Adaptive behavior requires the separation of current from future goals in working memory. We used fMRI of object-selective cortex to determine the representational (dis)similarities of memory representations serving current and prospective perceptual tasks. Participants remembered an object drawn from three possible categories as the target for one of two consecutive visual search tasks. A cue indicated whether the target object should be looked for first (currently relevant), second (prospectively relevant), or if it could be forgotten (irrelevant). Prior to the first search, representations of current, prospective and irrelevant objects were similar, with strongest decoding for current representations compared to prospective (Experiment 1) and irrelevant (Experiment 2). Remarkably, during the first search, prospective representations could also be decoded, but revealed anti-correlated voxel patterns compared to currently relevant representations of the same category. We propose that the brain separates current from prospective memories within the same neuronal ensembles through opposite representational patterns. Adaptive human behavior requires the representation of both imminent and future goals in response to changing task requirements. Little is known about how the brain distinguishes between information that is currently relevant and information that is only prospectively relevant.While working memory is thought to be pivotal to the active maintenance of representations for current task goals, representations serving prospective tasks should be shielded from affecting currently relevant input and output, and vice versa. Studies using reaction time and eye movement measures have indeed shown that currently and prospectively relevant representations differentially bias processing of perceptual input e.g., . Furtherunrelated activity is sent through the network and interacts with the pattern of changed responsivity that reflects the activity-silent memory. This is indeed what Such latent changes in responsivity are by definition difficult to test through activity-based measures. One prediction is that prospective memories re-emerge in activity-based dependent measures when A priori there appear to be a number of hypotheses. First, the standard synaptic potentiation mechanism predicts that the altered pattern of responsivity directly follows the pattern of activity during encoding of the item, thus predicting a high degree of similarity between the active and the silent representation when revived. A second possibility is that it is unnecessary to assume activity-silent representations at all, as has recently been argued by first search (turning it into a current template), for the second search (turning it into a prospective template) or would not be relevant for either search task . Using MVPA of fMRI activity in object-selective visual cortex, we directly compared the neural representations of these templates when needed for the current search task, to when needed for the prospective search task. Experiment 1 served to establish the relationship between currently and prospectively relevant representations, while Experiment 2 extended the comparison to representations that could be dropped from memory entirely, as they became irrelevant for the subsequent tasks.In two experiments, we aimed to further understand how working memory distinguishes between information relevant for either imminent or future goals. We asked observers to perform two consecutive visual searches for particular target objects drawn from different object categories  performed two consecutive visual search tasks (Search 1 and Search 2). The two to-be-sought-for target templates were presented at the start of each trial, after which a cue indicated which of the two targets would have to be looked for first – thus making it currently relevant, while the other target became prospectively relevant. To limit the working memory load, and to maximize the chances of decoding current and prospective targets and their differences , we only varied the target from trial to trial for one of the two searches (thus referred to as the ‘variable template search’). These targets served as the basis of the multivariate pattern classification analyses, and could thus either be Current or Prospective in nature. The other search was always for the same flower (referred to as the ‘constant template search’). The flower search served as an additional task to assign current or prospective status to the variable template, but the flower itself played no role in the classification analyses. For each search, participants indicated whether the target object was present or absent among six exemplars of the same category. = 928.18, p < 0.001, F = 183.06, p < 0.001, F = 10.87, p = 0.003, F = 9.22, p = 0.006 F = 6.32, p = 0.02, Our analyses targeted posterior fusiform cortex (pFs) which is known to be involved in representing object categories, and which we independently mapped for each participant , and vice versa . t = 8.18, p < 0.001, d = 1.67) and when prospectively relevant  = 5.67, p < 0.001, d = 1.16). However, while decoding accuracy for the current representation remained significantly above chance up and beyond the Search 1 display, the prospective representation returned to baseline during the delay. Notably, decoding performance was higher when the item was currently relevant than when it was prospectively relevant  = 3.22, p = 0.004, d = 0.66), consistent with its importance for the upcoming search task. Thus, object-selective cortex proves sensitive to object category as well as task-relevance prior to search.As can be seen in cross-relevance decoding scheme to assess whether current and prospective targets shared the same neural representational pattern  = 8.81, p < 0.001, d = 1.80 or CP: t = 9.04, p < 0.001, d = 1.85). There was no difference in decoding performance between the two schemes  = 1.43, p = 0.167, d = 0.29). These results indicate that during the delay prior to search, the representational pattern of the category was similar regardless of the current or prospective