related to lipid metabolism depending on the degree of obesity are shown in SREBP2 and ABCG1 gene expression was significantly higher in NAFLD than in NL . Furthermore, gene expression of SREBP2 was significantly higher in NASH vs. NL  were significantly up-regulated in NASH vs. SS  was higher in NASH vs. SS (p = 0.042 and p = 0.029). ACC1 gene expression was also increased in NASH vs. NL  and a gene related to lipid oxidation (CROT). We also found a positive correlation between the liver expression of miR33a and PPARα. However, we found negative correlations between the expression of this miRNA, and ABCG1 and CROT. Finally, we found no correlation between miR122 expression and the expression of genes involved in lipid metabolism.In The last objective of our work was to study the potential of the circulating levels of these miRNAs as diagnostic and prognostic biomarkers of differentiation between simple steatosis and nonalcoholic steatohepatitis. We also evaluated them according to BMI and to liver histology.miR33a according to degrees of obesity and liver histology so we no longer determine their circulating levels.No significant differences were found in the hepatic expression of p < 0.001; p < 0.001). Moreover, circulating levels of miR122 were significantly higher in patients with ModO than in those with MO . Moreover, the most important result was that MO patients with NASH showed significantly higher circulating levels of miR122 than MO with SS . We found no correlation between serum levels and hepatic expression of the other miRNAs studied.When we studied the correlation between miRNAs and their expression in liver, a significant correlation was detected between serum and hepatic r = 0.430, p < 0.001) and negative correlation with HDL-C . A significant correlation was also observed between miR122 and liver enzymes . The correlation of miR33b* circulating levels with glucose, triglycerides and AST was also positive .miRNA 122 circulating levels revealed a strong positive correlation with glucose , receiver operating characteristic (ROC) curves were obtained. The accuracy with which miR122 discriminates NAFLD from non-NAFLD subjects and an advanced disease from a mild clinical form showed an average area under the curve of receiver operating characteristics (AUROC) of about 0.82 and 0.76, respectively .The binary logistic regression model that contained miR122 serum levels adjusted for age, BMI, HDL-C, triglycerides, AST, and ALT as covariates showed that miR122, along with ALT, made the highest contribution to the detection of the hepatocellular ballooning presence in population studied and was expressed by the exponent values of the beta coefficients (exp (B) or odd ratio: 2.19 for miR122; 1.03 for ALT; miR122 and miR33a/b*, the key lipid metabolism-related gene expression, and the clinicopathological factors of ModO and MO patients with NAFLD. We also investigated whether circulating miRNA levels are good predictors of NAFLD severity.In a previous study, we demonstrated a clear relationship between NAFLD and genes related to the de novo synthesis of fatty acids in a cohort of women with MO . ConsidemiR33b* and the down-regulation of miR122. In this sense, recent studies report the functional involvement of miRNAs in metabolic and endocrine pathways and adipogenesis [Nowadays, the relationship between human liver miRNA expression and degrees of obesity has not been studied yet. The present work provides evidence that morbid obesity is associated with altered hepatic miRNA expression in humans, specifically the up-regulation of ogenesis ,29. The ogenesis ,35,36. Aogenesis .miR33b* expression has been found in several human tissues including the liver [miR33b* liver gene expression in both ModO and MO women.Although there are numerous studies on the role of miRNAs in viral hepatitis and hepatocellular carcinoma in humans , relativhe liver , its relmiR33a and miR122 expression between groups classified by liver histology. However, some authors have reported that liver miR122 expression is decreased in NASH [Another finding is that there were no significant differences in  in NASH ,26 and m in NASH . These dmiR33a and miR33b work in concert with their host genes, SREBP2 and SREBP1, to ensure that a cell’s metabolic state is balanced in different in vitro studies [ABCG1 expression was increased in NASH vs. SS. In ModO patients, SREBP1c and ACC1 expression, genes related to liver lipogenesis, SREBP2 and ABCG1, genes related to lipoprotein secretion, and CPT1α, related to fatty acid oxidation, were increased in NASH vs. SS. In addition, we found a positive correlation