There is no evidence comparing head-to-head the effects of monetary incentives to act and to abstain from acting on behaviour. We present an experiment, conducted between June and September 2012, that directly compares the effects of those two different monetary incentive schemes on eating behaviour: we evaluate incentives to eat against incentives not to eat. A large number of participants (n = 353) had bowls of sweets next to them while they watched different videos over two experimental sessions that were two days apart. Sweets eating was monitored and monetary incentives to eat or not to eat were introduced during one of the videos for participants randomly allocated to these conditions. Our results show that, while both types of incentives were effective in changing sweets-eating behaviour when they were in place, only incentives not to eat had significant carryover effects after they were removed. Those effects were still significant two days after the monetary incentives had been eliminated. We also present some additional results on personality and health-related variables that shed further light on these effects. Overall, our study shows that incentives not to eat can be more effective in producing carryover effects on behaviour in domains like the one explored here. •We experimentally compare the effects of different incentives on eating behaviour.•Subjects had bowls of sweets next to them while they watched videos in lab sessions.•Incentives to eat or not to eat were randomly allocated during one of the videos.•Both incentives were effective in changing eating behaviour when they were in place.•Only incentives not to eat had significant carryover effects after being removed. RecentlBehavioural research, however, has also uncovered a series of effects and principles that are more complex than the mere impact on the targeted behaviour. Financial incentives, in particular, have been associated to unintended effects and ‘hidden costs’  such as Studies have also started exploring the unintended ‘spillover’ effects of incentives on behaviours other than the ones directly targeted , or the Such a comparison is of key interest for health policy purposes, as in real world applications incentives will often need to be removed at some point, and both paying to act and paying to abstain from acting could have potential backfire effects once removed. For example, we could pay people not to eat fat foods for some time and then remove the incentive. This could result in reduced calorie intake if the intervention helps people build up healthier eating habits, but, based on what we know about motivation, it could also potentially increase the consumption of calorie dense foods if it undermines people's intrinsic motivation to control their eating in the absence of incentives. An alternative option would be, for instance, to pay people to eat low-fat food items and then remove the incentive. The precise results of these alternative interventions would depend on many factors, but in order to be able to compare directly the merits of incentives to act and to abstain from acting, we need a clean comparison using exactly the same target behaviour and environment.To the best of our knowledge, this paper presents the first controlled head-to-head study of the effects of monetary incentives to act and to abstain from acting on behaviour. We focus on eating behaviour, which is an issue of significant health policy relevance, and which has already received attention in previous studies of incentives . In partWe conducted a laboratory experiment in which participants had bowls of sweets next to them while they watched different videos over two sessions set two days apart. During the first session we introduced monetary incentives to eat or not to eat sweets from the bowl and monitored how that affected eating behaviour while the incentives were in place, after they were removed on the same day, and two days after they were removed.The rest of the paper is organised as follows: Section 22.1The general methodology used in our experiment was to leave bowls of sweets (Jelly Beans) next to the participants while they watched different videos on individual computer screens during two experimental sessions set two days apart. Sweets eating was monitored throughout the two sessions, and monetary incentives to eat or not to eat sweets were introduced during one of the videos in the first session to observe their effects on eating behaviour.1)“Control” condition: Participants could keep on eating sweets as they liked during the next video.2)“Eat” condition: Participants received £3 at the end of the session if they ate at least 10 Jelly Beans during the next video.3)“Don't Eat” condition: Participants received £3 if they did not eat any Jelly Beans during the next video.Each participant watched a total of four different videos individually, with a bowl of Jelly Beans next to them . Three of the videos were in the first experimental session, while the fourth video was in the second session. During the first video, we let participants take sweets from the bowl and eat them as they pleased. We explicitly told people that they could eat sweets from the bowl as they liked. Before starting the second video, we implemented one of the three following conditions and informed participants about it:Before the third and fourth videos, participants  were informed again that they could eat sweets as they liked during the videos. Eat condition to eat too many sweets, or a number of sweets that was too low for 10 min. In the other videos, 10 min provided more time to obtain good observations. All the videos were selected to be mildly boring, so that they tended to encourage sweets eating , immediately after they were removed (during Video 3), and also two days later (during Video 4). An obvious complication with the effects observed immediately after the incentives were removed is that the different amounts of sweets eaten during the video with incentives can affect subjects' appetite in the next video. Nevertheless, as the results will show, it is still possible to extract interesting insights from eating behaviour during Video 3. In addition, sweets-eating behaviour during Video 4 provides a clean test of the carryover effects of the monetary incentives used.1)Big Five Inventory , to meas2)Health and Taste Attitudes Questionnaire , which m3)A question about the frequency of sweets intake.4)A question about the desire to lose weight.5)Two questions about height and weight to calculate the Body Mass Index of the participants.In the first experimental session, before starting with the first video, participants responded to various questionnaires intended to elicit additional personality and health-related information. The questionnaires included the following elements:2.2Eat or Don't Eat conditions. Randomization resulted in 133 subjects in the Eat condition, 112 in the Don't Eat condition and 108 in the Control condition. Attrition from the first day of the experiment to the second was 11%, so that the number of participants in the second session was 314.A total of 353 subjects participated in 35 experimental sessions. On their arrival to the lab, subjects were identified anonymously by using an ID code assigned by the online recruitment system (SONA), and they were asked to read an informed consent form and sign it if they agreed to carry on with the experiment. All subjects who came to the lab gave their consent and decided to take part in the experiment. We then asked people to draw a number at random to determine their cubicle in the lab, and participants in each session were randomly assigned to either the Control group, or the The experimental protocol was approved by the LSE Research Ethics Committee, and by the Board of Directors of the Centre for the Study of Incentives in Health (CSIH), which funded the experiment. All experimental sessions were run at the LSE Behavioural Research Lab (BRL) between June and September 2012. Subjects were recruited from the volunteers in the BRL subject pool, which comprises about 5000 subjects, mostly current and former students of the University of London. There were no other eligibility or exclusion criteria to select participants. In the invitation email, subjects were not informed about the exact nature of the experiment that would be conducted. They were simply told in advance that the experiment would require coming to the lab for two separate sessions on two different days of the week (Monday and Wednesday); that each session would last about an hour; that they would receive a fixed amount of £20 for their participation in both sessions (in addition to any amount won during the sessions); and that they would have the opportunity to get an extra payment related to their tasks. Exact experimental instructions and any other materials used in the experiment are available from the authors upon request.3Video 4-1). Finally, Eat condition showed clearly the highest number of sweets eaten, followed by the Control condition and then the Don't Eat condition. All these differences are statistically significant at the 1% level: the monetary incentives to eat and not to eat were both effective in modifying behaviour in the expected direction while they were in place.As Don't Eat condition shows the lowest number of Jelly Beans eaten, followed quite closely by the Eat condition and then the Control condition. The difference between Don't Eat and Control  is statistically significant, but the difference between Eat and Control (or between Eat and Don't Eat) is not. The most noteworthy result in Video 3 is that people ate significantly less sweets in the Don't Eat condition than in the Control condition. This result is the opposite of what would be expected on the grounds of the appetite state of the subjects: given that people in the Don't Eat condition ate fewer sweets during Video 2, they should have undermined their appetite less. Therefore, one would expect that subjects in the Don't Eat condition would eat more, not less, during the next video. Thus, this result shows a clear carryover effect of the incentives not to eat on subsequent behaviour in the same experimental session.The most interesting results come from Video 3 and especially Video 4. In Video 3, right after the incentives were removed, the Don't Eat condition than in the Control and the Eat conditions. The difference is statistically significant at 5% when comparing with the Eat condition and at 10% when comparing with the Control condition. This pattern is also clearly observed in the difference between the sweets eaten