The act of making a choice, apart from any outcomes the choice may yield, has, paradoxically, been linked to both the enhancement and the detriment of intrinsic motivation. Research has implicated two factors in potentially mediating these contradictory effects: the personal control conferred by a choice and the costs associated with a choice. Across four experiments, utilizing a physical effort task disguised as a simple video game, we systematically varied costs across two levels of physical effort requirements  and control over effort costs across three levels of choice  to disambiguate how these factors affect the motivational consequences of choosing within an effortful task. Together, our results indicated that, in the face of effort requirements, illusory control alone may not sufficiently enhance perceptions of personal control to boost intrinsic motivation; rather, the experience of actual control may be necessary to overcome effort costs and elevate performance. Additionally, we demonstrated that conditions of illusory control, while otherwise unmotivating, can through association with the experience of free-choice, be transformed to have a positive effect on motivation. People will fight for their right to choose in some instances and flip a coin to avoid choosing in others; in parallel, research on the relationship between choice and motivation is complex and has produced results that are often conflicting. Research on the motivational consequences of choosing, rather than focusing on the content of choices, has focused on how the very act of making a choice influences valuation processes and intrinsic motivation—and the findings have been contradictory. The act of making a choice, separable from any extrinsic gains or losses the decision may incur, has been linked to both motivational enhancements and decrements .From these paradoxical findings, however, two major factors have emerged as potential mediators of whether the act of choosing has a positive or negative effect on intrinsic motivation: (1) the personal control provided by a choice and (2) the costs associated with making a choice . Thus, tThere is substantial evidence that the act of making choices, in and of itself, is intrinsically rewarding and motivating , 2015. FConversely, there are also certain contexts in which passivity is valued and choice is avoided or appears to have a discounting effect on outcomes . This prper se that bestows psychological benefits but instead the sense of personal control conferred by making a decision. Consistent with this proposition, evidence suggests that the degree of personal control offered by a decision may mediate between its beneficial versus detrimental effects on intrinsic motivation , and three levels of control over effort costs: real control (Free-Choice), illusory control (Restricted-Choice), and no control (No-Choice). We assessed the motivational outcomes of choice via preference and performance linked to the different conditions, as these measures are commonly utilized in the choice literature as a proxy for motivation . As the We had three overarching hypotheses across four experiments comprising this study. Based on effort discounting theory, we hypothesized that (I) intrinsic motivation and thus motivational outcomes would be generally enhanced for lower compared to higher effort requirement trials (Low-Requirement > High-Requirement). Based on evidence that perceptions of control have positive effects on motivation, we further hypothesized that (II) intrinsic motivation and thus motivational outcomes would improve as a function of the amount of control available (Free-Choice > Restricted-Choice > No-Choice). Finally, based on a combination of evidence suggesting that a context of high costs and low personal control may produce a particularly damaging coalition, we anticipated that preference for and performance on an effortful task should be undermined most severely at the junction of high effort costs and low personal control . Thus, we hypothesized that (III) intrinsic motivation and thus motivational outcomes would be most strongly diminished when no choice is offered, but effort expenditures are high. Across four experiments, our approach to testing these hypotheses was to first examine the individual influences of cost (effort requirements) and control (choice conditions) and then combine these factors to test their conjoint effects on motivational outcomes.Figure 1 for a trial schematic. Participants were adult undergraduate students, recruited from Rutgers University-Newark, who provided written informed consent in accordance with the Declaration of Helsinki and were compensated with course credit. The Institutional Review Board of Rutgers University approved the study. To test study hypotheses, we created a novel physical effort task in E-Prime , which employed, in different combinations across four experiments, three choice conditions offering different levels of control (described below) over two levels of physical effort costs. The paradigm was presented as a video game in which participants fought aliens using “blaster” weapons. The blasters were “charged” manually by quick, repeated key presses , represented in real-time by an on-screen “charge bar,” which incrementally filled in red with each key press. Blasters only “fired” at an alien if they were fully charged in a pre-allotted amount of time. See Table 1), the full set of choice (control) and effort (cost) conditions are defined here. The full set of conditions included two levels of effort costs  and three levels of control across choice conditions . The three choice conditions directly manipulated level of control over effort requirements by offering real control, illusory control, and no control, respectively . Subtle blaster color categories represented the choice and effort conditions, with two similarly colored exemplar blasters in each category . Participants were given no explicit information regarding how the blaster cue color categories mapped onto choice and effort contingencies. Across all experiments, choice and effort conditions were presented in random order. To fill the charge bar, Low-Requirement blasters required a random number of presses between 11 and 20, while High-Requirement blasters required a random number of presses between 21 and 30. Effort requirements (number of presses) were randomly drawn from ranges that were only subtly different between conditions so that effort contingencies would be somewhat uncertain. This feature of our design allowed implementation of our Restricted-Choice condition (detailed in Experiment 2). See the Supplementary Materials for further detail regarding conditions and stimuli sets.Four variations on this task were implemented, comprising four separate experiments with non-overlapping participant samples. While each individual experiment involved a different subset of conditions (indexed in Table 1) on preference for and performance on choice- and effort- related trials. In order to measure preference for blasters linked to different conditions, participants rated each blaster in the set on seven point Likert-type scale, with a one indicating “I don’t like it at all” and a seven indicating “I like it a lot”. Each blaster was rated before and after playing the game. During both rating sessions all blasters were visible on the screen. As such, ratings inherently represented preference in relation to the whole set of blaster cues for a given game. In each experiment, pre-game ratings were statistically compared to ensure there was no systematic bias in preference before the game. To control for individuals’ prior color predilections, preference was operationalized as an individual’s change in preference (computed by subtracting post- from pre-game ratings). Preference change scores were statistically compared across experimental conditions. Additionally, to determine whether preference changes were different than no change, preference change scores were statistically compared to zero. Performance was operationalized as the percentage of successful trials in a given condition. Thus, the two dependent variables across all four experiments were the change in preference for blasters associated with each condition and the percentage of successful trials in each condition. All error bars throughout the manuscript represent standard error of the mean.Four experiments tested the effects of different combinations of choice (control) and effort (costs) conditions  and varied levels of control over effort costs. The key difference between Experiments 3 and 4 is that Experiment 3 utilized two levels of control across choice conditions , while Experiment 4 utilized three levels of control across choice conditions .Experiment 1 used trials of either Low- or High-Requirement, while holding the control factor constant with only No-Choice trials in order to test the effects of different levels of effort costs on preference and performance. In this experiment we further sought to establish baseline preference and performance levels for Low- and High-Requirement trials in the absence of choice. The No-Choice condition was implemented by offering participants a single blaster per trial. In line with our first hypothesis, we predicted that participants would show an increased preference for and enhanced performance on Low- compared to High-Requirement trials.SD = 7.33). Participants were randomly assigned to one of four counterbalanced sets of stimuli . Two stimuli sets were composed of only the green blasters and two sets of only orange. Within all four sets of blaster stimuli, the color-value (lightness vs. darkness) distinguished the levels of effort  in a counterbalanced fashion, with two exemplar blasters in each effort condition. In a single block there were 16 No-Choice trials: eight Low-Requirement and eight High-Requirement. Blocks repeated six times across the game, resulting in a total of 48 No-Choice, Low-Requirement trials and 48 No-Choice, High-Requirement trials.The final sample, excluding one subject for whom no data was collected, totaled 36 participants  = -1.43, p = 0.162]. Preference ratings for Low-Requirement blasters increased from an average pre-game rating of 4.08 (SD = 1.55) to an average post-game rating of 5.32 (SD = 1.51). For High-Requirement blasters, preference ratings decreased from an average pre-game rating of 4.67 (SD = 1.49) to an average post-game rating of 2.82 (SD = 1.67). For both Low- and High-Requirement conditions, the change in preference was significantly different from zero (both p’s < 0.005). Additionally, the change in preference for Low- compared to High-Requirement blasters differed significantly , suggesting that the difference between Low- and High- effort requirements was sufficiently learned. There were no gender differences in the change in preference for either condition.Figure 3B shows success rates for Low- and High-Requirement trials in Experiment 1. While success rates were high for both conditions, success for Low-Requirement trials  was higher than for High-Requirement trials , although this moderate difference did not reach significance . Males had significantly better performance than females for both Low-Requirement  and High-Requirement .In this experiment in which only effort costs were varied, we sought to establish baseline levels of preference for and performance on Low-Requirement and High-Requirement conditions when there was no control over effort costs. Based on effort discounting theory, which holds that the value of an outcome is discounted by its associated effort requirement, we hypothesized that participants would show enhanced preference and performance for the Low- compared to High-Requirement condition. These hypotheses were supported, as evidenced by strong preference