status of the object.Next, we used the tern see . This an = 11.57, p < 0.001, d = 2.36), and this was stronger than when the object was prospectively relevant  = 9.42, p < 0.001, d = 1.92). This is to be expected as during the first search of the Current condition  the current template category is actually presented on the screen, whereas in the Prospective condition the objects on screen  differ from the prospective target in memory. Importantly, we were still able to also decode the prospective category during the first search  = 1.90, p = 0.035, d = 0.39). In other words, the prospective category re-emerges when observers are actively searching for an unrelated target.Next, we wanted to know whether it was possible to successfully decode the category of the prospective template while participants were searching for a different object. As can be seen in below-chance decoding performance during Search 1  = −4.79, p < 0.001, d = −0.98, and PC: t = −3.67, p = 0.001, d = −0.75). Although we hypothesized that current and prospective templates might differ in representational format, and thus cross-relevance decoding accuracy might have been reduced, we did not expect the sign of decoding to flip. The reliable deviation from chance further confirms that information on the prospective memory was present in object-selective cortex during the first search. In addition, the fact that decoding was below chance suggests that current and prospective representations of the same object category were represented through opposite multivariate patterns.This then raises the question as to whether the re-emerging prospective representation resembles its counterpart when currently relevant. To assess this we used the cross-relevance decoding scheme. Remarkably, here we observed Finally, we assessed how the dissociation between current and prospective representations generalized across the different phases of the trial. As t = 9.86, p < 0.001, d = 2.01). This decoding was stronger than for the previously current search target, which was now no longer relevant  = −7.51, p < 0.001, d = −1.53). Nevertheless, and unexpectedly, we also observed above-chance decoding for this first target during Search 2  = 3.30, p = 0.002, d = 0.67, blue). Note that this reflects classification of a target that is no longer relevant, whereas during Search 1 it reflected the target that was not yet relevant. Moreover, the cross-relevance decoding scheme also shows a pattern similar to what was observed during Search 1  = −4.65, p < 0.001, d = −0.95 and PC: t = −4.49, p < 0.001, d = −0.92). We will return to the re-emergence of the no longer relevant target later.Although not the primary goal of our study, we conducted the same analyses also for Search 2. As expected, here we saw the pattern reverse see . In the To further elucidate the relationship between current and prospective representations, dissimilar, as is illustrated by the representations taking opposite corners in the MDS plot in As can be seen from same category representations were more similar than different category representations across relevance  = −5.82, p < 0.001, d = −1.28, as performed on Fisher-transformed 1-rho values). In contrast, during Search 1, prospective targets differed most from current targets when they belonged to the same category, more so than when they belonged to different categories  = 3.06, p = 0.005, d = 0.64). Likewise, during Search 2, no longer relevant targets were less similar from relevant targets when they belonged to the same category, than when they belonged to different categories  = 4.75, p < 0.001, d = 0.97). Thus these analyses statistically confirm what we can observe from the MDS plots, namely that current and prospective objects move from similar to opposite representations.To statistically test these effects, we computed the average dissimilarity between current and prospective objects, separately for when drawn from the same category (e.g. current cow versus prospective cow) and when drawn from a different category (e.g. current cow versus prospective skate/dresser) and used paired t-tests (N = 24). What causes current and prospective representations to anti-correlate? One possibility is that the brain separates current from prospective templates within the same neuronal ensembles by actively transforming the representational pattern of prospective templates to be opposite to that of current templates. A second possibility is that the mechanism of making a representation prospective is more passive, and that the reversed pattern results from simply temporarily dropping a representation from memory, as it is temporarily irrelevant. One piece of evidence from Experiment 1 suggests that postponing and dropping an item may indeed involve similar mechanisms: During Search 2 we observed similar negative decoding for target representations that had served the first search and that were thus no longer necessary. However, getting rid of a competing target representation when switching to a new search may also involve active mechanisms, now to prevent interference from the past target rather than from a future target. Results from our lab indeed suggest such a suppression of previous targets . A thirdTo address this, Experiment 2 sought to replicate and extend the main findings with a number of design changes. The most important change was the inclusion of a third condition, in which the memory item was cued to be irrelevant for any of the search tasks , but replacing the constant template task of Experiment 1 with what we call a ‘duplicate search’ task, in which participants indicated whether or not one of the objects in the search display appeared twice see . Note th = 1.65, p = 0.210,  = 2.48, p = 0.128,  = 0.16, p = 0.747,  = 37.24, p < 0.001,  = 19.56, p < 0.001,  = 14.76, p = 0.001, t = −7.40, p < 0.001, d = −1.48), while for the duplicate search, speed was the same regardless of the order  = −1.47, p = 0.154, d = −0.294).For both the template search and the duplicate search we computed mean RTs and mean percentage correct depending on the search order see .. We ranThe fact