in Video 4 and in Video 1. While subjects in the Control and the Eat conditions ate more sweets in Video 4 than in Video 1, the subjects in the Don't Eat condition ate slightly less. In this case, the pattern is significant at 1% when comparing with the Eat condition and at 5% when comparing with the Control condition. This result shows a clear carryover effect of the incentives not to eat, which affected sweets-eating behaviour two days after the incentives were removed. The result is particularly striking given that participants were only exposed to the monetary incentives for 5 min two days before. On the contrary, the behaviour of people in the Eat condition was indistinguishable from the Control condition two days later.In Video 4, the results show that people ate considerably fewer sweets two days later in the Models 1–5), we stack the four videos together and treat the data as a panel with four time observations per subject. We use pooled panel (population-averaged) Ordinary Least Squares (OLS) regressions with robust standard errors clustered at the subject level to account for individual heterogeneity, and for the fact that error terms can be potentially correlated across videos for the same subject. The dependent variable in all the pooled OLS regressions is the number of Jelly Beans eaten during the different videos, while the explanatory variables in the various specifications include: dummies for the Eat and Don't Eat conditions; dummies for Video2, Video3, and Video4; treatment–video interaction terms ; scores on the five personality dimensions ; scores for the two main categories of the Health and Taste Attitudes Questionnaire (HTAQ) ; self-reported frequency of sweets intake (SweetsIntake); self-declared intention to lose weight (LoseWeight); and calculated scores for the Body Mass Index (BMI).To complement these patterns, Model 6) uses the number of Jelly Beans eaten during Video 1 as the dependent variable, and it is simply meant to give a sense of how the personality and health-related variables affected sweets eating in the absence of any effects of monetary incentives. The explanatory variables are the same as above, with the exception of the treatment dummies (and of course of the video dummies and interaction terms). Estimates in this case are obtained using a standard OLS regression adjusting the variance-covariance matrix for possible heteroskedasticity.The final regression (Model 1) confirm significant and negative effects of the Don't Eat interaction terms: subjects assigned to the incentives not to eat, ate significantly fewer Jelly Beans than participants in the control group, not only during Video 2 but also in Videos 3 and 4. On the other hand, there are no significant interaction effects for the subjects in the Eat condition, apart from the obvious one for Video 2. Finally, while the main treatment dummies are not significant, the effects of the time dummies are as expected: compared to the first video, subjects across all conditions tended to eat fewer Jelly Beans in Video 2 (which was only 5 min long) and in Video 3, and more in Video 4 (two days later).The results of the baseline pooled panel OLS regression (Model 2), the estimates show significant and positive effects of Agreeableness and Neuroticism, and a significant negative effect for Openness. Importantly, the previously described effects of the Don't Eat interaction terms are fully confirmed: subjects previously paid not to eat continued eating significantly less in Videos 3 and 4.When the scores on the five personality dimensions are added as controls in the pooled OLS regression (Model 3), self-reported frequency of sweets intake, self-declared intention to lose weight, and individual BMI (Model 4). Curiously, self-reported concern for the Healthiness of food had a positive effect on sweets eating. Frequency of sweets intake and BMI had also positive effects, and desire to lose weight a negative one: subjects with higher BMI and who usually eat more sweets also ate more sweets in the experiment, while people who had a desire to lose weight ate less.The effects of these interaction terms are also robust to using as alternative controls the variables for the HTAQ dimensions (Model 5): subjects assigned to the Don't Eat condition continue eating significantly less than in the control group in Videos 3 and 4 .Results are qualitatively unaltered when all control variables are introduced simultaneously in the pooled OLS regression (Model 6) show that the main personality influence on sweets eating was Neuroticism, which might suggest that people who tended to get more anxious during the videos ate more sweets. Also Openness seems to have had some influence on eating behaviour, with more open people eating fewer sweets, but the effect is only significant at the 10% level. The regression also confirms the positive effects described above for the self-reported concern for the Healthiness of food, frequency of sweets intake, and BMI, and the negative effect of desire to lose weight. Also these results are confirmed when the OLS regression is replicated excluding the three subjects in the sample who suffered from diabetes .It is important to note that the results of the pooled OLS regressions reported above are broadly confirmed when using random effects panel models; when all variables are standardized to minimize multicollinearity issues potentially arising from the interaction terms; and when regressions are replicated excluding the three subjects in the sample who suffered from diabetes . The results of the final OLS regression  and below (Model 1b) the median consumption, are reported in Model 1). The findings are of key interest as they confirm that incentives not to eat primarily had a lasting effect in the cases where a change in the target behaviour is envisaged the most, namely the ‘over-eaters’ who ate sweets above the median level.In Eat condition. Furthermore, the carryover effects of the incentives not to eat seem limited to subjects who in Video 1 ate sweets above the median level. These subjects (but not the ones who ate sweets below the median level) continued eating significantly fewer sweets in Videos 3 and 4.First, as in the whole sample, both incentives to eat and not to eat were effective in changing the eating behaviour of subjects who in Video 1 ate sweets below the median level. For subjects who in Video 1 already ate sweets above the median level, only incentives not to eat were effective in changing eating behaviour. Second, like in the whole sample, none of the sub-groups exhibited significant carryover effects of monetary incentives in the 4We have investigated the effects of two different types of monetary incentives on sweets-eating behaviour: incentives to eat and incentives not to eat. These two incentives schemes have been widely used in research and policy but they have never been directly compared head-to-head using the same target behaviour and the same environment. We found that both types of incentives were effective in modifying behaviour as expected while they were in place, but only incentives not to eat had significant carryover effects on eating. People who were paid not to eat sweets for only 5 min, showed reduced sweets eating two days after this incentive was removed.One interpretation of these results is that paying people not to eat helped them to exert and build up their self-control. Then this improved ability to exert self-control and refrain from engaging in a behaviour that may be undesirable carried over to subsequent situations where incentives were absent. On the contrary, paying people to eat was not so successful in building-up a habit that carried over to future contexts.An alternative explanation may be that paying people not to eat sweets simply primed them with the notion that not eating sweets is something good and influenced subsequent behaviour, while incentives to eat failed to successfully prime people with the idea that eating sweets is good. This is consistent with the well documented ‘bad is stronger than good’ effect, which is the notion that negative messages are more salient and easier to retain than positive ones . SimilarWe also conducted a split-sample analysis to look at whether the effects of the different types of incentives induced consumption in the ‘under-eaters’ and reduced consumption in the ‘over-eaters’, for whom a behavioural change can be desired the most in practical health-policy applications. The split-sample analysis reassuringly confirms that incentives not to eat primarily had a lasting effect on the subjects who ate sweets above the median level. This strengthens the policy implications, as it indicates that the effects of monetary incentives to improve poor behaviour do not appear to have adverse spillover effects on the subjects who are not directly targeted by the policy.While our results pertain to a specific context and should not be interpreted as general evidence against the carryover effects of incentives to act, they suggest that incentives to abstain from acting are likely to have more powerful and long-lasting effects on behaviour, at least in circumstances that have similar features to our set-up. What characterizes our set-up is mainly an ambivalent health situation where subjects can choose to act in a way that is pleasurable and tempting, but potentially harmful or unwanted at a deeper level. The same pattern applies to a large number of health behaviours of high policy relevance, like alcohol drinking and unsafe sex, which makes our results relevant to these applied areas.It is worth noting, however, that another relevant category of health-related situations is that in which people can choose to act in a way that is beneficial or desirable, but also costly and effortful, so that they are tempted to abstain from acting. Such situations could include behaviours like exercising, eating vegetables, taking a screening test or vaccination, or going to see the doctor. These situations are essentially the opposite of our set-up in the sense that the behaviour that requires self-control is to act instead of to abstain from acting. In these cases, incentives to act and not to act are likely to have different consequences.Based on our results, we would expect that the patterns produced by incentives to act and to abstain from acting are likely to reverse, so that incentives to act may have stronger carryover effects than incentives to refrain from acting. Further research along these lines is needed to fully uncover the effects of different types of incentives in these different circumstances.