for Low-Requirement blasters. High success rates across conditions confirmed that both the Low and High levels of effort were achievable for subjects. Success rates were moderately higher in the Low-Requirement condition, although this difference did not reach significance. Thus, results from subsequent experiments can be interpreted in light of participants preferring Low- to High-Requirement and showing a modest, albeit non-significant, boost in performance in the Low-Requirement condition. While men and women did not show a difference in their preferences for the effort conditions, men performed significantly better than women across both effort conditions. As the task was disguised a video game, performance differences may have been due to men potentially having more favorable perceptions of the task.Experiment 2 used Low-Requirement trials of either Restricted- or No-Choice to test the effects of different levels of control  on preference and performance, with effort level held constant. In this experiment we sought to establish baseline preference and performance levels for Restricted- and No-Choice trials in the context of a low effort requirement. While the No-Choice condition offered only one blaster for participants to use, the Restricted-Choice conditions offered two blasters from the same effort requirement category (Low-Requirement in this experiment). Thus, the Restricted-Choice condition did not grant any actual control over effort requirements. However, control is often inferred even when individuals actually possess none . To furtSD = 5.09). Participants were randomly assigned to one of four counterbalanced sets of stimuli . Two stimuli sets were composed of only light colored blasters (light green and light orange) and two sets of only dark colored blasters (dark green and dark orange). Within all four sets, color-hue (green vs. orange) distinguished choice conditions  in a counterbalanced fashion, with two exemplar blasters in each choice condition. The Restricted-Choice condition was implemented by offering a choice between two blasters of slightly different colors that were both from the Low-Requirement condition. Importantly, choice in this condition did not confer any control over effort costs, however, this may not have been apparent to participants. In a single block there were 32 Low-Requirement trials: 16 Restricted-Choice and 16 No-Choice. Blocks repeated three times across the game, resulting in a total of 48 Restricted-Choice, Low-Requirement trials and 48 No-Choice, Low-Requirement trials.Thirty-four participants were recruited and completed the experiment  = -0.39, p = 0.699]. Across both the Restricted- and No-Choice conditions, preference ratings decreased from similar average pre-game ratings of 5.07 (SD = 0.92) and 5.16 (SD = 1.14) to similar post-game ratings of 4.66 (SD = 1.04) and 4.75 (SD = 1.38), respectively. For both conditions, the change in preference did not significantly differ from zero  and there was no difference in the pre- to post- change in preference between the Restricted- and No-Choice conditions. From before to after the game, preference for both conditions decreased approximately 0.06 of a point on a seven point Likert-type scale. There were no gender differences in preference, nor in performance for either condition.Figure 4B shows success rates for Restricted- and No-Choice trials in Experiment 2. Participants performed similarly in the two conditions, successfully completing 95% (SD = 7.9%) of No-Choice trials and 94.2% (SD = 9.9%) of Restricted-Choice trials .This experiment employed conditions of No-Choice and Restricted-Choice for the purpose of determining the impact of no control and illusory control, respectively, on preference for and performance on trials requiring only low effort levels. As illusory control can have a beneficial effect on valuation and intrinsic motivation , we hypoThe Variable Choice and Effort, Without Free Choice Game used Restricted- and No-Choice trials of both Low- and High-Requirement to test the combined influence of different levels of control and cost factors on preference and performance in a 2x2 design. Within this experiment we examined hypotheses two and three: that preference and performance ratings would favor conditions where greater control is perceived, and that low control but high effort would have the combined influence of diminishing motivational outcomes. As in Experiment 2, the Restricted-Choice condition did not confer any control over effort costs, as the blaster options offered in Restricted-Choice were always within the same effort category . Thus, the perception of control was free to subjectively vary, while actual control was effectively zero across both choice conditions. Although there was no effect of choice on preference or performance in Experiment 2, which involved only low effort requirements, we predicted that the addition of a High-Requirement condition might elicit a positive motivational effect from the Restricted-Choice condition. We specifically hypothesized that the contrast of having two levels of effort might increase the salience of personal control, as participants tried to exercise control to avoid the higher effort costs (in line with participant preference for Low-Requirement established in Experiment 1). As with all of the experiments, the choice and effort contingencies were obscured such that actual levels of control over effort costs and how these contingencies mapped onto blaster cues were somewhat ambiguous. For example, effort cost requirements (number of presses) for the two conditions were drawn from ranges that were consecutive to one another , thus making effort costs somewhat difficult to characterize. Such ambiguities in the task left a margin of uncertainty for participants to make inferences regarding the degree to which Restricted-Choice afforded control over effort costs. We predicted that participants would infer control in the Restricted-Choice condition, which would boost intrinsic motivation for effort trials in this condition. Thus, in line with our second general hypothesis, we predicted participants would show enhanced preference and performance for Restricted-Choice compared to No-Choice trials. In-line with hypothesis three, we also specifically anticipated that the additional effort requirements of the High-Requirement condition might undermine motivation, especially as high effort costs intersected with low levels of personal control. Thus, we predicted that the most severe decrements in motivational outcomes would be observed in the No-Choice, High-Requirement condition.SD = 4.83). Participants were randomly assigned to one of four counterbalanced sets of stimuli. Color-hue category (green vs. orange) distinguished levels of choice  and color-value category (lightness vs. darkness) distinguished levels of effort ; within these constraints, conditions were fully counterbalanced across color categories, creating four sets of blaster stimuli with two exemplar blasters in each of 2x2 conditions . Across a single block there were 16 Restricted-Choice trials (eight Low-Requirement and eight High-Requirement) and 16 No-Choice trials (eight Low-Requirement and eight High-Requirement). Blocks repeated four times across the game resulting in a total of 32 Restricted-Choice, Low-Requirement trials; 32 Restricted-Choice, High-Requirement trials; 32 No-Choice, Low-Requirement trials; and 32 No-Choice, High-Requirement trials.Thirty-three participants completed the experiment  to post-game  and preference for High-Requirement blasters decreased from pre-game  to post-game . Across all conditions, changes in preference were significantly different from zero . A 2x2 ANOVA of the change in preference data, indicated a main effect of effort , such that the increase in preference for Low-Requirement blasters was significantly different (using the Bonferroni-corrected significance threshold of p = 0.025) than the decrease in preference for High-Requirement blasters for both the Restricted-Choice  and No-Choice  conditions. However, there was no main effect of choice , as the Restricted-Choice and No-Choice conditions (collapsing across effort conditions) showed virtually no difference in this experiment . There was no interaction of the choice and effort conditions in the before to after game preference change . When gender was added as a between subjects factor in the ANOVA, there were no significant effects related to gender for either the preference or performance measures.Figure 5B shows success rates for all conditions in the Variable Choice and Effort Without Free Choice Game. Success rates were high (above 95%) for all four conditions . A 2x2 ANOVA indicated a main effect of effort , such that success rates for Low-Requirement blasters were significantly greater than success rates for High-Requirement blasters for the No-Choice condition  and this difference for the Restricted-Choice condition approached the Bonferroni-corrected significance threshold of p = 0.025 . However, there was no main effect of choice , as the Restricted-Choice and No-Choice conditions (collapsing across effort conditions) showed virtually no difference . There was no interaction of the choice and effort conditions .a priori hypothesis that the greatest motivational decrements would be observed in the No-Choice, High-Requirement condition, we also examined performance differences in this condition relative to the other conditions. While the decrement in performance in the No-Choice, High-Requirement condition was significant when compared to both No-Choice, Low-Requirement  and Restricted-Choice, Low-Requirement , the decrement was not significant in comparison to Restricted-Choice, High-Requirement .As we had an controlling their behavior rather than providing them with an opportunity for control, intrinsic motivation is undermined , and thus, experience enhanced motivation in this condition. We hypothesized that the varied effort requirements might create a direct contrast effect, making the choice condition more salient in Experiment 3 than in Experiment 2. Thus, in line with our second general hypothesis, we anticipated increased intrinsic motivation when participants were given a choice and, therefore, enhanced motivational outcomes for the Restricted- compared to No-Choice condition. However, this hypothesis was not supported. Only the effort, but not the choice condition had an effect on preference and performance. Replicating results from Experiment 1 and supporting our first general hypothesis, participants in Experiment 3 both preferred and were more successful on Low-Requirement compared to High-Requirement trials. Replicating results from Experiment 2, Restricted-Choice compared to No-Choice did not significantly affect preference or performance. Thus across Experiments 2 and 3, there was no evidence that the mere provision of choice, conferring only illusory control, had any impact on participants’ preference or performance. These findings are in line with studies demonstrating that mere choice, devoid of opportunities for personal control, does not enhance motivation . Furtherdermined .While we did not observe a differential effect between the choice conditions related to increasing effort requirements, it is possible that both effort conditions required an effort cost beyond some threshold at which the potential motivational benefits of choice are undermined–particularly when choices offer no real control. Thus, the null effect of choice in this experiment may be due to both low levels of personal control (in both the Restricted- and No-Choice conditions) and due to effort costs (across both effort conditions).While our second general hypothesis was not supported, performance results are consistent with our third general hypothesis, which