between miR33a and PPRAα expression and a negative correlation with ABCG1 and CROT. In this respect, miR33a and miR33b, intronic miRNAs located in the SREBP2 and 1 genes, respectively, have recently been shown to regulate lipid homeostasis in concert with their host genes [miR33a and miR33b and their passenger strands, miR33a*/b*, and demonstrated that they share a similar lipid metabolism target gene network. They also showed that miR33 and miR33* represses key enzymes involved in cholesterol efflux (ABCA1 and NPC1) and fatty acid metabolism (CROT and CPT1α) in human hepatocyte cell lines [miR33a correlated inversely with ABCA1 protein levels. Moreover, miR33 target gene, CROT, correlated inversely with miR33a. However, regarding miR33b*, in our study, we found positive correlations between this miRNA and genes related to lipoprotein secretion and to lipid oxidation. These discrepancies could be explained because our study is conducted in a cohort of obese women, whereas Goedeke et al. studied this relationship in human hepatic cell line [Some authors have previously established that  studies ,40. In ost genes ,40,41,42ll lines , in agrell lines , in a MOell line . It is kell line . Therefoell line . AltogetmiR122, and suggested that the lower expression of miR122 in liver is a consequence of a high rate of release into the circulation [miRNAs can also circulate freely in blood, so we evaluated the microRNA circulating levels of miR33b* and miR122 in our cohort. We detected a correlation between serum and hepatic miR122 expression, in agreement with other authors , which sculation .Of particular interest among our findings is that circulating miR122 and miR33b* levels were higher in ModO and MO women than in normal-weight subjects. Moreover, circulating levels of miR122 were higher in patients with ModO than in those with MO. In this respect, previous studies have shown that several miRNAs seem to be involved in the regulation of adiposity and insulin sensitivity. Wang et al. showed that elevated circulating miR122 was positively associated with obesity and insulin resistance in young adults . Other agold standard for the histopathological diagnosis of NAFLD, its invasive nature means that it is difficult to use and unsatisfactory. Therefore, it is essential to search for NASH biomarkers. To do so, we examined circulating miRNAs and their association with the stratification of histological disease severity, and we found that miR122 levels were increased in MO patients with NAFLD compared with MO with NL. It should also be noted that MO women with NASH showed significantly higher circulating levels of miR122 than MO with SS. Similarly, Pirola et al. [Although liver biopsy is still the a et al.  analyzeda et al. ,48. Morea et al. . As the a et al. ,51.We also explored the association between miRNA circulating levels and the severity of NAFLD histology and found a positive correlation between miR122 and lobular inflammation and hepatocellular ballooning. ROC analysis revealed that miR122 has the potential to distinguish NASH from SS, because it predicts hepatocellular ballooning or lobular inflammation with an AUROC of 0.76. Although this predictive value is not good enough for an ideal biomarker, the ability of miR122 to predict NASH is quite similar to that of other studies ,53,54. HOur cohort has made it possible to establish clear relationships between obese women with NAFLD and altered hepatic miRNA expression, without the interference of such confounding factors as gender or age. However, these results cannot be extrapolated to men or overweight subjects.2) (MO), 30 moderately obese women (BMI 32–38 kg/m2) (ModO), and 30 normal-weight control women (BMI < 25 kg/m2). Liver biopsies were obtained from the morbidly obese women during planned bariatric surgery, from the moderately obese women during laparoscopic cholecystectomy for benign gall bladder disease or laparoscopic hiatus hernia repair and from the normal-weight women by means of percutaneous biopsy. Of 30 normal-weight control women, we only obtained liver biopsies from 8 women. All biopsies were performed for diagnostic indications. The pathology service of the hospital provided the control liver samples, taken with informed consent, and once they had been shown to be normal, the minimal excess sample was used to perform gene expression analysis.The study was approved by the institutional review board . All participants gave written informed consent for participation in medical research. In this study we included 62 morbidly obese women ; (2) MO with NL histology (n = 22); (3) MO with simple steatosis (SS) ; (4) MO with nonalcoholic steatohepatitis (NASH) ; (5) ModO with NL histology (n = 9); (6) ModO with SS ; and (7) ModO with NASH .According to their liver pathology and BMI, patients were sub-classified into the following groups: (1) Liver controls: normal-weight subjects with normal liver (NL) histology . cDNA was synthesized using a High Capacity RNA-to-cDNA Kit (Applied Biosystems).The liver samples obtained were conserved in RNAlater  for 24 h at 4 °C and then stored at −80 °C. TaqMan Assays predesigned by Applied Biosystems  were used for the detection of To quantify miRNA from liver samples, we used the miRNeasy Mini Kit  for the isolation of total RNA. The specific primers used—hsa-miR33a, hsa-miR33b*, hsa-miR122—were all from Applied Biosystems, and values were normalized with the housekeeping gene hsa-miR-16.elegans miR39 miRNA mimic was added to the serum samples before RNA was extracted (miRNeasy Serum/Plasma Spike-In Control).To quantify miRNA from serum samples, we used the miRNeasy Serum/Plasma Kit (Qiagen). The housekeeping gene C. All reactions were carried out in duplicate in 96-well plates using the 7900HT Fast Real-Time PCR systems (Applied Biosystems).t-test or one-way ANOVA. The strength of association between variables was calculated using Pearson’s method or Spearman’s ρ-correlation test. Area under the receiver operating characteristic curve (AUROC) was used as an accuracy index for evaluating the diagnostic performance of the selected miRNA. To analyze the potential of circulating miRNAs to designate hepatocellular ballooning in liver, we performed a binary logistic regression model. miR122, with a non-normal distribution, were log transformed for this analysis. p values <0.05 were considered to be statistically significant.All the values reported are expressed as mean ± SEM and were analyzed using SPSS version 23.0 . Differences between groups were calculated using Student’s In conclusion, there is also a clear relationship between the presence of NASH and higher miR33b* liver expression in both ModO and MO women. Moreover, miR33a/b* seem to act as “micro-controllers” of hepatic lipid metabolism in NAFLD. Finally, the circulating levels of miR122 could be included in a panel of different biomarkers to improve the accuracy of the non-invasive diagnosis of NASH.


These roles are achieved by interaction with other proteins, but particularly by interaction with DNA. As a transcription factor, p53 is well known to bind consensus target sequences in linear B-DNA. Recent findings indicate that p53 binds with higher affinity to target sequences that form cruciform DNA structure. Moreover, p53 binds very tightly to non-B DNA structures and local DNA structures are increasingly recognized to influence the activity of wild-type and mutant p53. Apart from cruciform structures, p53 binds to quadruplex DNA, triplex DNA, DNA loops, bulged DNA and hemicatenane DNA. In this review, we describe local DNA structures and summarize information about interactions of p53 with these structural DNA motifs. These recent data provide important insights into the complexity of the p53 pathway and the functional consequences of wild-type and mutant p53 activation in normal and tumor cells. Tp53 mutations and inactivation of this gene plays a critical role in malignant transformation [p53 is one of the most intensively studied tumor suppressor proteins and its regulation and relation to cancer has been reviewed extensively ,2,3. Theormation ,3. As a ormation ,5,6. It The discovery of B-DNA structure by Watson and Crick in 1953 showed the basic structure of DNA . FurtherHairpin and cruciform formation is dependent on DNA sequence and requires inverted repeats in the nucleic acid sequence. The length of this repeat should be six or more nucleotides ,15. High2+ ions, which relieves the electrostatic repulsion [Taq DNA polymerase [S. cerevisiae and triplex formation reveals single stranded DNA  and less by dimers (15%) or monomers (6%), but are also bound by higher oligomeric p53 forms (33%) . T4 endosolvases . They inficiency . Althougficiency ,121. Intficiency ,122 and ficiency ,123. By ficiency . The strficiency . Interesnd ~4500  shows a nd ~4500 .Hemicatenanes are thought to be important intermediates for DNA replication, repair and recombination. This structure is created by a denaturated DNA duplex that renatures in the presence of HMGB1 or HMGB2  and is rMammalian telomeres are organized into large duplex loops in vivo (T-loops) . In vitrTriplex-forming sequences occur in many gene promoters, for example IL2R, DNA-POL1 and MYC , which sMyc promoter and by