We propose a new method for measuring the degree of eyestrain on 3D stereoscopic displays using a glasses-type of eye tracking device. Our study is novel in the following four ways: first, the circular area where a user's gaze position exists is defined based on the calculated gaze position and gaze estimation error. Within this circular area, the position where edge strength is maximized can be detected, and we determine this position as the gaze position that has a higher probability of being the correct one. Based on this gaze point, the eye foveation model is defined. Second, we quantitatively evaluate the correlation between the degree of eyestrain and the causal factors of visual fatigue, such as the degree of change of stereoscopic disparity (CSD), stereoscopic disparity (SD), frame cancellation effect (FCE), and edge component (EC) of the 3D stereoscopic display using the eye foveation model. Third, by comparing the eyestrain in conventional 3D video and experimental 3D sample video, we analyze the characteristics of eyestrain according to various factors and types of 3D video. Fourth, by comparing the eyestrain with or without the compensation of eye saccades movement in 3D video, we analyze the characteristics of eyestrain according to the types of eye movements in 3D video. Experimental results show that the degree of CSD causes more eyestrain than other factors. Currently available 3D stereoscopic displays require the user to wear anaglyph, passive or active shutter glasses, although some 3D stereoscopic displays that are not based on glasses have been commercialized recently. In spite of the maturity of 3D eyeglass displays, eyestrain from viewing them is caused by various factors and remains a problem that must be overcome. To solve this problem, we require a method that can measure the degree of eyestrain accurately and objectively.Previous research can be categorized into subjective  and objeIn previous research, eye refraction or pupil responses, including accommodation, were measured on participants viewing 3D stereoscopic displays –8. Althoet al. applies an eye foveation model that considers the user's gaze position and gaze error in order to measure the eyestrain according to hue variance, edge, and motion information in 2D displays [et al. define the circle region where the user's gaze position exists by considering the gaze estimation error; they do not determine the gaze position that has a higher probability of being the correct one inside the circle region. In addition, they do not measure the eyestrain according to various factors in the 3D stereoscopic display.In subsequent research in this field, researchers have attempted to measure eyestrain based on eye BR or the speed of pupil accommodation using a camera vision system –11. Theset al. reviewed previous techniques for gaze-contingent displays considering foveation for peripheral level-of-detail management, 3D visualization, and stereoscopic imaging. However, a specific gaze detection algorithm has not been used or provided in previous studies [In a previous research , Van der studies ,13. In aTherefore, we propose an innovative method to analyze eyestrain on an anaglyph (red-green glasses) stereoscopic display considering an eye foveation model that is based on the viewer's gaze position and gaze error, and on the gray edge information.Although the stereoscopic display that uses passive or active shutter glasses is more advanced, we chose the anaglyph stereoscopic display to measure eyestrain for this research because of the following advantages: first, the passive or active shutter-based method cannot be used on conventional 2D displays because the shutter requires additional equipment on the display. However, the anaglyph-based method can be used on conventional 2D displays without the use of additional equipment. Second, anaglyph glasses are inexpensive and lighter than passive or active shutter-based glasses.By wearing an eye-movement capturing device that resembles eyeglasses and attaching four near-infrared (NIR) illuminators to the monitor's corners, we can easily measure the eye BR and gaze position of a user. Gaze estimation that considers the human field of vision (FOV) and average gaze error is used to measure more accurately and reliably a user's eyestrain at specific gaze positions within the entire 3D display area. In addition, the circular area where the user's gaze position exists is defined based on the calculated gaze position and gaze estimation error. Within this circular area, one position where edge strength is maximized can be detected, and we determine this position as the gaze position that has a higher probability of being the correct one. Based on this gaze point, the eye foveation model is defined. We measure the correlation between the degree of eyestrain and the causal factors of eyestrain, such as the degree of change of stereoscopic disparity (CSD), stereoscopic disparity (SD), frame cancellation effect (FCE), and edge component (EC) on a 3D display. By comparing the eyestrain in a conventional 3D video and an experimental 3D sample video, we analyze the characteristics of eyestrain according to various factors and the types of 3D video. In addition, by comparing the eyestrain with or without the compensation of eye saccades movement in a 3D video, we analyze the characteristics of eyestrain according to the types of eye movements in a 3D video. Our gaze tracking system does not measure gaze in the 3D space , but measures gaze in the 2D space (X and Y position on a monitor).This paper is organized as follows: in Section 2, we explain the proposed devices and algorithms. In Section 3, the factors of visual fatigue caused by the 3D display are shown. In Section 4, we provide the experimental setup and results. In Section 5, the discussions of the experimental results are shown. Conclusions and future work are presented in Section 6.2.2.1.Our proposed device for capturing eye-movement includes a small web-camera with a zoom lens and the interface of a universal serial bus . The camTo acquire clear eye images and calculate the user's gaze position, four NIR illuminators, with a wavelength of 850 nm that does not dazzle the user, are attached to the four corners of the monitor . To capt2.2.With a captured eye image, the gaze tracking algorithm works as follows. To extract the center of the pupil in the eye image, a circular edge detection (CED) algorithm is employed. The algorithm approximates the pupil position where the difference in gray levels between two adjacent circular templates is maximized . BecauseWith the detected pupil center and the four SR positions, gaze position on a monitor is calculated using a geometric transform method ,16. In g2.3.Previous research shows that the sensitivity to contrasts in human vision decreases with an increase in the distance from the user's gaze position –14,17,18The human foveation model can be calculated based on ,17,18, aFurthermore, in order to obtain a more accurate visual sensitivity foveation model, the average gaze error (1.12°) of our gaze tracking method is considered in the mask of the wavelet domain, as shown in The Sobel mask is applied to left and right images, respectively. The left and right images are the images separated from the original 3D stereoscopic (red-green) images of In our research, a user wears the red-green glasses shown in xf =  is a user's gaze position and x = T (pixels) is any point in the image. In addition, λ is the wavelet decomposition level and θ shows the LL, LH, HL, and HH sub-bands of the wavelet transform. Here, LL and HH are the low and high frequency components both in the horizontal and vertical directions, respectively. LH and HL are the low frequency component in one direction and the high frequency component in the other direction among horizontal and vertical directions, respectively. Bλ,θ is the set of wavelet coefficient positions in sub-band  [N is the width of an image, and v is the viewing distance measured in image width from the eye to the image plane [Nv is the calculated Z distance between a user's eye and the image plane. If we assume that e is the gaze tracking error in degrees, the radius of the circle region (where user's gaze position exists) determined by the gaze tracking error can be Nυ tan e in the image plane [λ‖x − xfλ,θ‖2 < NV tan e of x) belongs to the circle region whose radius is Nv tan e. Further, MSMi(x) is the magnitude of the filter response of the Sobel filter at the position (x) and i (0∼3) because we use four directional Sobel masks . TMSMi(x) is the total magnitude of the filter response of the Sobel filter at the position (x); we find the correct gaze position of the user as xcfλ,θ using xcfλ,θ instead of xfλ,θ in Consequently, the original equation for the distance from the foveation point of d  :(2)dλ,θge plane ,18. Therge plane . ConsequTo apply the weighting mask of 2.4.et al. show that blinking frequency increases during prolonged work on visual display terminals [In previous research , an increrminals . TherefoUsing the captured eye image shown in 3.The image quality of conventional 3D stereoscopic displays can be influenced by various factors, such as camera configurations, image compression, and display . The facBecause it has been reported that various factors exist that can damage the 3D perception and cause user eyestrain, it is extremely difficult to consider all causes. Among such causes, the excessive SD is regarded as one of the major factors on eyestrain. However, we use commercial 3D video for experiments, and the level of SD in each image is usually adjusted by post-processing such as to provide eye comfort to the audience for the commercial 3D video. However, in most cases, the CSD in successive images cannot be adjusted because the CSD is also determined by scene changes based on the plot of the video. Therefore, in this study, we measured the change in eyestrain based on the following three 3D factors: degree of CSD, degree of SD, and degree of FCE. The FCE is one of the typical factors that belong to the first category of spatial distortions. As the third category, the excessive SD is regarded as one of the major factors on eyestrain. Additionally, the degree of EC is calculated as a 2D factor to measure the change in eyestrain according to the edge degree both in left and right images of 3D display. Detailed explanations for obtaining ECs are provided in Section 3.3.3.1.SD represents the positional difference between the left and right images in a 3D stereoscopic display, and it affects the user's depth perception. However, an excessive CSD of images can result in eyestrain. Therefore, we measured the relationship between eye BR and the CSD in the image region, as defined by the foveation model shown in In the stereoscopic (red-green) image of If the gray value in the left image is different from that in the right image even with the same object, these differences in gray level can cause an incorrect disparity calculation. Therefore, brightness normalization is performed on the left and right images by adjusting the mean gray values of the left and right images to be the same, which can reduce the disparity caused by the gray level differences of corresponding pixels in the left and right images. Then, SD is calculated using the stereo matching algorithm from the open source computer vision (OpenCV) library , as show3.2.In conventional 3D stereoscopic displays, the difference of occlusion of an object in left and right images can occur specifically in the left or right boundary areas of the display. We compute the FCE as /2—the gaze position of the X-axis. If the FCE is negative number, we change it to positive by obtaining the absolute value of the FCE. For example, in the case of gaze positions  and , the revised FCEs are 620 (1280/2–20) and 630 (1270–1280/2), respectively.3.3.In previous research , the relIn our research, the BR is measured as a sum in the time window of 60 s; this time window is moved by an overlap of 50 s. Therefore, all factors, including CSD, SD, FCE, and EC are measured as a sum in the same time window of 60 s, respectively; this time window is moved by an overlap of 50 s such that we can obtain the value of each factor synchronized with the BR. For example, in the images of 80 s, we can obtain three pairs ((80 − 60)/10 + 1) of the values , , , and , respectively. Because the total video length used in our experiment is 25 min 30 s (1530 s), the number of pairs of values , , , and ) is 148 ((1530 − 60)/10 + 1), respectively. With these pairs, we measure correlations similar to In addition, our system does not adapt the video material in real time. Successive images of a user's eye are acquired by synchronizing with the 3D video images in real time. Then, the gaze position, BR, and the corresponding values of CSD, SD, FCE, and EC are obtained through an off-line experiment.4.The participants in this experiment wore the proposed glasses-type device with red-green glasses shown in Based on the relationship observed between the eye BR with the function of time on task and the prolonged works on visual display terminals ,26, we mAs listed in et al. measured the relationship between eyestrain and the increase of SD on a sample video by deliberately increasing the level of SD of 3D contents. However, the goal of our research is to measure eyestrain by various factors on conventional 3D video, which is used in the real world, instead of the experimental sample video. Consequently, we used the commercial 3D video for experiments and the level of SD was usually adjusted by post-processing such as to provide eye comfort to the audience for the commercial 3D video. Thus, the experimental results showed that the SD is not