posited that the greatest motivational deficits would occur when the lowest levels of personal control met the highest effort costs. Participants were least successful in the No-Choice, High-Requirement condition, suggesting a lack of control and effort costs may exert a conjoint influence to undermine motivation. To further parse the effects of personal control and effort costs on the motivational consequences of choice, future studies should examine the effects of differing levels of control across a greater range of effort costs, including a no-effort condition. Nonetheless, results from the current study suggest that in the context of effort requirements, a choice conferring only illusory control may not sufficiently bolster perceptions of personal control or override decision-related costs to enhance motivation.The Variable Choice and Effort, With Free Choice Game was similar to Experiment 3, but introduced a new choice condition: Free-Choice. In the Free-Choice condition, participants were given a choice between one Low- and one High-Requirement blaster and were allowed to freely choose which they preferred to use. Thus, Experiment 4 used Free-, Restricted- and No-Choice trials of both Low- and High-Requirement to test the combined influence of different levels of personal control and effort costs on preference and performance. As before, the Restricted-Choice condition did not offer any actual control over effort requirements; however, the Free-Choice condition did. We sought to determine whether the provision of Free-Choice would increase perceptions of control to enhance intrinsic motivation for freely chosen effort trials. In line with our second general hypothesis, we generally predicted that motivational outcomes would be enhanced correspondent to the level of control conferred by choice (Free-Choice > Restricted-Choice > No-Choice). However, choice conditions could not be fully dissociated for the preference data, because the Free- and Restricted-Choice conditions utilized the same set of blaster cues . Thus, preference could only be determined for the collapsed Choice condition (encompassing both Free- and Restricted-Choice). Therefore, for preference data, we hypothesized that Choice trials would be preferred over No-Choice trials. Furthermore, as the only difference between the two conditions comprising the collapsed Choice condition was whether the blasters options presented were from the same (Restricted-) or different (Free-) effort categories, we further anticipated that perceptions of control elicited by Free-Choice trials would generalize to the Restricted-Choice trials as well. Therefore, for the performance data, we hypothesized that we would observe enhanced performance in both Free- and Restricted-Choice conditions. In line with our third general hypothesis, we also predicted that any observed deficits in motivational outcomes would occur at the intersection of the lowest levels of personal control (only No-Choice in this experiment) and the highest effort costs (High-Requirement).SD = 4.21). Participants were randomly assigned to one of four counterbalanced sets of stimuli. As in Experiment 3, conditions were fully counterbalanced across color categories, creating four sets of blaster stimuli . In terms of the blaster stimuli, this Experiment only differed from Experiment 3 in that the color-hue category (green vs. orange) representing Choice, represented both the Free- and Restricted-Choice conditions together. Across a single block there were 16 No-Choice trials (eight Low-Requirement and eight High-Requirement) and 16 Choice trials. Within the Choice trials, there were eight Restricted-Choice trials (four Low-Requirement and four High-Requirement) and eight Free-Choice trials (in which participants could freely choose Low- or High-Requirement). Blocks repeated four times across the game resulting in a total of 32 No-Choice, Low-Requirement trials, 32 No-Choice, High-Requirement trials, at least 16 Choice, Low-Requirement and at least 16 Choice, High-Requirement trials, plus an additional 32 Free-Choice trials (with effort level dependent on participants’ choices).The final sample, with one participant excluded for not following directions, totaled 33 participants  to post-game  and preference for High-Requirement blasters decreased from pre-game  to post-game . Across all conditions, changes in preference were significantly different from zero . A 2x2 ANOVA of the change in preference data, indicated a main effect of effort , such that the increase in preference for Low-Requirement blasters was significantly different (using the Bonferroni-corrected significance threshold of p = 0.025) than the decrease in preference for High-Requirement blasters for both Choice  and No-Choice . However, there was no main effect of the choice  and no interaction of the choice and effort conditions in the preference change . When gender was added as a between subjects factor in the ANOVA, there were no significant effects related to gender for either the preference or performance measures.Figure 6B shows success rates for all conditions in the Variable Choice and Effort With Free Choice Game. Success rates were high (above 93%) across all six conditions . The 3x2 ANOVA including all three choice conditions and both effort conditions did not reveal any significant differences among the conditions .We also decided to analyze the performance data in a 2x2 fashion, with Free-Choice trials excluded. This analysis was performed for several reasons: (1) we wanted to perform an analysis comparable to that in Experiment 3; (2) treating Free-Choice and Restricted-Choice as separate conditions may not have accurately reflected participants’ experience of the game as both types of choice were implemented utilizing the same set of blaster cues and thus may have been indistinguishable to participants; (3) collapsing Free- and Restricted-Choice into a single Choice condition was not appropriate as the Free-Choice condition involved a higher percentage of Low-Requirement trials than the two other choice conditions ; and (4) average success scores were computed from fewer data points in the Free-Choice, High-Requirement condition as participants tended not to choose High- blasters when given a Free-Choice and three subjects never chose a High- blaster during a Free-Choice trial. Thus, performance was analyzed in a 2x2 fashion, excluding Free-Choice trials. While this analysis paralleled the one performed in Experiment 3, the key difference here is that the Free-Choice condition may have still exerted an influence on the context in which participants experienced Restricted-Choice in Experiment 4.F = 5.57, p = 0.025]. While success rates for Low-Requirement blasters were greater than success rates for High-Requirement blasters, post hoc pairwise comparisons did not reach significance for either of the Choice  or the No-Choice  conditions.The 2x2 ANOVA with factors of Restricted- and No-Choice and Low- and High- Requirement revealed a main effect of effort , such that success rates for the Choice condition were significantly greater than success rates for the No-Choice condition for High-Requirement , while this difference was not significant for Low-Requirement . There was no interaction of the choice and effort factors .Further, the 2x2 ANOVA revealed a main effect of choice , this decrement in performance did not reach the Bonferroni-corrected significance threshold of p = 0.017 when compared to No-Choice, Low-Requirement .As we had an This experiment employed conditions of No-Choice, Restricted-Choice, and Free-Choice across both Low-Requirement and High-Requirement trials to determine the combined impact of varying both costs and control on motivational outcomes. The design of Experiment 4 was similar to Experiment 3, except that only half of the Choice trials were Restricted-Choice trials (split evenly between Low- and High-Requirement), and the other half of the Choice trials were Free-Choice. Free-Choice trials offered participants a choice between a High- and a Low-Requirement blaster and they freely chose which they preferred to use. Given the Restricted-Choice condition in Experiments 2 and 3 did not enhance preference or performance, we sought to determine whether the provision of Free-Choice, a choice that did actually confer control over effort costs, would enhance intrinsic motivation. In line with our second general hypothesis, we predicted that outcomes would be enhanced correspondent to the level of control conferred. While preference data did not support this hypothesis, it did replicate the findings from Experiments 1–3, with an increase in preference for Low-Requirement cues, a decrease for High-Requirement cues, and no apparent effect of Choice (representing both Free- and Restricted-Choice) on preference.Performance data, however, did demonstrate main effects of not only effort but also choice. While the 3x2 analysis of success rates did not yield significant effects, this may have been due to the two choice conditions being indistinguishable to participants as they were both represented by the same blaster cues. Thus, we also analyzed performance data in a 2x2 fashion, excluding Free-Choice trials, in order to have an analysis comparable to that in Experiment 3 and to remove the low effort advantage conferred by Free-Choice. Results from this analysis replicated results from Experiments 1 and 3, and supported our first hypothesis, with participants performing significantly better on Low- than High-Requirement trials. Interestingly, while effort effect sizes in Experiment 3 (without Free-Choice) were moderate to large, effort effect sizes in this experiment (that did include Free-Choice) were small to moderate, suggesting that in the context of Free-Choice, the magnitude of effort’s effect was reduced.Importantly, results from this analysis also supported our second hypothesis that intrinsic motivation and thus motivational outcomes would be enhanced correspondent to the level of control conferred by the choice condition. Experiment 4 was the only experiment to offer actual control over effort costs and the only experiment in which participants performed better on effortful trials in which they were given a choice compared to those in which they were not. Interestingly, the choice effect sizes for performance in this experiment were comparable to the effort effect sizes. This suggests that in the context of Free-Choice, effort and choice may have a similar effect magnitude on performance. This positive motivational effect of choice was present even when Free-Choice trials were excluded. Thus, even when the lower effort advantage conferred by Free-Choices was removed from the analysis, the motivational effect of personal control, uniquely offered in this experiment, still had a positive impact on the illusory control condition, a previously unmotivating condition.Thus, in contrast to Experiments 2 and 3, in which Restricted-Choices were offered alone and there was no positive effect of choice, the results of Experiment 4 suggest that the provision of occasional free choices may be sufficient to provide motivational benefits in an effortful, and otherwise uncontrollable context. While participants were not more successful on Free-Choice trials than on Restricted-Choice trials, this may have been due to both the Restricted- and the Free-Choice trials utilizing the same set of blaster stimuli. Given the subtle differences between these two Choice conditions, it is possible that participants may not have explicitly distinguished between Free- and Restricted-Choice conditions, and rather, perceived all the choices as conferring some control over effort costs.In line with our third hypothesis, and consistent with results from Experiment 3, we again observed the lowest