that participants were equally accurate at finding the template object regardless of the search order and that they were slower in the template search when performed second  suggests that the quality of the memory representation did not decay when it had to be postponed, but reactivating it for Search 2 required additional time. So any differences between current and prospective representations were not simply due to participants being worse on the prospective memory. Moreover, while in Experiment 1 the search of interest (variable template) was more difficult than the remaining search task (constant template), here, if anything, it was the other way around. Yet, the fMRI findings show a pattern very similar to that of Experiment 1, as reported next.As in Experiment 1, here we focus on multivariate representational patterns within the same three intervals: Delay, Search 1, and Search 2  = 4.40, p = 0.018, t = 10.76, p < 0.001, d = 2.15) Prospective  = 5.88, p < 0.001, d = 1.17) and Irrelevant  = 5.61, p < 0.001, d = 1.12). Pairwise comparisons revealed the difference between Current and Irrelevant to be significant  = 3.74, p = 0.001, d = 0.74). In contrast to Experiment 1 though, the difference in decoding accuracy between the Current and Prospective conditions was not significant  = 1.33, p = 0.193, d = 0.26), nor was there a significant difference between the Prospective and Irrelevant conditions  = 1.39, p = 0.175, d = 0.27). We will return to the possible reasons for this difference between experiments in the Discussion.cross-relevance decoding scheme to assess whether current, prospective and irrelevant items shared the same neural representational pattern  = 9.85, p < 0.001, d = 1.97), Current-Irrelevant  = 8.10, p < 0.001, d = 1.62) and Prospective-Irrelevant  = 6.90, p < 0.001, d = 1.38). A one-way repeated measures ANOVA with decoding scheme as factor revealed no significant differences between decoding schemes  = 2.4, p = 0.101, Next, we used the tern see . The claF = 35.17 p < 0.001, t = 9.47, p < 0.001, d = 1.89) and significantly higher than for both the prospective template  = 5.53, p < 0.001, d = 1.10) and the irrelevant item  = 7.29, p < 0.001, d = 1.45). This was to be expected since during the first search of the Current condition, the current template category was on the screen. More importantly, and in line with Experiment 1, we were able to decode the prospective category at above-chance levels during Search 1  = 3.35, p = 0.003, d = 0.67). At the same time, decoding of the Irrelevant category remained at chance  = 0.60, p = 0.550, d = 0.12), and was significantly weaker than for Prospective condition  = 2.27, p = 0.032, d = 0.45). Thus, information about the prospective template can be recovered while participants perform a different search task, whereas completely irrelevant categories are no longer decodable.A one-way ANOVA on the within-relevance decoding during Search 1 showed a significant difference in decoding accuracy  = 16.35, p < 0.001, below-chance decoding during Search 1 for the Current-Prospective cross-relevance classification =−5.92, p < 0.001, d = −1.18), while the Current-Irrelevant = −1.89, p = 0.071, d = −0.37) and the Prospective-Irrelevant  = 1.99, p = 0.058, d = 0.39) cross-decoding schemes did not significantly differ from chance. Most importantly, decoding accuracy for the Current-Prospective scheme was significantly lower than for the Current-Irrelevant scheme  = −2.95, p = 0.007, d = −0.59), indicating that the Prospective condition involved a stronger representational transformation than the Irrelevant condition. Since the prospective and irrelevant trials contained exactly the same visual input (see Methods), this result suggests that this transformation is driven by the future relevance of the prospective template rather than by some passive, automatic mechanism that would be the same for prospective and irrelevant representations .Next, we used the cross-relevance decoding scheme to evaluate whether the representations of the prospective targets and irrelevant items resemble their current counterpart see . A one-wt = 12.54, p < 0.001, d = 2.50), and decoding of the Prospective – now relevant - category was stronger than for the previously current – now no longer relevant – search target  = −8.84, p < 0.001, d = −1.76). In contrast to Experiment 1, where we unexpectedly observed above-chance decoding for this no longer relevant target during Search 2, here the current condition was at chance  = 1.14, p = 0.265, d = 0.22). However, similar to Experiment 1, we found below-chance decoding in the same time range for the Current-Prospective cross-relevance decoding scheme  = −6.57 p < 0.001, d = −1.31), indicating that there was information present on the previously relevant target.In line with Experiment 1, the pattern reversed for Search 2 see . In the same category were more similar  than the representations of targets from different categories  = −9.14, p < 0.001, d = −1.82, dissimilar: As in Experiment 1, prospective targets differed most from current targets when they belonged to the same category than when they belonged to different categories  = 5.20, p < 0.001, d = 1.04 and Search 2: t = 6.45, p < 0.001, d = 1.29). Again, throughout the course of the trial, the neural representations of the target objects moved from predominantly category space in the Delay period prior to search to predominantly relevance space during the two searches, where current and prospective objects were represented in opposite corners of the representational space.Next, we created representational dissimilarity matrices and multidimensional scaling (MDS) graphs for each interval of interest  for the three possible condition combinations: Current-Prospective, Current-Irrelevant and Prospective-Irrelevant see . First, t = −8.27, p < 0.001, d = −1.65 and Prospective-Irrelevant: t = −4.73, p = 0.001, d = −0.94). Importantly however, and in contrast to Prospective items, for Irrelevant items we did not find evidence that the representations were warped in opposite corners of the multivariate space during