related to eyestrain in commercial 3D video in our experiments. In addition, the consequent level of eyestrain in our experiment was low, which reduces the R2 value. And, we measured the short-term  change of eye BR according to the change of CSD, SD, FCE, and EC during the short time of 1 min, respectively, whereas the long-term change of eye BR was usually measured during the long time in previous research [2 value, consequently.In previous research , Cho et research . Therefo2 indicates the confidence that we have in predictions using the regression lines. If the data is better (more reliably) fitted by the regression, the consequent R2 value increases [2 values for the 3D factors and the 2D factor by our method, respectively.The average gradient values for the three 3D factors and the single 2D factor by our method are 0.3084, −0.0506, 0.2440, and −0.1948, respectively. These gradient values were calculated from the fitted line (regression line) on the factor data and the BR, as shown in 2 of CSD by our method are larger than those by a previous method [2 of the FCE values by our method are same to those by a previous method [In s method  and in ts method  and usins method  and thosk factorial design that is used for measuring the effect of each factor and the interaction effect on the system performance [k factorial design with these two factors. First, we measure the maximum and minimum SD from the test images. Based on the medium value of the maximum and minimum SDs, we divide the images into two types, Large and Small, as listed in For the next analysis, we perform the method of 2formance ,32. Becay) can be regressed based on the nonlinear regression model in Then, the BR , , , and  in the cases of , , , and , respectively. From them, To perform the method of 2y1–y4 are 0.4842, 0.5871, 0.4703, and 0.4098, respectively, based on y1–y4 and q0, Aq, Bq, and ABq are calculated as 0.4878, 0.0106, −0.0478, and −0.0410, respectively. The q0, Aq, Bq, and ABq represent the average value of y1–y4, the effect of SD, the effect of CSD, and the interaction effect of SD and CSD, respectively. The sum of squares total (SST) is obtained to calculate the proportion of the effect for each factor [In Equation (7)h factor :(8)SST=SSA), sum of squared factor B (SSB), and sum of squared factor AB (SSAB) are 0.0004, 0.0091, and 0.0067, respectively. The effect ratios of each factor are calculated by 100 × SSA/SST (%), 100 × SSB/SST (%), and 100 × SSAB/SST (%), respectively. Consequently, they are 2.76%, 56.19%, and 41.04%, respectively. Based on these values, we can reach the following conclusions:-The effect of factor B (CSD) is approximately 20.4 (56.19/2.76) times greater than that of factor A (SD).-The effect of factor B (CSD) is approximately 1.4 (56.19/41.04) times greater than that of the interaction effect of factors A and B.-The interaction effect of factors A and B is approximately 14.9 (41.04/2.76) times greater than that of factor A (SD).Using k factorial design. In addition, we provide descriptive statistics based on effect size [d is calculated as the difference value between two means divided by a standard deviation for the data. Based on the previous research [d value as small, medium, and large, respectively. In [d, an effect size of 0.2 to 0.3 might be a small effect. A value of around 0.5 might be a medium effect, and that of 0.8 to infinity (the d might be larger than one) might be a large effect.From these conclusions, we can confirm that the CSD has more of an effect on eyestrain than the SD in terms of the method of 2ect size . In statect size . Based oect size , we showresearch , we defively. In , for Cohd between two factors that cause eyestrain with the results of our method from d between the average correlation coefficient of CSD and that of SD is 1.5145, which is closer to 0.8 (large effect) than to 0.2  or to 0.5 (medium effect). Therefore, we can confirm that there exists a difference between the average correlation coefficient of CSD and that of SD as a large effect size. Another example is that Cohen's d between the average gradient of CSD and that of FCE is 0.3345, which closer to 0.2 than to 0.5 or to 0.8. Thus, we can confirm that there exists a difference between the average gradient of CSD and that of FCE as a small effect size.As listed in Here, the average correlation coefficient and the average gradient usually show a degree of correlation between the eyestrain measured using the eye BR and the factor. By referring to the results of Summer in Heidelberg and we obtained permission from the video copyright owner [For comparison, we measured the relationship between the BR and various factors through additional experiments with the experimental sample video. The name of sample video is ht owner .A total of fourteen people participated in the experiment, and each person watched the 3D video using active shutter glasses for 30 min. The user's eye images were captured at a speed of about 70 fps using a remote (lab-made) gaze tracking system as shown in As listed in d between two factors that cause eyestrain with the results of our method as listed in In For the next experiment, we include the subjective evaluations of our system as shown in not at all, and ten corresponds to yes, very much) [For the subjective evaluation, the following six questions were answered using a 10-point scale, where one corresponds to ry much) .I have difficulties seeing.I have a strange feeling around the eyes.My eyes feel tired.I feel numb.I feel dizzy looking at the screen.I have a headache.Subjective tests were performed before and after watching the 3D video. All the relationships between BR and the 3D/2D factors from p-value is 0.0091 and it is less than 0.01, we can confirm that the subjective eyestrain after watching the 3D video is significantly higher than before watching the 3D video, with a confidence level of 99% (0.01) [p-value is less than the confidence level, the null-hypothesis is rejected, which indicates that there exists a difference between the subjective eyestrain before and after watching the 3D video. In addition, we include the subjective evaluations before and after watching the 3D video of As shown in % (0.01) . We form% (0.01) , if the p-value is 0.0001 and it is less than 0.01, we can confirm that the subjective eyestrain after watching the 3D video is significantly higher than before watching the 3D video, with a confidence level of 99% (0.01) [As shown in % (0.01) .In our experiment, the successive images of the user's eye are acquired by synchronizing with the 3D video images at a speed of 15 fps. From that, 15 gaze points are obtained per 1 s. We regard the position calculated based on pupil center and four SRs at every eye frame as the user's gaze point. Here, we do not consider eye jittering during fixations because of drift, tremor and involuntary micro-saccades , and sacIn order to consider the eye jittering and saccadic movement, eye images that are captured at faster speed are necessary. For that, a high-speed camera is required for the eye capturing device, which increases the size and weight of the eye capturing device shown in 2 of FCE values by our method are same to those by a previous method [With the eye images of 15 fps , we redus method  and thos5.Our eye capturing device and method can be applied easily to other stereoscopic viewing modalities. et al.'s method can be used for 3D graphic content such as virtual reality and game content whose rendering volume can be controlled by their algorithm. However, our system is used for actual video images captured through a camera where it is difficult to control rendering the part of the viewing volume.Because the implementation algorithm and code of  are not In our research, the proposed system does not adapt the video material in real time. The successive images of the user's eye are acquired by synchronizing with the 3D video images in real time. Then, the gaze position, BR, and the corresponding values of CSD, SD, FCE, and EC are obtained through an off-line experiment.In future work, our system can be used as a real-time system that can control various factors  according to the user's eyestrain as follows. If the eyestrain is perceived by our system based on the increased number of BR, our system can send the signal to 3D TV where the CSD should be reduced. Because the FCE is usually caused by the conflict of SD and the occlusion by the monitor boundary , if the Because the BR and all the factors  were measured in the time window of 60 s and this time window was moved by an overlap of 50 s, our system can send the eyestrain signal and request adaptation of the 3D video to the 3D TV system every 10 s when used in a real-time system.The relationships between the factors  and eyestrain measured by BR are calculated with the data which are obtained while each user is watching the 3D video. Because the subjective test cannot be performed while watching the 3D video (it can be done only before or after watching video), these relationships cannot be measured by the subjective test. Therefore, we performed the subjective test for measuring eyestrain before and after watching the 3D video. From the Therefore, the first conclusion of our research is that with the commercial 3D video, the degree of correlation between the eyestrain and the CSD is highest. Those of the FCE, EC, and SD are the second, third, and fourth highest correlations, respectively. That is because the level of SD in each image is usually adjusted by post-processing such as to provide eye comfort to the audience for the commercial 3D video. However, in most cases, the CSD in successive images cannot be adjusted because the CSD is also determined by scene changes based on the plot of the video. The second conclusion is that with the experimental 3D sample video (where the level of SD of 3D contents is deliberately increased), the degree of correlation between the eyestrain and the SD is highest. Those of the CSD, EC, and FCE are the second, third, and fourth highest correlations, respectively. The third conclusion is that the subjective eyestrain after watching the 3D video  is higher than that before watching the 3D video with a confidence level of 99% (0.01).et al. applied an eye foveation model that considers the user's gaze position and gaze error when measuring the eyestrain according to variance of hue, edge, and motion information in 2D displays [Lee displays . Althougdisplays  of Figurdisplays  as showndisplays . These adisplays .Other differences, improvements and contributions of our research compared to previous one  are as fHowever, we evaluate the correlation between the degree of eyestrain and the causal factors of visual fatigue, such as the degree of CSD, SD, FCE, and EC in the 3D stereoscopic display. In addition, by comparing the eyestrain in conventional 3D video and experimental 3D sample video, we analyze the characteristics of eyestrain according to various factors and types of 3D video. And, by comparing the eyestrain with or without the compensation of eye saccades movement in 3D video, we analyze the characteristics of eyestrain according to the types of eye movements in 3D video. In addition, we show the applicability of our method to the 3D system based on the polarized glasses and active shutter glasses as shown in k factorial design, descriptive statistics based on effect size, and subjective evaluation with T-test. However, we did them as shown in The last difference, improvement and contribution of our research compared to previous one  are as fet al. presented a new computer interface that combines gaze tracking with brainwave measurements in an integrated head-mounted device for the navigation in 3D world. We just adopt the method of attaching four NIR illuminators at the four corners of monitor, locating the four corneal SRs in the eye image, and Kappa calibration from [In previous research , Bang etion from  as shownion from , and it 6.In this paper, we proposed a new method for measuring the degree of eyestrain caused by viewing 3D stereoscopic display based on eye BR, considering both the viewer's gaze position and edge information. To quantify the eyestrain on the viewer of a 3D display, we used a pupil detection device that consists of eyeglasses and a web camera. We measured the viewer's BR to determine the extent of eyestrain. Then, we evaluated the correlation between the extent of eyestrain and the three 3D causal factors  and one 2D factor (EC) of 3D display. Our experimental results showed that the CSD has a significantly larger effect on eyestrain than the other factors. In addition, by comparing the eyestrain in conventional 3D video and experimental 3D sample video, we analyzed the characteristics of eyestrain according to various factors and the types of 3D video. Further, by comparing the eyestrain with or without the compensation of eye saccades movement in 3D video, we analyzed the characteristics of eyestrain according to the types of eye movements in 3D video.Although we measured the eyestrain using the foveation model considering the fovea centralis region, the peripheral vision can also be a factor that causes eyestrain in the case of motion and in real-time applications, if not controlled. Therefore, this is the limitation of our research and we would leave this issue open what would happen if a global model is used instead of the strain in the “fovea centralis” region. For future work, we would conduct research to determine whether our system can be used as a real-time system that can control various factors , according to user's eyestrain. In addition, we plan to combine the measurements of eye BR with brainwaves or other physiological signals for a more accurate evaluation of eyestrain.