levels of performance in the No-Choice, High-Requirement condition, again supporting the notion that low levels of personal control and high levels of costs may combine to undermine motivation . While cThe present research attempted to disambiguate how control and cost factors affect the motivational consequences of choosing, across differing levels of personal control, within an effortful task. Across four experiments we tested the hypotheses that intrinsic motivation and thus motivational outcomes would be: (I) enhanced when lower compared to higher effort was required, (II) enhanced correspondent to the level of control conferred by choice, and (III) diminished when the lowest levels of personal control intersected with the highest effort requirements. These hypotheses were generally, although not always specifically supported.Our first hypothesis was premised upon effort discounting theory, which holds that effort decreases the utility of related outcomes . Thus, wOur second hypothesis, that intrinsic motivation and thus motivational outcomes would be enhanced according to the level of control offered by the choice condition was generally supported, although the threshold for an effect of control was not what was predicted. As much research has demonstrated the motivational benefits of personal control and even of illusory control , 2015, wBased on evidence that low levels of personal control and heightened costs associated with a decision may combine to produce a particularly damaging coalition, our third hypothesis predicted that we would observe the largest decrements in motivation when personal control was at its lowest and cost was at its highest. This hypothesis was largely confirmed in both of the experiments testing control and cost together (Experiments 3 and 4), suggesting that the combined effect of low personal control and a context of high costs can lead to diminished intrinsic motivation.Another interesting effect to emerge was that, while illusory control alone did not enhance intrinsic motivation, the positive effects of free choice appeared to generalize from the real control condition to the illusory control condition. That is, when real control was offered, there was enhanced performance in illusory control trials, even when the direct influence of Free-Choice trials (and the low-effort advantage conferred by Free-Choice) was removed. This suggests conditions of illusory control can be contextualized by the opportunity for real control, enhancing motivational effects of illusory control when it otherwise might not be associated with increased motivation.Together the pattern of results across all of our experiments provides support for our overarching hypotheses, suggesting that personal control provides benefits to intrinsic motivation while effort costs undermine these benefits. Further, our results suggest there may be interplay between control and cost factors that may influence the motivational consequences of making choices. For example, our study demonstrated that under conditions of effort costs, low levels of personal control might not override these costs to boost intrinsic motivation in a decision making context. This is consistent with work suggesting there may be a framing effect for decisions such that choice may lose its desirability and advantageous features in the context of loss frames and high costs . For exaThe present study also had several limitations. First, while we directly manipulated personal control across our choice conditions, no self-report manipulation checks were included and, thus, we can only infer that it was indeed perceptions of personal control that led to the motivational effects observed in Experiment 4. Future work should include self-report measures to ensure that perceptions of personal control match corresponding manipulations of control. Second, in future work, measures of motivation should be refined and potential modulating factors should be tested. For example, while we used preference and performance as measures of motivation across conditions, we did not include a self-report measure of intrinsic motivation for the different choice and effort conditions. Self-reported intrinsic motivation may have more directly addressed study hypotheses. Further, the self-relevance of task goals has been demonstrated to be an important factor in determining motivation and effort exertion . While tThe current work has significant implications for the study of personal control and effort. Across fields such as counseling, motivation science, and education, there is a common fundamental goal of enhancing intrinsic motivation to equip individuals to tackle effortful personal challenges. While it is often not possible or not desirable to alter the level of effort required to achieve a goal, it may be possible to enhance intrinsic motivation to meet effort requirements. Our results suggest that when effort requirements are high, bolstering the experience of personal control to boost intrinsic motivation may require the experience of real control in order to overcome effort costs.Our study provided evidence that a context of physical effort costs may negate the benefits of making some choices. Future research should explore whether the cognitive effort demanded by more computationally difficult choices e.g.,  underminWhile the beneficial effects of personal control have been repeatedly demonstrated across domains from performance on simple tasks and educational activities to coping with stress e.g., , many stHS-T and ET conceptualized and designed the study. Data collection and analysis was completed by HS-T under the supervision of ET. All authors contributed to the interpretation of experimental results. HS-T drafted the manuscript and ET and JR provided critical revisions. All authors have approved the final version of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


I, an inhibitor of PKR. VAI consists of three base-paired regions that meet at a three-way junction; an apical stem responsible for the interaction with PKR, a central stem required for inhibition, and a terminal stem. Recent studies have highlighted the potential importance of the tertiary structure of the three-way junction to PKR inhibition by enabling interaction between regions of the central and terminal stems. To further investigate the role of the three-way junction, we characterized the binding affinity and inhibitory potential of central stem mutants designed to introduce subtle alterations. These results were then correlated with small-angle X-ray scattering solution studies and computational tertiary structural models. Our results demonstrate that while mutations to the central stem have no observable effect on binding affinity to PKR, mutations that appear to disrupt the structure of the three-way junction prevent inhibition of PKR. Therefore, we propose that instead of simply sequestering PKR, a specific structural conformation of the PKR-VAI complex may be required for inhibition.Highly structured RNA derived from viral genomes is a key cellular indicator of viral infection. In response, cells produce the interferon inducible RNA-dependent protein kinase (PKR) that, when bound to viral dsRNA, phosphorylates eukaryotic initiation factor 2α and attenuates viral protein translation. Adenovirus can evade this line of defence through transcription of a non-coding RNA, VA RNA-dependent protein kinase (PKR) is a key interferon-stimulated enzyme involved in the innate immune response to viral infection. PKR is a Ser/Thr kinase that consists of tandem copies of a conserved double-stranded RNA binding motif  at the N-terminal domain, and a C-terminal kinase domain . PhosphoI) that accumulates during the late stages of infection to inhibit PKR  prior to DLS analysis. After dialysis, samples were subjected to filtration through a 0.1μm filter  and equilibrated at 20°C. The hydrodynamic radius rH and homogeneity (distribution of rH) of each sample was examined using the Zetasizer Nano S system  equipped with a 4 mW laser (λ = 633 nm) as previously described . ΔC. ΔC2 valab initio SAXS envelopes, which was intriguing given that these are the only RNAs that demonstrated inhibition of PKR , where residues in the loop of the CS base-pair with a stretch near the 3'-end of the RNA allowing a pseudoknot conformation. Based on the computational models from 2 value 2.36) and Δ2bp  display similar overall structures and are in good agreement with the low-resolution SAXS reconstructions. The imperfect fit to the SAXS curves is most likely a result of the vast reduction of the model’s degrees of freedom . Any attempts to introduce the pseudoknot as an additional restraint for Δloop, Δbulge, Δ4bp resulted in obtaining even higher χ2 values and structures that did not fit well to the low-resolution SAXS reconstructions for these models. Together, these results suggest that inhibition may require a specific orientation of the 3wj to enable pseudoknot formation.However, preliminary computational models of wt RNA, Δ2bp and ΔGU demonstrated significant deviation from the n of PKR . To deten of PKR  to enabln of PKR . The res1-169 complex in the absence of Mg2+ where PKR1-169  and wt RNA interact in a side-by-side orientation that involves entire length of PKR1-169 [2+ [1-169 complex in the presence of 5 mM MgCl2 (Buffer 1) to determine the effect of Mg2+ ions on overall structure and stability of the complex. The pair distribution function in the presence of Mg2+ is very similar to that previously determined in the absence of Mg2+ [ab initio models shows a one-to-one complex that is consistent with the previously observed solution conformation  as representative of an RNA mutant with a disrupted 3wj conformation  and CS mutants to improve our understanding of PKR inhibition. Binding affinity of these RNAs for PKR were not drastically different and consistent with previously published data [The antiviral properties of PKR are well established . Virusesding RNA . The strociation –14, 19. tructure , 52. Ison of PKR , 14, 19.hed data , 55. Theet. al. [I, is responsible for inhibition of PKR. CS stem length has also been shown to be crucial for PKR inhibition [Overall, the low-resolution structures of wt RNA and mutants in the presence of divalent cation support the formation of a pseudoknot requiring a specific structural 3wj orientation. This pseudoknot was originally proposed by Ma et. al.  and lateet. al. , 52. Inshibition . We demoab initio envelope, computational models predicting the pseudoknot structure  may adopt a pseudoknot conformation in the presence of Mg2+, and our results support pseudoknot formation under similar conditions . Based on our observations, the solution conformation of wt RNA in the presence of Mg2+ (Dmax relative to our previously determined SAXS solution structure of wt RNA in the absence of Mg2+ [2+, whereas a perpendicular orientation of the CS and AS are observed in the absence of Mg2+.It has been reported that Mgg2+ ions , 52. Laug2+ ions  have pre of Mg2+ , yellow  of Mg2+ . CompariI in PKR inhibition, the length of the central domain structure as well as its nucleotide composition are very important for the stability and the function of VAI as an inhibitor of PKR. In summary, our results suggest the importance of studying such structural determinants in other non-coding RNA inhibitors of PKR. Studies are currently underway to test the importance of the 3wj in a cellular context.Taken together, our results support the role that the formation of a specific structural conformation of the 3wj plays in inhibition of PKR. Overall the data presented here show that in addition to the previously proposed role of the central stem-loop of VA