luciferase transactivation assay in cell lines that this p53 binding leads to represssion of transcription [Recent research has highlighted the importance of quadruplexes in multiple cellular processes, including DNA replication, telomere maintenance and the binding and activity of transcription factors ,134. Comcription . It was cription .DNA binding is fundamental for the ability of p53 to act as a tumor suppressor. p53 has diverse activities in transcription, repair, recombination, replication and chromatin accessibility. These functions are realized through versatile modes of p53 interaction with DNA . p53–DNAAn important part of the complex role of p53 in cancer development is DNA binding of mutant p53. Despite the partial or complete loss of sequence specific DNA binding, mutant p53 proteins can induce or repress transcription of mutant p53-specific target genes and various mutant p53 proteins can bind to oligonucleotides mimicking non-B DNA structures . Mutant It was demonstrated that the stability of p53 is enhanced by its binding to local DNA structures. These structures can therefore remarkably change p53 dependent transactivation as was demonstrated for triplex structures in promotor regions . Besides


The gamma-aminobutyric acid (GABA)-glutamate hypothesis of schizophrenia suggests a neurotransmitter imbalance – reduced GABA and increased glutamate - which causes disruption of the modulation between inhibitory GABAergic interneurons and excitatory glutamatergic neurons. In the left superior temporal gyrus (STG) both hyperactivation and increased glutamate levels have previously been associated with auditory hallucinations in schizophrenia patients. However, the STG GABA-glutamate imbalance by simultaneously measuring GABA and glutamate in the same subjects has not previously been tested, and was therefore the aim of the present study. We hypothesized reduced GABA and increased glutamate in the patients relative to controls. Furthermore, reduced GABA and elevated glutamate in STG should be related to severity of auditory hallucinations in these patients.The current study tested 23 schizophrenia patients  and 53 healthy controls. The sample included both female and male participants above the age of 18. All patients were on medication, and they were tested at different times relative to the treatment onset. Magnetic resonance spectroscopy (MRS) was used to acquire data from voxels in the right and left superior temporal gyrus (Heschl’s gyri) with a 3T GE 750 Discovery MR scanner. PRESS and MEGA-PRESS sequences were applied to measure glutamate and GABA, respectively. Voxel tissue water was used as reference for glutamate and GABA. Scores on the Positive and Negative Syndrome Scale (PANSS) were also collected, and used to differentiate hallucinating from non-hallucinating patients.Separate 2(Group) x 2(Hemisphere) ANOVAs were estimated for GABA and glutamate. No main or interaction effects came out significant for GABA <2.7, p>0.1, η2<0.03). The analysis for glutamate resulted in a main effect of Hemisphere =24, p<0.001, η2=0.25) in which the right STG showed overall higher concentrations than the left STG. In addition, an interaction effect between Group and Hemisphere was found =5.22, p=0.03, η2=0.07). Bonferroni Post-hoc analysis showed significantly elevated glutamate levels in patients relative to controls in the right STG only (p=0.005). Furthermore, a multiple regression analysis was estimated between severity of hallucinations (PANSS P3 item) at the time of testing, and GABA and glutamate values in left and right STG. Although the overall model fit was non-significant, an approximate significant correlation was found between hallucination severity and left STG glutamate levels .The present study found higher glutamate levels in schizophrenia patients relative to healthy controls in the right STG. In spite of no overall differences in glutamate in the left STG, as initially hypothesized, glutamate levels in this region was found to predict severity of auditory hallucinations. One could speculate that the additional neuronal activity associated with auditory hallucinations elevate glutamate to ‘normal levels’ corresponding to that of healthy controls. The increased glutamate levels in the right STG seems (linearly) unrelated to auditory hallucinations and future analysis should test whether other symptoms are related to this finding. Overall, the study found only limited support for the GABA-glutamate hypothesis; In spite of increased glutamate in one of the regions, GABA was not found to be reduced in patients and was unrelated to auditory hallucinations.