In-vivo quantification of cardiac perfusion is of great research and clinical value. The dual-bolus strategy is universally used in clinical protocols but has known limitations. The dual-saturation acquisition strategy has been proposed as a more accurate alternative, but has not been validated across the wide range of perfusion rates encountered clinically. Dual-saturation acquisition also lacks a clinically-applicable procedure for optimizing parameter selection. Here we present a comprehensive validation study of dual-saturation strategy in vitro and in vivo.The impact of saturation time and profile ordering in acquisitions was systematically analyzed in a phantom consisting of 15 tubes containing different concentrations of contrast agent. In-vivo experiments in healthy pigs were conducted to evaluate the effect of R2* on the definition of the arterial input function (AIF) and to evaluate the relationship between R2* and R1 variations during first-pass of the contrast agent. Quantification by dual-saturation perfusion was compared with the reference-standard dual-bolus strategy in 11 pigs with different grades of myocardial perfusion.2 = 0.92, mean difference of 0.1 ml/min/g; myocardial perfusion ranges between 0.18 and 3.93 ml/min/g).Adequate flow estimation by the dual-saturation strategy is achieved with myocardial tissue saturation times around 100 ms , with the lowest echo time, and following a signal model for contrast conversion that takes into account the residual R2* effect and profile ordering. There was a good correlation and agreement between myocardial perfusion quantitation by dual-saturation and dual-bolus techniques (RThe dual-saturation acquisition strategy produces accurate estimates of absolute myocardial perfusion in vivo. The procedure presented here can be applied with minimal interference in standard clinical procedures. Myocardial perfusion is affected by several pathological situations, and the degree of myocardial hypo-perfusion correlates with clinical prognosis -3. ThereSemi-quantitative methods based on the contrast enhancement ratio ,6 or upsAn alternative approach is dual-saturation acquisition, in which AIF and myocardial tissue information are collected after injection of a single bolus of undiluted contrast agent . In thisin-vitro (phantom) and in-vivo  experiments , echo time (TE), contrast injection rate, and image profile ordering on estimates of AIF and myocardial contrast uptake in a set of s Figure . Signal All imaging experiments were performed on a 3T-TX Achieva platform  equipped with a 32-channel phased-array cardiac coil.The phantom consisted of 15 plastic 50 ml falcon tubes, each filled with distilled water and a specific concentration of contrast agent gadolinium diethylenetriamine pentaacetic acid , ranging from 0 to 22.5 mM. Three imaging experiments were performed with the same phantom to evaluate the effect of contrast concentration on R1 values and saturation times, and the influence of profile ordering on estimated final contrast concentration.Gold-standard T1 values of each tube were estimated using a look-locker inversion recovery acquisition with 147 inversion times ranging from 6.06-5886.06 ms, with a time interval of 40 ms. A new inversion pulse was applied every 10 s to avoid signal saturation. To reduce the influence of excitation readout at different inversion times, an excitation flip angle of 5° was applied during the TFE shot . For tubPD) image was acquired without saturation pulse to normalize the signal intensity of each contrast concentration (S/SPD). All images were acquired by low-high profile ordering to enable short TS and to avoid read-out effects on signal intensity.The second experiment was designed to evaluate the signal behavior at different saturation times and contrast concentrations. All parameters except TS were held constant , and ten images of each tube were acquired at TS = 10, 20, 30, 40, 50, 60, 70, 80, 90 and 200 ms. An additional proton density  with half-scan factors of 65% and linear and reverse-linear profile ordering. For each tube, we also computed signal intensity with the signal model described in the Experimental procedures were performed in castrated-male Large-White pigs. The study protocol was approved by the Institutional Animal Research Committee and conducted in accordance with recommendations of the Guide for the Care and Use of Laboratory Animals.Pigs were sedated by intramuscular injection of ketamine (20 mg/kg), xylazine (2 mg/kg), and midazolam (0.5 mg/kg). A marginal vein in the ear was cannulated for peripheral intravenous access. Sedation was maintained by a continuous intravenous infusion of ketamine (2 mg/kg/h), xylazine (0.2 mg/kg/h) and midazolam (0.2 mg/kg/h).in-vivo experiment was designed to evaluate the R1 and R2* variations at different contrast concentrations during first-pass perfusion, in order to establish the relationship between the two relaxation rates in-vivo. In a second in-vivo experiment, 11 pigs with different degrees of myocardial perfusion were examined simultaneously by the dual-bolus and dual-saturation methods to allow comparison of cardiac perfusion estimates by the two approaches. For hypoperfusion, 6 pigs underwent a closed-chest transmural myocardial infarction (45 minutes ischemia in the left anterior descending coronary artery followed by reperfusion); the procedure was performed with a dedicated percutaneous catheter inserted via the femoral artery [The first l artery ,19. Indein-vivo, different TS and TE were acquired during first pass of the contrast agent. To estimate R1 values every second, 6 saturation times were acquired over the range from 3.2-328.2 ms, with intervening gaps of 64.8 ms. The saturation times were acquired by sharing the same saturation pulse between 6 slice locations in transverse orientation along the descending aorta. The slices were acquired in ascending order to ensure that a saturated blood signal due to RF excitation pulses in one slice did not influence the signal acquired from the other slices. To estimate R2* values for every TS, four TE were acquired over the range from 0.8-3.8 ms with intervening gaps of 1.0 ms. The same experiment was performed dynamically every second over one minute, during which 0.1 mmol/kg of Gd-DTPA Magnevist  was injected at a rate of 3 ml/s, followed by a 20 ml saline flush.To define the relation between R1 and R2* values and contrast concentration For these experiments, R2* values were obtained by fitting the signal at different TE to a mono-exponential model. The same procedure was applied for all TE values at different TS, and the mean was taken as the final result. R1 values were obtained by fitting the signal from different TS at the shortest TE to a regular saturation recovery model. After estimation of R2* and R1 for each dynamic acquisition, the values of the two quantities were fitted to a quadratic relation, as previously proposed .To evaluate the two quantification strategies, dual-saturation acquisition was combined with the dual-bolus injection protocol. For dual-bolus injection, the injection protocol was done using a single power MR injector Spectris Solaris  using a previously described methodology by Ishida et al. . Two bol3) and an additional interleaved low resolution image (6.8×2.6×10.0 mm3) were acquired at every R-R interval over 120 s. Interleaved low resolution image was acquired between the saturation pulse of the first high resolution image and image readout, sharing the same saturation pulse between low and high resolution images. To prevent any signal interaction between both images the planning was defined avoiding crosses between slices. When the heart rate was above 120 bpm one high resolution slice was removed from the acquisition to include all slices in a single RR interval. The image sequence consisted of spoiled TFE acquisition with a saturation time of 100 ms for high resolution images and 20 ms for low resolution images (TR/TE/FA = 2.0 ms/0.9 ms/15°). Images were acquired with reverse-linear profile ordering with a half scan factor of 75% and a SENSE factor of 2.6. All perfusion images were reconstructed to 2.0×2.0 mm2 in-plane resolution. To obtain baseline T1 values, an additional MOLLI (modified look-locker inversion recovery) T1 map was acquired with a 3–5 scheme [Three high resolution images . Time-intensity curves were transformed into concentration curves using the methodology described in the where F represents the magnitude of the function, directly related to the blood flow, and k describes the decay rate of h(t) due to contrast wash-out. No shift was included in the model since AIF and cardiac muscle contrast uptake were measured close enough, making such correction unnecessary. Cardiac flow was assessed by scaling the F value according to a muscle density of 1.05 g/ml and assuming a constant hematocrit of 0.35 following high permeability modelAfter flow-map generation, a quantitative comparison of the dual-bolus and dual-saturation methods was performed by ROI analysis in the ischemic or hyperemic region and the contralateral wall for all acquired slices and animals. The ROI was defined in the dual-bolus flow maps and was copied to dual-saturation maps to extract the final numerical data. Correlation and Bland-Altman plot analysis were performed to compare flow measured with the two approaches.Table PD) increased linearly with contrast concentration in the phantom, with a linear fitting correlation coefficient of R2 = 0.986. In contrast, for a TS above 40 ms the signal was fully recovered at higher contrast concentrations, with a value of S/SPD close to 1, saturating the signal response.The effect of the TS on signal intensity at different contrast concentrations is shown in Figure c/Sb data   Figure d.Figure 2* and ΔR1 . Bland-Altmann plot .In this study we present a validation analysis for myocardial perfusion by dual-saturation acquisition including in-vivo experiments (0.1 vs 0.02 mmol/kg), while the r1 relaxivity of Gd-BOPTA is just 1.31 times higher than that of Gd-DPTA [This correlation is slightly higher than that previously reported for analysis of the dual-bolus method with the multiple saturation recovery times (m-SRT) technique   and cardiac muscle from the MR signal. One factor is a high contrast concentration passing through the ventricular cavity, which directly produces an underestimate of AIF due to over-long TS selection and R2* signal decay. A second factor is the readout effect of the cardiac muscle MR signal in high resolution images and its influence on the estimation of contrast concentration from the MR signal.Accurate estimation of AIF first requires careful selection of the TS in order to avoid signal saturation at a too high contrast concentration. The results presented in Figure in-vivo, as previously reported [The second source of error that can affect AIF estimation at high contrast concentration is the potential for R2* effects. This effect can be observed in Figure reported ,22.Readout effects are more pronounced in high resolution images than in the AIF estimation due to the application of higher turbo factors before reaching the center of the k-space. This series of RF pulses influences magnetization and therefore the estimation of contrast concentration within the cardiac muscle. The proposed signal model demonstrates a good agreement with the acquired data, yielding a correlation coefficient close to 1.The main limitation of the present study is that the dual-saturation method was compared to dual-bolus acquisition as a gold standard. Comparison with microspheres was not performed. However, the dual-bolus method has consistently shown good agreement in phantom analysis , with miThe dual-saturation strategy for absolute quantification of myocardial perfusion by 3T-CMR shows an almost perfect correlation with the dual-bolus method across a wide range of perfusion scenarios . The dual-saturation strategy is moreover easier to implement in clinical protocols. Obtaining reliable flow measurements with the dual-saturation strategy requires careful selection of specific parameters. Chief among these are the correct selection of TS to ensure adequate evaluation of contrast uptake in the AIF (TS < 30 ms) and cardiac muscle (TS < 100 ms); the relation between the contrast injection protocol and the R2* effects on the AIF—always attempting to acquire images with the shortest possible TE; and the readout scheme for proper conversion of signal intensity changes into real contrast uptake.