Acropora tenuis larvae in fine-mesh enclosures on each of four larval-enhancement plots, comprising natural reef substrata and ten settlement tiles, on degraded reef areas in the northwestern Philippines. Initial mean total settlement on tiles in larval-enhancement plots was high (255.3 ± 68.6), whereas no larvae settled in natural control plots. Recruit survivorship began stabilising after five months, with juveniles becoming visible by eye at nine months. After three years a mean of 2.3 m−2 colonies survived within each larval-enhancement plot. Most colonies grew rapidly (16.1 ± 0.7 cm mean diameter) and spawned successfully at three years, thereby quickly re-establishing a breeding population. In contrast, natural recruitment failed to produce any new visible A. tenuis colonies. These results demonstrate that mass larval settlement can rapidly enhance recruitment and coral recovery on degraded reef areas, and provides an important option for active reef restoration where larval supply and recruitment success are limiting.Reef-building corals have essential roles in reef ecosystems but are highly susceptible to disturbances. Increasing anthropogenic disturbances are eroding coral community resilience, leading to declining reef ecosystem function and status globally. Successful reproduction and recruitment are essential for restoring coral populations but recruitment-limitation can constrain recovery. We supplied ~400,000  More than 60 percent of the world’s coral reefs are degraded or seriously threatened with loss in coming decades7. Human threats to coral reefs are particularly severe in the Coral Triangle region that encompasses the global center of marine biodiversity8. It is estimated that nearly 95 percent of coral reefs in the Southeast Asia region are threatened by human activities and about 50 percent of these reefs are in the high or very high threat categories, with Indonesia and the Philippines having the largest areas of threatened reefs7. Reef degradation directly threatens the extraordinary biodiversity and immense ecological and socio-economic values of reefs that support many millions of people9. Although it is not currently feasible to restore all degraded reefs, mitigating the causes of degradation and initiating restoration of corals on reefs that can be managed are becoming increasingly urgent.Coral reefs are facing intensifying anthropogenic impacts11. However, ongoing declines in coral abundance and reproductive biomass can lead to loss of breeding colonies and decreasing reproductive success  resulting in recruitment limitation or failure14.Successful reproduction and recruitment are critical for the maintenance and recovery of scleractinian coral species and underpin reef resilience16. Most coral restoration studies have used smaller-scale asexual fragmentation of adult colonies in combination with various transplantation and nursery techniques, but these are relatively labour-intensive and expensive18. Fragmentation damages donor colonies and increases disease risk, and most coral fragmentation studies have used fragments with limited genetic diversity which constrains their resistance to future stress disturbances19. Alternatively, mass larval enhancement using millions of sexually-derived larvae resulting from large-scale spawning events of highly fecund corals21 has strong potential for scaling-up restoration efforts to larger reefal scales10. Mass larval enhancement also has significant advantages of providing greater genotypic diversity among recruits that is likely to improve adaptive and evolutionary potential22 and increase resistance to future disturbances23, thereby strengthening reef community resilience and recovery rates.Active coral restoration can enhance coral cover and abundance, but current restoration methods have not yet been effective at larger scales needed to halt or reverse the decline in reef coral communitiesin situ field trials using mass coral larval settlement have been published, and both studies used reef areas adjacent to healthy coral communities in Marine Protected Areas25. Heyward et al.24 used Acropora larvae sampled from coral spawn slicks and reared in small 1.8 m floating culture ponds to ‘seed’ small 1.8 × 1 m reef areas at Ningaloo Reef, Western Australia24. After four weeks more than 6,500 acroporid coral recruits were growing on conditioned terracotta tiles in the larval ‘seeded’ areas, whereas settlement on control tiles and natural recruitment rates were up to 100-fold lower24. However, subsequent survival or growth of the recruits was not monitored. Hence, longer-term recruitment outcomes from this study were not determined. In Palau, ~1 million ex situ cultured Acropora digitifera larvae were used to ‘seed’ replicate concrete pallet balls for 24 hours resulting in significantly higher acroporid recruitment on fibre-cement tiles attached to ‘seeded’ pallet balls compared with unseeded control tiles after 5 weeks25. Monitoring after 30 weeks and ~13 months showed no significant differences in acroporid recruit densities between ‘seeded’ and control treatments due to high post-settlement mortality of A. digitifera recruits and high rates of natural acroporid coral recruitment in this healthy reef system25.To date, results from only two small-scale Acropora tenuis recruits on coral recruitment tiles and natural reef substrata for three years.Here, we evaluate for the first time the longer-term effects of mass coral larval settlement directly on degraded reef areas in the northwestern Philippines where coral cover is low and natural recruitment is severely limited. Specifically, we demonstrate the effectiveness of using mass larval enhancement to initiate coral recovery by monitoring settlement, survival, growth and onset of sexual reproduction of This experiment was designed to test the effect of supplying large numbers of coral larvae on replicate degraded reef areas during a five-day larval settlement period to quantify initial larval settlement and longer-term recruitment outcomes. Long-term monitoring over three years enabled subsequent patterns of post-settlement survival, growth and onset of sexual reproduction of coral recruits in larval enhanced plots versus control plots without larval provision to be compared.26. However, reef status and water quality in the Lingayen Gulf have been declining since the early 1980s, coinciding with increased fishing pressure including destructive blast fishing, and aquaculture development27. Live coral cover on reefs in Bolinao declined from about 41% mean cover in the late 1908s to less than 22% mean cover by 199927. The coral community28 in the Magsaysay reef area was subsequently severely impacted by a Crown-of-thorns starfish outbreak in 2007  and previous extensive blast fishing which no longer occurs27. For the present study, a total of eight 6 × 4 m reef plots (about 2–4 m apart) were haphazardly selected on the reef and demarcated using steel bars. Four randomly selected plots were treated with larvae  and the other four plots were controls – without provision of larvae. Prior to the larval enhancement experiment, photographs of the benthic communities within each plot were taken using a 1 × 1 m frame and analysed using CPCe29 to quantify benthic cover of corals and other benthos and to determine the reef community status in the area. A total of 10 random points were generated and the benthic category underlying each point was scored in each of the 24 frames taken in each of the plots. There were no A. tenuis adult colonies or visible recruits from previous spawning events present inside the plots prior to the larval enhancement activity.Prior to the larval enhancement experiment, degraded reef areas at 3–4 m depth were identified at the Magsaysay reef in the Lingayen Gulf, Anda, Pangasinan, Northern Luzon, Philippines  were deployed inside each of the eight plots just prior to the larval enhancement experiment to quantify the initial settlement rates of the A. tenuis larvae. Each tile was identified with a coded tag, and was attached onto a stainless steel post projecting from a base plate attached to the reef, with a 0.5 cm gap above the base plate to enable larval settlement on all surfaces30. Dead table Acropora pieces used to produce the tiles were collected from the intertidal zone beside Cory reef rubble bar near the experimental site  file that was further refined using 3D modelling software . The surface area of the 3D image of the tiles was analysed and calculated using netFabb® software. As there was minimal variation in the surface area of the scanned tiles (mean: 360 ± 3.7 SE cm2), larval settlement and recruitment was expressed as numbers of recruits per tile.To estimate the total surface area of the irregular surfaces of the tiles, 30 representative tiles were 3D scanned using a DavidAcropora tenuis (minimum diameter of 15–20 cm) were collected from the reef ~2–4 m deep in Caniogan, Anda, Pangasinan  were collected for use in this experiment. Gamete collection and larval culture followed standard methods with minor modifications40.Colonies were maintained in a healthy condition in concrete tanks with flow-through seawater and aeration and were monitored periodically at night to check for setting and spawning behaviours41. After 1 hour, seawater containing excess sperm was siphoned off (sperm-washing) beneath the floating eggs and new filtered seawater was slowly added. This process was repeated three times to remove excess sperm that may degrade water quality during larval culture and to prevent polyspermy41. Subsamples of embryos and eggs were collected and examined after a further hour under a stereomicroscope to determine percentage fertilization.Many thousands of spawned egg-sperm bundles were skimmed off the water surface and transferred to a fertilization container in 10 L of 1 µm filtered seawater. Gamete bundles were gently agitated to facilitate egg and sperm separation and to maximize subsequent cross-fertilization−2 for 24 h. To maintain embryos and larvae in a healthy condition, aeration was supplied 24 h after fertilization and 50 L of new filtered seawater was added daily to tanks with developing larvae. Spawned coral colonies were returned in a healthy condition and reattached onto the reef using underwater adhesive (a mixture of cement and epoxy).Developing embryos were transferred into 13 large rearing tanks  at water surface densities between 4–5 embryos cmAt 4 d post-fertilization, competent actively swimming larvae were collected using a plankton net sieve (60 µm mesh size) and transferred to a holding basin. The total number of larvae was estimated using three replicate 60 ml sub-samples taken from the basin after the larvae had been concentrated and thoroughly mixed throughout the water column to distribute them evenly, and then the larvae were distributed equally into twelve strong 40 × 50 cm 20 L plastic bags. Oxygen was supplied into each bag before it was sealed for transport to the field for the larval enhancement experiment at Magsaysay reef.A. tenuis larvae whose diameters are 300–500 µm42. Cylindrical 20 g weights (1.75 × 4 cm) were inserted along the lower edge of the mesh to firmly hold the mesh enclosure in place on the reef and prevent larvae from escaping during the settlement period. The mesh enclosures covered the top and sides of each site and extended up to 75 cm from the reef with the bottom open to the reef to enable larvae to settle, and the corners were secured using steel reinforcing bars driven into the substratum. An estimated 410,670 ± 26,354 larvae were introduced into the mesh enclosure on each of the four larval-enhanced plots through re-sealable openings distributed over the upper areas of the mesh enclosures. Control plots were also covered by organza-hapa mesh enclosures during this period but no larvae were introduced. All living corals inside the plots (between 10 – 30 colonies up to 80 cm diameter per plot) were carefully covered with thin plastic mesh with 1 cm mesh openings to avoid the mesh enclosures from becoming caught and ripping and damaging the coral tissues.Organza-hapa mesh enclosures were used to retain the larvae inside the four larval enhancement plots during the 5-day larval settlement period. The organza cloth (100–150 µm mesh opening) was sewn to form 6 × 4 m enclosures, with hapa nylon net (500 µm mesh) as a second, outer layer for additional support. This mesh enclosure assembly can effectively retain Five days after the provision of larvae inside the mesh enclosures, the enclosures and the protective plastic mesh around living corals were removed from all plots. All settlement tiles were then carefully collected and kept submerged in clean seawater while carefully taken to the BML laboratory to monitor the initial numbers of settled coral spat on each tile under a stereoscope while the tiles remained submerged in seawater. Each coral recruit was recorded and carefully mapped to facilitate repeated monitoring at 2, 5, 9, 12, 15, 17, 19, 21, 23, 31, 34 and 35 months after settlement. The tiles were returned immediately to their specific location and correct orientation within each experimental plot after each monitoring period. Where required, small clumps of algae were periodically removed from the tiles to avoid recruits being overgrown by algae.43. Corals are initially small and invisible cryptic recruits and subsequently grow to become larger visible colonies that are often referred to as juveniles44. Acropora tenuis recruits that had settled on natural reef substrata inside the larval-enhanced plots became visible underwater 9 months after the larval enhancement activity. The concurrent size of the juvenile A. tenuis on the recruitment tiles and their characteristic morphology were used as the basis for identification of A. tenuis juveniles on natural substrata inside the plots that originated from the larval enhancement experiment. Identified A. tenuis juveniles on the natural reef substrata were mapped and tagged with a numbered tag for growth and survivorship monitoring together with the juveniles on the recruitment tiles for 36 months after the larval enhancement. Juvenile colonies on the natural reef substrata were repeatedly monitored at 9, 12, 15, 17, 19, 21, 23, 31 and 35 months after settlement.The process of larval settlement includes the initial attachment of competent larvae onto substrata and their metamorphosis into a coral polyp (spat), while recruitment refers to the subsequent life stage of the coral following post-settlement survival and growth to become new members of the populationIn situ growth monitoring commenced at this time, with the length (l), width (w) and height (h) of each of the juvenile corals on recruitment tiles and natural substrata measured using calipers. Mean planar diameter was calculated from the maximum and minimum diameters measured for each colony. The ecological volume (EV) was calculated using the volume formula: EV = πr2h, where r = (l + w)/445.Acropora coral skeleton recruitment tiles quarterly in each of the control plots for 24 months from May 2013 to May 2015. Tiles were biologically conditioned for one month in tanks with flow-through seawater and aeration at the BML aquaculture facility then carefully transported in seawater to the experimental site. Tiles were labelled and attached to individual posts in the same manner as tiles used for the larval settlement experiment, described above. The recruitment monitoring periods encompass the known coral spawning periods in Bolinao-Anda reefs from February to July, with extended planulation periods throughout the year for brooding pocilloporid and poritid coral species37 . Retrieved tiles were bleached in 10% sodium hypochlorite solution for at least 48 hr and then air-dried. Tiles were examined under a stereomicroscope and each coral spat recorded was categorized as acroporid, pocilloporid, poritid, others, or unidentified (for broken and damaged unidentifiable recruits) based on their skeletal morphology46.Background natural recruitment rates in the restoration site were monitored by deploying and replacing ten 10 × 10 cm natural A. tenuis colonies that recruited onto coral tiles and natural reef substratum was monitored by carefully breaking and replacing small branches to determine the presence of gametes35. Monitoring was done at age 10, 20 and 34 months prior to the predicted spawning periods after 1, 2 and 3 years of growth. After two years of growth, the A. tenuis colonies resulting from settled larvae ranged in size from 3 to 23 cm mean diameter, with an overall mean diameter of 11 (±1) cm, which was close to the minimum size threshold recorded for Acropora colonies to become sexually reproductive33. At 35 months the volume of all gravid and non-reproductive colonies was measured to determine the size and age of onset of sexual reproduction. At 36 months spawning of the gravid A. tenuis colonies was monitored in situ after the April 2016 full moon.The onset of sexual reproduction in 16. The costs were categorised and then summed for all materials and infrastructure, boat hire and fuel, diving and labour for gravid coral collection (using different wage rates for different skill levels), spawning and larval rearing, site preparation and capital costs for the larval mesh enclosures 47. Sediment traps were deployed for 24 h periods in the larval enhancement site. Water motion was measured using the clod card technique48. Three clod cards were deployed for 24 h per plot, and in the BML seawater aquaculture tanks. Clod cards were weighed prior to deployment and after retrieval, and the weight lost was used as an indicator of the water motion.Sea Surface Temperature (SST) data from 2013 to March 2016 were obtained from coralreefwatch@noaa.gov, and these data were supplemented with Data are reported as mean values ± standard error. The four larval enhancement plots and the four control plots were used as statistical replicates (N = 4), with data from the ten tiles in each plot averaged to quantify mean initial settlement rates, and subsequent growth and number of surviving recruits at age 35 months. Analysis of similarities (ANOSIM) was performed using PRIMER v6 to test for similarities and significant differences in the percent benthic cover composition  between larval enhancement and control plots before the larval experiment. Benthic cover data did not require transformation and similarities were calculated using the Bray-Curtis similarity measure. Significant differences in the initial larval settlement patterns on different tile surfaces on tiles from the larval-enhanced plots after five days of larval settlement were tested using One-way ANOVA. Tukey’s HSD test was conducted post hoc to determine significant differences in settlement patterns among tile surfaces.49. The same analysis was used to determine any significant difference in survival patterns of juvenile corals on natural substrata and on tiles from nine to 35 months after the larval enhancement. In one case multiple recruits that had settled close to each other on the reef substratum subsequently grew together and fused to form a single colony, and this was considered as a ‘censored observation49. Significant increases in growth of juvenile corals through time were determined using Repeated Measures ANOVA. One-way ANOVA was used to compare growth rates of juvenile corals on recruitment tiles versus growth rates on natural substrata, and to compare the rates of natural recruitment of acroporids on settlement tiles in the study area versus recruitment of A. tenuis from the larval enhancement activity.Survivorship of coral recruits on different surfaces of tiles was analysed using survival analysis, a non-parametric pairwise comparison test based on the Kaplan–Meier functionR: 0.04, P = 0.40, ANOSIM) in the four replicate larval enhancement plots and the four control plots  biologically conditioned natural Acropora skeleton tiles that were attached to reef surfaces in the larval enhancement treatment plots . The mean total number of surviving recruits on the ten tiles in each settlement site after 35 months was 3.3 (±1.0), which equates to 9 colonies (±2.6) per m2 of tile surface. The mean number of surviving A. tenuis colonies on the natural reef substrata in each settlement site after 35 months was 16 (±5.7), which equates to 2.3 new colonies developing from larvae on each m2 of the approximately 7 m2 of available reef substrata in each of the larval settlement plots  cm3 mo−1 and were similar  to growth rates of recruits that settled on tiles  than natural acroporid recruitment during any of the monitoring periods .A total of 4,197 coral spat were recorded settled on 307 natural recruitment tiles deployed for three-month periods over two years. Natural juvenile coral recruitment was dominated by pocilloporids 88.5%), with very minor recruitment of acroporids (2.7%), poritids (2.3%), and other recruits (3.8%), and 2.8% of recruits were broken and damaged and therefore unidentifiable  in Northern Luzon since the early 1980seef Fig.  and othe55 including corals62. The sources of early juvenile mortality could not be determined but are likely to include competition, overgrowth and potential allelopathic effects of algae59, competition with other reef invertebrates64, herbivore grazing and carnivore predation67, sedimentation and eutrophication69 and stochastic disturbances50. In addition, some coral recruits may have died after the first settlement census due to insufficient energy reserves remaining after settlement and initial skeletogenesis58, prior to the uptake and establishment of mutualistic Symbiodinium communities and the supply of photosynthetic energy-rich compounds70.The results of this study demonstrate for the first time that mass larval supply resulting in enhanced rates of settlement directly onto badly degraded reef areas can significantly increase coral recruitment. As expected the highest rate of juvenile mortality occurred during the first few months after settlement, resulting in a Type III survivorship curve typical of many broadcast spawning marine invertebrates72. High survival of recruits occurred after nine months with only five colonies that settled on tiles dying between 24 and 32 months post-settlement. Of these, only one of the A. tenuis colonies derived from the larval enhancement experiment bleached and died during the coral bleaching event at 26 months after settlement. Similar low rates of bleaching were observed among other Acropora colonies near the experimental site, with highest levels of bleaching in pocilloporid and fungiid corals. Predation by muricid Drupella corallivores resulted in the death of four of the larger colonies growing on tiles during December 2015, but no colonies growing on reef substrata were preyed upon. Predation by Drupella was also recorded to have reduced survival of some transplanted A. millepora juveniles on another BARC reef in a previous study67.In contrast to some other field studies that were terminated due to low or no recruit survival, longer-term repeated monitoring during the present study showed remarkably high survivorship and growth of recruits occurred after five to nine months until 35 months Fig. . This paA. tenuis colonies resulting from settlement of reared larvae were similar on the natural reef substrata and coral skeleton tiles  compared to cultured A. tenuis that were settled on artificial substrata in an outdoor hatchery, and subsequently transplanted after 18 months to reef habitats in subtropical Akajima Island, Okinawa, Japan (4 cm yr−1)73. Corals from the present study also grew faster than hatchery cultured A. millepora colonies where sub-adults were transplanted onto BARC reef outcrops (4.6 cm