Many college students drink heavily and experience myriad associated negative consequences. This review suggests that a developmental perspective can facilitate a better understanding of college drinking. Specifically, using an emerging adulthood framework that considers the ongoing role of parents and neurodevelopmental processes can provide insight into why students drink. Most college students drink and tend to drink more and more heavily than their non–college-attending peers. These drinking patterns are affected by environmental and temporal characteristics specific to the college environment, including residential campus living, the academic week, and the academic year. Additional psychosocial factors are of particular relevance to the drinking behavior of college-age people, and include exaggerated peer norms, the development and use of protective behavioral strategies, and mental health considerations. Understanding the unique interaction of person and environment is key to designing prevention/intervention efforts. Approximately 41 percent of 18- to 24-year-olds are enrolled in a postsecondary degree-granting institution . As a grThe developmental context in which drinking behavior occurs in college-aged men and women is unique, and developmental considerations can inform both basic and intervention research with this population.The sociodevelopmental notion of emerging adulthood is a helpful conceptual framework through which to understand risky drinking during the college years , 2005. FIdentity exploration. During emerging adulthood, when individuals are figuring out their own identity (particularly in the domains of love and work), alcohol use may be a part of exploring a wide range of lifestyle options before adopting adult roles and identity. Students may also use alcohol to cope with identity confusion .Instability. The college years are associated with frequent residential moves and changes in friends and partners, educational status, and jobs. Alcohol use often is elevated during periods of transition  and perhSelf-focus. Upon college entry, students gain independence from their family and relative freedom from obligations and commitments to others. They make independent decisions, and with weaker social controls from family and other institutions, they experience fewer constraints on risk behaviors. Friends may have the most influence on behavior during this time, and students inclined to use alcohol likely establish friendships that support drinking .Feeling in-between. Emerging adults may feel neither adolescent nor fully adult, and therefore may feel a sense of responsibility in some domains but not others. For example, they may feel capable of deciding whether or not to use alcohol but may not feel they need to conform to adult standards of comportment. Some students may see the college years as a “time out” from adult responsibilities  and givePossibilities. Finally, emerging adulthood is a time when people can make dramatic changes in their lives and is characterized by biased optimism. Because college students’ expectations for a positive future are so high, they may not acknowledge that negative consequences related to drinking behavior may occur.As mentioned above, once emerging adults head to college, they depart from the structure and oversight provided when living with parents. However, parents do still matter during the college years. For example, research finds that higher levels of perceived parental involvement may buffer students from the effects of peers on alcohol use and problems ; parentaThe developmental context of college drinking is characterized not only by psychosocial but also biological factors. A growing body of research reveals that the brain’s frontal lobes do not fully mature until the mid-20s . During In the next section, we summarize descriptive data about college student drinking and its consequences, keeping in mind that it occurs within this developmental context characterized by the features of emerging adulthood, a changing but still significant role for parents, and continuing neurocognitive development.National surveys provide valuable data on the drinking habits of college students in the United States. They include the Harvard College Alcohol Study e.g., , the NatHeavy drinking results in negative consequences for both drinking and nondrinking students:A total of 646,000 physical assaults, 97,000 sexual assaults, 599,000 unintentional injuries, and 1,825 deaths are linked to alcohol use among college students annually .Forty percent of college student drinkers report alcohol-induced memory loss, such as blackouts , which iTwenty-one percent of college student drinkers report unplanned sexual activity while drinking, and 10 percent report unprotected sex while drinking . Such beStudents also report that drinking alcohol is related to social/interpersonal problems, poor self-care , and diminished self-regard  .Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition range from 6 to 31 percent, and rates of alcohol dependence range from 6 to 16 percent , and even dropping out . According to College attendance places students at increased risk for alcohol consumption and alcohol-related problems in part because of environmental features, including communal living and an academic week that often allows students to select a schedule with long weekends. Furthermore, the rhythm of the academic year includes social holidays and events that happen predictably across college campuses.Communal living is an important risk factor. For example, The transition from high school to the first year of college is associated with increases in alcohol use and heavy drinking . Heavy oCollege students typically drink the heaviest on weekends, and, for some, weekend-like drinking begins on Thursday . HoweverImportantly, patterns of drinking across the academic year are not uniform; multiple trajectories characterize the overall pattern of drinking across the first college year . HoweverDrinking among freshmen peaks during Spring Break, Thanksgiving, Christmas, and New Year’s weeks  and tendDrinking and related consequences are higher during Spring Break than the typical week ; howeverExtreme drinking and negative alcohol-related consequences also are associated with 21st birthdays , which tDrinking also tends to spike during campus- or university-specific events. For example, during “State Patty’s Day,” a student-constructed, party-focused holiday at Pennsylvania State University, first-year students were more likely to drink and drink heavily . On thisSporting events also are associated with heavy drinking among college students  and alsoDespite the often risky nature of drinking during college, most, although not all, students “mature out” of such behavior . The aveCollege student drinking is affected by several psychosocial determinants that also influence drinking behavior in similar ways during other developmental periods. For example, like the general populations, college students tend to drink more if they believe drinking will have positive effects and consequences, and they tend to drink less if they have negative expectations about drinking e.g., . In addiA person’s reasons or motives for drinking also influence their alcohol use. For example, drinking to increase positive affect, called enhancement motives, consistently predicts alcohol use and tends to be linked to negative alcohol consequences indirectly, through higher drinking levels . MeanwhiBelow we discuss in more detail common psychosocial determinants that exert influence in a way that is unique to the college years. We highlight exaggerated norms, protective behavioral strategies, and mental health.Peers influence young adult drinkers in several direct and indirect ways . PerhapsIn light of all of the contextual and developmental factors that contribute to risk described above, it is essential that students learn to drink safely (if they choose to drink) when navigating the novel drinking environment of college. However, the extent to which college students acquire and use safe drinking skills varies. Most emerging adults leave home for college before they attain the minimum legal drinking age. Thus, peers and not parents or other adults often serve as the primary sources for learning how to drink. Protective behavioral strategies—techniques that can be used to minimize harm associated with alcohol use such as setting drink limits, consuming nonalcoholic in addition to alcoholic drinks, avoiding drinking games, and using a designated driver—have received an increasing amount of attention in the college-drinking literature over the past few decades. A recent review highlights several studies that consistently reveal that individuals who report using more protective behavioral strategies also report drinking less and/or experiencing fewer alcohol-related problems .Approximately three-quarters of lifetime mood or anxiety disorders begin by age 24, coinciding with the typical college years , and aboThe findings reviewed above have several implications for interventions with the special population of college-aged individuals. In general, a harm prevention/harm reduction approach, as opposed to an abstinence-based approach is considered most appropriate for young people who are developing drinking habits and have not exhibited signs of dependence . Also, gDespite the importance of the developmental context to college student drinking, to date, developmental considerations have had limited influence on intervention development. A notable exception is parent-based intervention, which has been well received and shows promise both as a standalone intervention  and a suTailoring interventions to address environmental issues of the college setting also may be beneficial. Such interventions include establishing substance-free residential options and changing the academic schedule to ensure that students take classes on Fridays and also in the mornings . IncreasEvent-specific prevention (ESP) is an intervention strategy that addresses temporal determinants of drinking behavior . ESP assMany of the psychosocial determinants of drinking during emerging adulthood reviewed above have informed the development of alcohol abuse prevention interventions. For example, correcting exaggerated perceived norms is a well-documented active ingredient of successful risk-reduction programs delivered both in person and by computer . FurtherTailoring alcohol risk reduction interventions to students with mental health concerns would be another way to integrate psychosocial determinants of drinking and the emerging adulthood framework into new interventions. For example, interventions that provide alternatives to substance use for coping with negative mood states could prove fruitful for students high on the instability dimension of emerging adulthood and who are experiencing negative affect related to transitions, or for students who experience identity confusion during this time of exploration. In addition, recent data demonstrate that depression may interfere with intervention-related change . AlthougTo date, we have no evidence-based interventions to reduce high-risk practices such as pregaming or drinking games . Such inMuch progress has been made in understanding the risk for alcohol misuse among college students. However, there still is room to understand the developmental, social, and environmental factors influencing college student drinking, to best design interventions that can ultimately reduce harm for this special population.


WNT10A defects are associated with tooth agenesis. We characterized Wnt10a null mice generated by the knockout mouse project (KOMP) and six families with WNT10A mutations, including a novel p.Arg104Cys defect, in the absence of EDA,EDAR, or EDARADD variations. Wnt10a null mice exhibited supernumerary mandibular fourth molars, and smaller molars with abnormal cusp patterning and root taurodontism. Wnt10a−/− incisors showed distinctive apical–lingual wedge-shaped defects. These findings spurred us to closely examine the dental phenotypes of our WNT10A families. WNT10A heterozygotes exhibited molar root taurodontism and mild tooth agenesis (with incomplete penetrance) in their permanent dentitions. Individuals with two defective WNT10A alleles showed severe tooth agenesis and had fewer cusps on their molars. The misshapened molar crowns and roots were consistent with the Wnt10a null phenotype and were not previously associated with WNT10A defects. The missing teeth contrasted with the presence of supplemental teeth in the Wnt10a null mice and demonstrated mammalian species differences in the roles of Wnt signaling in early tooth development. We conclude that molar crown and root dysmorphologies are caused by WNT10A defects and that the severity of the tooth agenesis correlates with the number of defective WNT10A alleles.WNT10A is a signaling molecule involved in tooth development, and  It has only recently become apparent, however, that the severity of the tooth agenesis is increased if both WNT10A alleles are defective or if a WNT10A defect is combined with defects in ectodysplasin A , ectodysplasin A receptor , or EDAR-associated death domain   cells produced by Velocigene, Regeneron Pharmaceuticals, Inc  for the NIH Knockout Mouse Project (KOMP). The knockout deleted 11,515 bp  that included the entire Wnt10a coding region. The knockout construct replaced the Wnt10a gene in ES cells (C57BL/6NTac background) with a ZEN-Ub1 cassette that introduced a bacterial lacZ code at the natural Wnt10a translation initiation codon (in exon 1) and a downstream neomycin phosphotransferase gene (neor) driven by the human ubiquitin C gene promoter (hUBCpro). The neomycin resistance gene was bracketed by a locus of X-over P1 sequence from bacteriophage P1 (loxP) sequences for convenient removal of the neor selection code. Successful knockout was determined by PCR genotyping (http://www.velocigene.com/komp/detail/14810). Three archived Wnt10a null mouse heads  and one archived wild-type head (1331) from 16-week-old mice were provided to us from KOMP for analysis.Wnt10a+/+ specimen 1331 and Wnt10a−/− specimens 1316, 1317, 1329, and sliced through the mental symphysis with a razor blade to generate hemimandibles. These were carefully dissected of soft tissues under a stereoscopic microscope using tissue forceps and a spoon excavator. The cleaned hemimandibles were radiographed using a Faxitron X-ray cabinet model MX-20  operating at 32 kV for 120 sec and photographed using a Nikon SMZ1000 dissection microscope equipped with a Nikon digital camera DXM1200 .The mandibles were removed from the four 16-week-old archived heads provided by KOMP: Molars were extracted from the left mandibles and debrided by soaking in 1% sodium hypochlorite for 10 min followed by ultrasound for 4 min. The right hemimandibles and molars were dehydrated by gradient acetone , mounted on 1″ aluminum sample stubs, and coated with carbon. Backscatter scanning electron microscopy (SEM) evaluation was performed using a Hitachi S-3000N variable pressure scanning electron microscope  under backscatter mode at 15 kV and 20 Pa pressure at the University of Michigan Microscopy and Image analysis Laboratory . Images were obtained at ×30, ×200, and ×600 magnification.μCT100 Scanco Medical, Bassersdorf, Switzerland) at the University of Michigan School of Dentistry Microcomputed tomography (μCT) core. Scan settings were as follows: voxel size 8 μm, 70 kVp, 114 μA, 0.5 mm aluminum filter, and integration time of 500 msec. The system was calibrated with a manufacturer-provided phantom consisting of 0, 100, 200, 400, 800, and 1200 mg HA/cm3 phantom rods embedded in resin. The data were analyzed using the μCT Evaluation Program V6.5-1. For the 3D molar images, the outside contour of each molar and incisor was outlined by marking the border of the molar/incisor on each scanning section. 3D images were generated within the contour-defined area. Because mature enamel is more dense than dentin and dentin is more dense than pulp, the enamel, dentin, and pulp portions of the molars were isolated using density threshold ranges for the purpose of measuring volume and density. The threshold range for measuring the entire tooth was 0–1000, for pulp was 0–260, for dentin was 260–650 (colored gray), and for enamel was 650–1000 (colored white). Enamel, dentin, and pulp volumes were calculated for each tooth using the μCT Evaluation Program V6.5-1 and the volumes within the contour-defined area of each tooth that corresponded to the threshold ranges described above. Virtual sagittal images were generated by cutting through the central plane of the 3D images using the image analysis software.All eight hemimandibles were embedded in 1% agarose and placed in a 19-mm-diameter tube and scanned over the entire length of the mandible using a microCT system  or saliva (2 mL) was obtained from recruited individuals and genomic DNA was isolated using the QIAamp DNA Blood Maxi Kit  or Saliva DNA Collection, Preservation, and Isolation Kit , respectively. The quality and quantity of the extracted DNA samples were determined by spectrophotometry at ODμg) was characterized by whole-exome sequencing using an Illumina TruSeq Exome Enrichment system and HiSeq 2000 platform at 75 base paired-end sequencing . Sequence output was inspected and aligned against human reference genome hg19, and variants were filtered and annotated using Ingenuity Variant Analysis tool by the University of Michigan Bioinformatics Core. In the Yale Center for Genome Analysis, the exome sequencing and subsequent analysis were modified from a previous report . In the University of Michigan DNA Sequencing Core, genomic DNA  using specific oligonucleotide primer pairs (Table S2). The amplification products were purified and characterized by direct DNA sequencing (Sanger sequencing) at the University of Michigan DNA Sequencing Core. The sequencing data were then compared to the human reference sequence, and sequence variants called and evaluated. WNT10A c.DNA and genomic changes were numbered with respect to the National Center for Biotechnology Information (NCBI) human WNT10A mRNA reference sequence: NM_025216.2 (numbered from the first nucleotide of the WNT10A translation initiation codon in exon 1) and genomic reference sequence NG_012179.1 (numbered from the first nucleotide of the reference sequence).The coding exons and intron junctions for WNT10A mutations cause tooth agenesis, we expected that Wnt10a null mice might also exhibit missing teeth. However, the Wnt10a null mice have a complete dentition. More surprisingly, one mouse (Null 1317) had erupted mandibular fourth molars. The supernumerary distal molars were small in size, with a single cusp and thin root , residual sockets for supernumerary teeth distal to mandibular third molars were evident, and indicated that fourth molars developed but were lost, presumably during tissue dissection and sample processing .Wild-type mice have one incisor and three molars in each quadrant of their dentitions. Since human Wnt10a−/− mice also exhibited crown dysmorphologies. To better characterize these alterations, we examined the mandibular molars of wild-type and null mice by light and scanning electron microscopy   and the first lingual cusp (L1) were fused together, forming a long, curved ridge surrounding the mesial and buccal aspects of the crown. The other two lingual cusps (L2 and L3) were relatively unaffected, although their size was apparently smaller. Also, instead of having a deep groove separating B2–L2 and B3–L3, the mutant first molar showed a deep U-shaped groove surrounding the second lingual cusp (L2), making this cusp the most prominent on the crown. Noticeably, this deep groove in one of the null mice (Null 1316) was black and appeared to be carious, which is exceedingly rare in mouse teeth. The overall morphology of the Wnt10a−/− mandibular second molar was not significantly altered, although the tooth size appeared to be smaller. However, unlike the wild-type molars, which have five cusps , the mutant molars had only four cusps, lacking the most distal cusp (cusp 4). The Wnt10a null mandibular third molar had a relatively normal crown morphology and cusp patterning but appeared to be reduced in size. Taken together, these findings demonstrate that Wnt10a plays a significant role in early morphogenesis and patterning of tooth crown development.Cusp patterning was altered in Wnt10a−/− molars showed taurodontic root morphology  of the Wnt10a during mouse molar development results in supernumerary mandibular fourth molars, smaller molars, altered root form (taurodontism), reduced volume of dentin, pulp calcifications, molar crown dysmorphologies, and increased risk of root resorption following tooth development and eruption.Taken together, these studies indicate that the absence of Wnt10a−/− incisors were characterized using μCT. In wild-type incisors, dentin completely encloses the pulp lingually (opposite the enamel). 