yr−1 mean diameter) or maintained in in situ nurseries for 3 years (4 cm yr−1 mean diameter)74. The relatively rapid growth rates of corals in the present study indicate the environmental conditions at Magsaysay reef are suitable for at least some corals, and that the surviving colonies are adapted to these conditions. Rapid growth may also have been enhanced by larvae selecting suitable natural microhabitat refuges and microbial communities directly on the reef, whereas larvae settled in ex situ or in situ nurseries will experience different physico-chemical and microbial conditions compared with those experienced by juvenile or sub-adult corals subsequently transplanted to different reef sites74. This may cause some transplanted colonies to be maladapted to their new environments and is an important issue that needs to be considered in future coral restoration projects involving transplantation of corals after settlement75.Mean growth rates of les Fig.  and thesAcropora species77 and could be used to enhance coral restoration efforts. Fusion of different genotypes increases juvenile colony size and growth to larger size refuges thereby increasing survival potential unless competition among genotypes or tissue resorption and cell parasitism results in detrimental impacts61.The fastest growth and largest volume was recorded for the colony that resulted from the fusion of 31 individual juvenile corals on the reef between 15 and 17 months of age. This fused colony subsequently exhibited irregular growth morphology Fig.  and patcA. tenuis on the degraded reef site within three years after larval settlement , 14 months ($217) or 19 months ($61) prior to transplantation to reef sites67. Production costs for 2.5 year old A. palmata recruits ranged from US$325 for corals reared in a land-based nursery, to $13 for corals outplanted onto a reef after two weeks82.It has been noted that for future reef restoration interventions to have impacts at meaningful scales, they must be effective, relatively simple and affordableAcropora coral species can be significantly enhanced using mass larval settlement to rapidly initiate coral population restoration and fast growth to sexual maturity within three years, even on degraded reef areas. Early post-settlement mortality can be ameliorated through the use of increased concentrations of competent coral larvae to substantially increase initial settlement rates and recruitment overall. This combined with high survival of juvenile corals indicates that mass larval enhancement is a viable and relatively low-cost active restoration option for initiating coral population recovery on degraded reefs, where environmental conditions are suitable for reef corals to grow but natural recruitment is limited, and chronic disturbances and key threats are effectively managed.Although post-settlement mortality is a key challenge and potential bottleneck for coral recruitment, our results clearly demonstrate that larval settlement and recruitment of Supplementary Information


Deinocerites mosquitoes. The genome has a length of 14,420 nucleotides and encodes five open reading frames.We present a draft genome of a novel rhabdovirus, called Grenada mosquito rhabdovirus 1 (GMRV1), with homology to Wuhan mosquito virus 9 (WMV9) (NCBI reference sequence NC_031303), isolated from  Rhabdoviridae family are enveloped negative-sense single-stranded RNA viruses. They are known to infect a broad range of hosts, including plants, mammals, and insects  in March 2015. RNA was extracted from the pool of mosquitoes with TRIzol LS reagent (Ambion) following the manufacturer's instructions. rRNA (human-mouse-rat) was depleted from the sample, and a next-generation sequencing (NGS) library was prepared with the IntegenX RNA-Seq directional kit for Illumina. The library was sequenced at the Harvard Medical School BioPolymers Facility using HiSeq rapid sequencing to generate 100-bp paired-end reads.Grenada mosquito rhabdovirus 1 (GMRV1) was recovered from a pool of 29 female Culicidae and human sequences in GenBank, 90 million nonhost reads remained. Using CLC Assembly Cell (Qiagen), Pickaxe assembled the 78 million nonhost reads into 34,956 contigs that were subsequently annotated using a BLAST+/RAPSearch pipeline  contig was initially identified as a rhabdovirus from a RAPSearch alignment with 46% identity to the RNA-dependent RNA polymerase (RdRp) of Wuhan mosquito virus 9 (WMV9) (NCBI reference sequence NC_031303). GMRV1 contains five open reading frames (ORFs), a 151-nt 3′ leader, and a 356-nt 5′ trailer. The 3′ leader and 5′ trailer show no complementarity. ORFs 1 to 5 are located at nt 152 to 1699, nt 1847 to 3256, nt 3375 to 4274, nt 4454 to 6397, and nt 7192 to 14064, respectively. We predict these ORFs to encode the N, P, M, G, and L proteins based on BLASTP searches and the genomic layout of RNA virus phylogeny was recently expanded through deep sequencing of metagenomes , 6. GiveMG385079.The genome sequence of GMRV1 was deposited in GenBank under the accession number


The chemical properties and biological mechanisms of RNAs are determined by their tertiary structures. Exploring the tertiary structure folding processes of RNA enables us to understand and control its biological functions. Here, we report a nanopore snapshot approach combined with coarse-grained molecular dynamics simulation and master equation analysis to elucidate the folding of an RNA pseudoknot structure. In this approach, single RNA molecules captured by the nanopore can freely fold from the unstructured state without constraint and can be programmed to terminate their folding process at different intermediates. By identifying the nanopore signatures and measuring their time-dependent populations, we can “visualize” a series of kinetically important intermediates, track the kinetics of their inter-conversions, and derive the RNA pseudoknot folding pathway. This approach can potentially be developed into a single-molecule toolbox to investigate the biophysical mechanisms of RNA folding and unfolding, its interactions with ligands, and its functions. While RNA folding is critical for its function, study of this process is challenging. Here, the authors combine nanopore single-molecule manipulation with theoretical analysis to follow the folding of an RNA pseudoknot, monitoring the intermediate states and the kinetics of their interconversion. To perform these biological functions, RNAs must fold into specific tertiary structures2. Thus, knowledge of the RNA folding process is key to understanding the role of RNA structures and manipulating their functionalities3. Various methods have been developed to explore RNA folding mechanisms, including cryo-electron microscopy4, small-angle X-ray scattering5, and NMR6. In addition to these expensive instrument-based methods, chemical probing approaches, such as selective 2ʹ-hydroxyl acylation and primer extension (SHAPE)7 and hydroxyl radical footprinting8, have been widely applied to probe RNA folding structures. As RNA folding is intrinsically an intra-molecular process, various single-molecule methods, such as smFRET9 and optical tweezers10, have been developed to investigate RNA folding. However, due to the diffusive properties of RNA folding and short-lived transition paths, direct experimental observation of the RNA folding process, in particular the capture of intermediate folding states, remains difficult10.RNAs play critical roles in the maintenance, transfer, and processing of genetic information and the catalytic control of gene expression15. By taking advantage of the ability to electrically detect charged biomolecules through a nanometer-wide channel, various nanopore biosensors have been developed, with targets including DNAs17, microRNA biomarkers20, tRNA21, peptides23, and proteins25 as well as epigenetic changes such as DNA methylation28. In another important application, nanopores have been used as a precise force instrument to explore biomolecular mechanisms such as protein unfolding29, DNA unzipping31, RNA unfolding32, and the binding of nucleic acids to enzymes33. Briefly, single target molecules are driven into the nanopore and produce an ion current “fingerprint” for their dissociation-translocation procedure. The nanopore under this experimental configuration is best suited to the study of unfolding-related biological problems. However, these nanopore methods thus far have not been reported to detect the other half of the biomolecular process—how a biomolecule folds into a functional tertiary structure.Nanopore is a promising label-free, single-molecule-based, next-generation sequencing technology34 as a model system , the chimera is threaded through the nanopore from the cis to trans side. As the attached streptavidin is wider than the pore entrance, it terminates chimera translocation and immobilizes the chain inside the nanopore.The T2 RNA PK contains two helices, H1 (5-bp) and H2 (7-bp), that are coaxially stacked on top of one another Fig. . The looT2 Table . The pro37 and biosensor development40, our complex probe was designed to possess multiple functions suited to RNA folding detection  Prior to folding in the trans solution, the T2 RNA structure is fully disrupted when passing through the nanopore. Therefore, the folding of all RNA molecules starts from the single-stranded unstructured conformation, regardless of their original folding states in the cis solution. (3) The folding time can be controlled such that the folding process can be terminated at an intermediate state when a negative voltage is applied to reversely pull the probe. After the pulling force unfolds the intermediate state, the generated nanopore signature can take a snapshot  of that state. (4) As any low voltage can be applied, the nanopore can provide a minuscule pulling force  that disrupts the RNA folding structure. This behavior enables us to discover the sub-populated intermediate structures that otherwise cannot be identified under larger pulling forces. (5) In the nanopore, the folding process of the RNA is conducted at room temperature without a heating–cooling process, allowing the folding mechanism to be studied under near-physiological conditions. (6) The DNA tags of the chimera produce distinct nanopore signatures from RNA. The differences in their unique signals can be used as markers to track the RNA position in the nanopore and determine the intermediate states thereafter32. Note that DNA tags do not affect the RNA structure, as reported previously32. Similar DNA–RNA chimeras have been utilized for RNA folding detection in optical tweezers42. (7) As shown in Fig. Unlike previous studies that have used streptavidin-nucleic acid constructs for single-nucleotide discriminationion Fig. . (1) Thetrans solution. This unstructured molecule is held in the trans solution for a pre-defined duration, i.e., folding time  for re-folding. At the end of the folding time, a negative voltage is applied to pull the folded RNA back into the cis solution. Its folding structure (formed during the folding time) is then inferred from the RNA unfolding signature. Repeating this protocol enables many single-molecule snapshots to be measured, classified, and assigned to specific folding structures with a fractional population. By varying the folding time, time-dependent folding populations can be obtained to elucidate the folding pathways.The overall strategy for the nanopore folding study is as follows: a positive voltage is first applied to drive a probe into the pore, disrupt its structure, and release the unstructured RNA of the probe into the 2+ (Methods). Figure I/I0 = 18.6 ± 1.6%, A). Next to the 5ʹ-tag, the T2 RNA was pulled into the pore while disrupting its initial structure, further reducing the current to Level-2 . Consistent with the previous finding, the RNA in