3D reconstructions of the mandibular incisors showed that the lengths of the WT and Wnt10a null incisors were similar but, significantly, the lingual side of the apical end of the Wnt10a−/− incisor had a V-shaped density defect, suggesting that the onset of dentin mineralization at the lingual surface of mandibular incisor was delayed or defective  that failed to develop  tooth agenesis associated with ism Figs., 7.Family 1 is a three-generation Taiwanese family Fig.A. The prNo hair, skin, or sweating problems were observed in the family, except that the paternal grandmother (I:2) was reported to have thin hair, and the scalp hair of the probands parents  was curly. Noticeably, among the five recruited individuals of the first two generations, the maternal grandfather (I:3) and the father (II:5) had intestinal polyps, while the other three  did not, according to the results of colonoscopy.MSX1,PAX9,AXIN2,LTBP3,EDA,EDAR, and EDARADD, but did identify two missense mutations in WNT10A . The p.Arg104Cys variation is not listed in the Exome Variant Server (EVS) database (DB). The p.Gly213Ser variation shows almost zero frequency  in the EVS DB. Sanger sequencing demonstrated that the proband was a compound heterozygote for these two mutations with the p.Arg104Cys mutation inherited from the mother and the p.Gly213Ser from the father. Noticeably, the maternal grandfather (I:3) and the youngest child (III:7), who had peg laterals and molar taurodontism. respectively, carried the heterozygous p.Arg104Cys mutation. While the p.Gly213Ser mutation has been shown to be disease causing (Table S1), the p.Arg104Cys mutation is a novel mutation that has not been previously reported. This missense mutation substituted a highly conserved positively charged residue (Arg104) with cysteine, which was predicted to be probably damaging with a score of 1.000 by PolyPhen-2. Therefore, we conclude that the p.Arg104Cys mutation is disease causing. Although some of the heterozygous carriers of this mutation  displayed abnormal tooth morphology, none of them had missing teeth except third molars, which suggested a low disease penetrance and expressivity for tooth agenesis associated with this WNT10A allele.Based upon the family pedigree, we suspected that the tooth agenesis might be caused by a recessive mutation. Therefore, we performed whole-exome sequencing of the parent–child trio . By screening through the candidate genes of tooth agenesis with exome data, we identified no potential disease-causing sequence variants in Family 2 was a four-generation Caucasian family referred to us by a geneticist Fig.E–I. BaseIn addition to tooth number abnormality, altered crown and root morphology of teeth were also observed in different family members. For the proband (IV:4) Fig.H, the upWNT10A mutations, g.6836C>A, c.321C>A, p.Cys107* and g.14757T>A, c.682T>A, p.Phe228Ile. The p.Cys107* variation shows ∼0.10% frequency , and the p.Phe228Ile variation ∼1.8% frequency  in the EVS DB. No potential disease-causing mutations were identified in other candidate genes for isolated tooth agenesis, MSX1,PAX9,AXIN2,LTBP3,EDA,EDAR, and EDARADD. The WNT10A mutations were confirmed and segregation analyzed by Sanger sequencing. The results showed that while the mother (III:3) and the aunt (III:2) were heterozygous for the nonsense mutation (p.Cys107*), both of the two affected children  were compound heterozygotes for p.Cys107* and p.Phe228Ile. Interestingly, the unaffected older sister (IV:3) and the father (III:4) were heterozygous carriers of the p.Cys107* and p.Phe228Ile mutation, respectively, which suggested incomplete penetrance of tooth agenesis for both of these WNT10A mutations in heterozygotes.To identify the genetic defect causing tooth agenesis in this family, we submitted DNA samples from four of the family members  for exome sequencing. With the analyzed exome data, we first searched for mutations in genes known to be associated with tooth agenesis and found two reported Family 3 was a three-generation Caucasian family referred to us by a geneticist Fig.. The proWNT10A by Sanger sequencing. However, considering the potential phenotypic contribution from sequence variants of other ectodermal dysplasia genes, we also analyzed EDA,EDAR, and EDARADD. The results identified four WNT10A sequence variations running in this family, g.6836C>A, c.321C>A, p.Cys107*; g.7008G>A, c.493G>A, p.Gly165Arg; g.14757T>A, c.682T>A, p.Phe228Ile; and g.15162A>C, c.1087A>C, p.Asn363His . The p.Gly165Arg variation shows ∼0.78% frequency (102/13006) and the p.Asn363His variation shows ∼0.028% frequency (3/10642) in the EVS DB. Since both parents were compound heterozygous for two WNT10A mutations, all of the children were also compound heterozygous for two of the four WNT10A alleles. The father (II:3) and the mother (II:4) carried p.Cys107*, p.Asn363His, and p.Gly165Arg, p.Phe228Ile mutations, respectively. The proband (III:1) was a compound heterozygote for p.Cys107* and p.Phe228Ile mutations, while the two unaffected children  were both carrying p.Gly165Arg and p.Asn363His mutations.Based upon the pattern of disease inheritance as well as the phenotypic similarity of this family with the previous two families, we specifically targeted WNT10A,EDA,EDAR, and EDARADD revealed that the proband was homozygous for the WNT10A p.Phe228Ile mutation . Besides the missing teeth, the proband had peg-shaped maxillary incisors . Abnormalities of hair, nails, or sweating were denied.Family 4 was Caucasian, with an apparent simplex pattern of inheritance Fig.E, as botFamily 5 was a Hispanic nuclear family with parents and two children. The proband, an 8-year-old girl, was the only affected individual in the family, who had 11 missing teeth  excluding third molars. All of her primary molars and the only permanent first molar were taurodontic Fig.F. Also, WNT10A,EDA,EDAR, and EDARADD revealed that the proband was compound heterozygous for two WNT10A missense mutations, g.14712T>A, c.637T>A, p.Gly213Ser and g.14757T>A, c.682T>A, p.Phe228Ile . The p.Gly213Ser mutation was inherited from the mother, and the p.Phe228Ile from the father. The younger brother had neither WNT10A mutation .Target gene analyses of WNT10A,EDA,EDAR, and EDARADD being screened, a heterozygous p.Gly213Ser mutation was identified . Although the p.Gly213Ser variation has a very low frequency  in the EVS DB, it has a higher allele frequency in Asian population, where it was associated with the agenesis of maxillary permanent canines , which suggests a pathogenic mechanism of loss of function and haploinsufficiency. Conditional deletion of β-catenin in either the dental epithelium or mesenchyme in mice leads to tooth developmental arrest at the bud stage. This helps explain the presence of tooth agenesis in WNT10A patients . Similarly, while mutations in axis inhibitor 2  and latent transforming growth factor-beta-binding protein 3  were demonstrated to cause severe oligodontia in humans  in tooth formation. Therefore, it is possible that WNT10A might be involved in noncanonical Wnt signaling, which might have a distinct role during odontogenesis. Many mouse models have supernumerary teeth, including knockout mice for genes involved in BMP, FGF, and SHH signaling, demonstrating that tooth number is determined by a complicated genetic network of many signaling pathways that are critical for early tooth development  and Downless (Edar mutant) mice show altered cusp morphology, indicating the significance of EDA-induced NF-κB signaling during tooth morphogenesis . During tooth root formation, the cervical loop of the enamel organ forms an epithelial sheet of two cell layers (HERS) that induces the differentiation of root odontoblasts. As the tooth erupts at the same time, the root sheath proliferates and the root elongates , a syndrome caused by EDA mutations. According to one study, 82% (9/11) of HED males and 67% (24/36) of heterozygous female carriers exhibited taurodontic molars  mutations cause Tricho-Dento-Osseous (TDO) syndrome with taurodontism, while molars manifest the taurodontic phenotype in conditional Dlx3 knockout mice , smaller molars, misshapened crowns and roots (taurodontism), pulp stones, and a susceptibility to root resorption. We identified WNT10A defects in six families with nonsyndromic tooth agenesis, including the novel WNT10A mutation . We ruled out defects in other candidate genes for tooth agenesis. Careful inspection of the dental phenotypes in these families identified many instances of misshapened crowns and root taurodonism. Molar crown dysmorphologies and root taurodontism have not previously been reported in families with WNT10A defects, but should be routinely documented in future cases. In our WNT10A families, pulp stones were only observed in our oldest proband . No root resorption was observed in any of our patients, but in consideration of the mouse Wnt10a null phenotype, the presence or absence of these phenotypic features should be included in future clinical reports.We conclude that WNT10A plays significant roles in tooth initiation and crown and root morphogenesis. The dental phenotype observed in