the nanopore reduced more conductance than DNA in the pore32. As the 3ʹ DNA tag entered and was immobilized (by the attached streptavidin) in the pore in place of T2 RNA, the nanopore current was returned to Level-1 (C). At this moment, the T2 RNA that was released into the trans solution started to re-fold (D). The folding time (tfold) for this illustrated event was 10 s. By the end of tfold, the voltage switched to −60 mV to pull the probe backward (from trans to cis) to unfold the RNA structure. This process produced the characteristic two-level block pattern, a long block at Level-3 , followed by a short deeper block at Level-4 . From Level-4, the current returned to the open pore level (G). According to a previous study32, this type of block pattern is the signature for the stepwise unfolding of a PK. The total duration of Level-3 and Level-4 was the unfolding duration (τ) of PK. Here, τ = 3500 ms ± 330 ms at −60 mV  is the key to discovering less stable intermediates. In addition to the various folding states identified above, we occasionally observed an open pore current when negative voltage was applied  molecular dynamics (MD) simulation. For the T2 RNA sequence, the simulation revealed that three intermediate structures Fig.  emerge bOverall, the observations revealed by the CG MD simulation are in agreement with the above nanopore experimental results. The nanopore folding snapshots and the CG MD simulation can be combined to construct the structural details for the observed intermediated states. In other words, by considering the features of unfolding nanopore signatures and the stabilities of the intermediated states relative to the ref-HP, we can establish connections between the intermediate states identified by the nanopore folding snapshots and the kinetics intermediates observed in the CG MD simulation. Compared with 12-bp ref-HP (18 ms ± 3 ms at −60 mV), 7-bp HP1 should be less stable and thus is assigned to the 2.5 ms state. The 13-bp HP2 corresponds to the 20 ms state due to it similar stability to ref-HP. In addition, the single-level blocks (−60 mV) for the two states  for each state  = 84.7% and kSS→HP2/(kSS→HP1 + kSS→HP2) = 15.3% of the population goes from SS to the HP1 and HP2 states, respectively. We note that HP1, TS, and PK share the same long native helix H2  barrier  to break the three non-native intra-loop base pairs in the HP1 hairpin loop in the HP1 → HP1* transition and the (mainly entropic) barrier  for the formation of the helix stem H1 in the HP1* → PKʹ transition , which had an unfolding duration of 1000 ms ± 80 ms in the nanopore  can regulate RNA structure formation and stabilize the pocket motifs in RNA or DNA tertiary structures52. To explore the effect of Mg2+ on the PK folding process, we repeated the nanopore snapshots in 1 M NaCl in the absence of Mg2+ (Methods). With 10 s folding time, T2 RNA without Mg2+ folded to four states, similar to T2 with Mg2+ , which lack loop–helix interactions and have much weaker stabilizing interactions with Mg2+.Magnesium ions  and can be regulated by magnesium ions. These results have important implications for the interplay between RNA structures and their functions in cells, since the regulatory functions of RNA molecules are often related to conformational transitions55.Our study suggests five-state folding kinetics for the T2 RNA PK. Similar to the unfolding kinetics for the same molecule revealed in a previous study54) and to develop further applications, the resolution and accuracy of the nanopore system need improvement. First, due to the resistor–capacitor (RC) time of the setup, the voltage change will simultaneously cause a curved charging current 64, for joint structure exploration. Most notably, our nanopore system can provide very small forces (by applying low voltage) to detect weak interactions in small RNA structures. As demonstrated by this and previous studies32, this system is also able to characterize non-canonical base pairs involved in tertiary structure formation and dissociation. Finally, this system may be generalized to investigate the folding of other biomolecules, including polymers, peptides, and even proteins. Overall, this method can find applications in biomolecular folding investigations and related areas such as pharmaceutical kinetic investigations and drug development.The improved system has the potential to study a variety of disease-relevant RNA and DNA tertiary structures, from hairpins2, 3-(N-morpholino) propanesulfonic acid (MOPS), and diethylpyrocarbonate (DEPC), were purchased from Sigma-Aldrich  and used as received. Lipid 1,2-diphytanoyl-sn-glycero-3-phosphocholine for bilayer formation was purchased from Avanti Polar Lipids  and used without further purification. All biotinylated RNA–DNA chimeras (Table 2 (pH 7.4). In the magnesium effect experiment, different concentrations of MgCl2 from 0 to 10 mM were used in the recording solution. In total, 5 μL of the chimera stock solution was directly released to the cis solution to reach a final concentration of 250 nM. Then, 25 μL of 100 μM streptavidin solution  was added to the cis compartment. The mixture was incubated for 15 min in order for streptavidin to bind the chimera and form the complex.All chemicals, including NaCl, MgClcis compartment. The protein can be spontaneously inserted into the lipid membrane to assemble a single nanopore. The ionic current through the nanopore at various transmembrane voltages was monitored by an Axopatch 200B amplifier  and acquired by a DigiData 1440A A/D converter (Molecular Devices), filtered with a built-in 4-pole low-pass Bessel filter at 5 kHz with a sampling rate of 20 kHz. A Clampex software (Molecular Devices) was used for the data recording and acquisition, and a Clampfit software (Molecular Devices) was used for analyzing the nanopore current traces, including event duration histogram analysis and amplitude histogram analysis.The nanopore recording chamber was assembled by two Teflon compartments that were separated by a thin Teflon partition film . Each compartment was filled with the recording solution to support the lipid bilayer formation and facilitate the ionic current flow. A lipid bilayer membrane was formed spanning a 150 μm orifice fabricated in the center of the partition. About 1 μL of the α-hemolysin protein solution was released into the tfold), and each folding state can occur with a specific probability (P). The folding procedure can be stopped at an intermediate structure or the native PK structure with a probability (P) by pulling the probe reversely in the trans-to-cis direction with a negative voltage (–60 mV). Under this pulling force, the formed folding structure can be disrupted. The duration from the beginning of voltage application to the block end for structure disruption is measured as the lifetime of the structure (τ). Each folding structure has a specific stability and can be identified from its lifetime. The lifetime of a folding structure follows the exponential distribution. According to previous studies66, if the bin is log(t), the distribution can be expressed asc is equivalent to the area covered by the distribution. For multiple states, the lifetime distribution can be expressed asThe folding of RNA to a specific tertiary state in the pathway is a time-dependent procedure. Tracking the time-dependent formation of each state in the nanopore allows the establishment of the RNA folding pathway. This process is realized by stopping the folding procedure after a given folding time , which is the beginning state of all RNA molecules measured, and 1–P0 is the total fraction of RNA molecules that form various folding structures (including intermediate and PK structures). P0 is obtained by measuring the number of SS events  divided by the total number of events collected.The fractional population of the state τ and fractional population, the mean and standard deviation (SD) were obtained from at least three experiments (n ≥ 3) with independent nanopores. All nanopore pulling experiments were conducted at room temperature (22 ± 1 °C).We first constructed the lifetime distributions (histograms) for RNAs experiencing a folding time of 1 s, 2 s, 5 s, 10 s, 30 s, and 60 s and then fit each histogram with four components  by using Eq. . Finally67. To enhance the conformational sampling, we used Replica-Exchange MD (REMD) with 10 replicas for temperatures ranging from 175 K to 400 K. In the simulation, the total simulation time per replica was set to t = 1 μs, with an integration time-step Δt = 0.5 fs. To monitor the folding process, the conformational snapshots were collected every 50 ps. For every 25 ns simulation time interval, the collected snapshots  were submitted to the clustering procedure. Based on the pairwise root mean square deviation (RMSD) for all the CG beads within a cutoff of 5.0 Å, the top 50% low-energy structures  were clustered to identify the typical intermediate states. The sequence of centroid structures of the clusters gave the folding pathway.Based on a CG RNA model, where the pyrimidines/purines are represented by 4/5 CG beads and a knowledge-based force field, we simulated the folding of the wild-type T2 and T2-polyU mutation RNAs using Langevin dynamics with the modified LAMMPS packages69 to estimate the transition rates between the different states. For a system of ω states, the master equation method considers the rate of population changes  and eigenvectors ω) of rate matrix To obtain the time-dependent population of each state For the T2 PK folding, our simulations suggested three intermediate states in addition to the initial unfolded and final folding states (see main text for details). For such a five-state system, the master equation involves a 5×5 rate matrix. By solving the master equation, we estimated the rate constants from the best fitted, experiment-determined population kinetics.F is defined as the RMSD between the experimentally determined population and the theoretically predicted population for each state:i at time t, respectively, and N=30 is the total count of experiment data .The fitness function 0, which should be deducted from the apparent total SS population in order to calculate the effective population of SS. Therefore, in the theory–experiment fitting process, the effective SS population is equal to the apparent  total SS population minus the population of the buffer state SS0. Our estimation based on the experimental data for the populations of the different states (see main text for the details) suggested that [SS0] is ∼4.2% of the total initial SS population. To avoid the possible trapping in local minima in the optimization procedure for the fitness function F, the parameter search process was repeated with different initial values. Additionally, to further confirm the fitted rate constants, we used alternative algorithms, such as the genetic algorithm, for the optimization. Comparisons between the different methods also led to the same consistent results.One of the states in the master equation calculation is the unfolded state, denoted as SS (single-stranded state). In the experiment, the SS state may be a fraction of the total ensemble of “unfolded” conformations identified from the electric current. In the experiment, the population of the SS state is estimated from the events where the electric current is not blocked. Limited by the experimental conditions, such as the purity of the RNA sequence and the effect of DNA extension, it is possible that not all the SS RNA chains can be involved in the folding process. There exists a buffer state in the experiment, SSAll data related to this manuscript are included in the main text and supplementary information and will also be available from the corresponding authors upon reasonable request.Supplementary InformationPeer Review File