Search 1, as the (dis)similarity between targets belonging to the same or to different categories was equal for the Current-Irrelevant  = 0.94, p = 0.354, d = 0.89) and Prospective-Irrelevant  = −1.36, p = 0.184, d = −0.27) comparisons.The results for the Current-Irrelevant  and Prossame category from the mean dissimilarity when of different category) for each relevance combination . This showed that same category representations were indeed relatively more dissimilar for Current-Prospective comparisons than for Current-Irrelevant  = 2.88, p = 0.008, d = 0.57) and Prospective-Irrelevant  = 4.69, p < 0.001, d = 0.93) comparisons.The uniqueness of the prospective transformation was further confirmed by directly comparing the relative similarity difference , the transformation of prospective representations cannot be the result of basic stimulus-induced neural adaptation or BOLD response properties.It has been shown that the content of working memory can be decoded from multivariate patterns of voxel activity when observers are remembering an item for a single task . FurtherIn line with earlier work in different task and stimulus domains , we obsedissimilar from their current counterparts. Importantly, they differed in a systematic manner, to the extent that prospective representations were even more dissimilar from current representations of the same object category than representations of a different object category, and were characterized by an inverse correlation with current representations. Conversely, the initial similarity between current and irrelevant targets, which was evident during the delay, mostly disappeared during search leading to the same dissimilarity values for targets of the same vs. different category. Thus, while irrelevant targets simply appeared to decay from memory, prospective target memories were transformed. We point out that similar (as yet unpublished) findings have recently been reported by Our results are the first to reveal a direct relationship between currently relevant object representations on the one hand, and prospective representations on the other. Prior to the first search current, prospective and irrelevant representations were very similar, as cross-relevance decoding (training on one status while testing on the other) showed above-chance performance for all classification schemes and there were no differences across conditions. However, while the representation for prospective objects clearly reversed during search, such a reversal was weak to absent for irrelevant objects. We observed that during search, the reconstructed prospective memory representations of objects proved An issue left unresolved in the present study is whether the observed transformation of prospective representations extends to specific exemplars. That is, are current and prospective representations even more dissimilar when representing the same exemplar? Unfortunately, the limited number of trials per exemplar and condition precluded a useful analysis here, and future studies should directly test the item-specificity of the transformation of prospective representations.Imaging studies of visual attention have demonstrated that assigning attentional priority to task-relevant objects at the expense of irrelevant objects leads to the transformation of the representational space, by relatively enhancing target-related distributed activity  and by rA crucial question that our data do not answer is what the exact mechanism is behind the transformation from current to prospective representations. One possibility is the involvement of an active cognitive control mechanism, which specifically attempts to dissociate prospectively relevant from currently relevant representations in order to prevent task interference. Such control mechanisms might be exerted through feedback connections emanating from frontal areas central to counteracting unwanted or task-irrelevant information . InteresAn alternative possibility is that local, and arguably more passive mechanisms cause the change in responsiveness. We believe that we can exclude at least two of such mechanisms on the basis of the current data, specifically sensory-induced neural adaptation, and at a macro level, a sensory-induced BOLD undershoot. Both mechanisms would predict the voxels that were most active during memory encoding to be least active a short while later. In fact, such adaptation in responsivity might be functional in memory retrieval , and mayAlthough the underlying mechanism remains unknown, we believe the results have important implications for theories of prospective memory storage in working memory. First, the fact that prospectively relevant objects could be decoded from the same regions of interest as the currently relevant objects indicates that the different memory states do not necessarily rely on different brain areas. The successful cross-relevance decoding, where we trained the classifier on one state and tested on another, further confirms this. Second, the idea that prospectively relevant memories are stored in an activity-silent format has recently been debated by Instead, the emergence of the prospective memory that we found here during the first search fits best with a change in responsivity, resulting in an activity-silent representation. The fact that it was necessary to add activity to the system for the prospective memory to emerge – here in the form of unrelated visual search displays, is already testament to this. Importantly, the current data put limits on the potential mechanisms by which the responsivity changes. A frequent hypothesis is that prospectively relevant representations are stored through temporary synaptic potentiation. Such short-term potentiation predicts that what was strongly activated during encoding, will become more responsive, when prospective, and fire more strongly when reactivated. We observed the opposite: What was strongly activated when current became more strongly suppressed when prospective and vice versa. This goes against a simple short-term potentiation account of activity-silent representations in working memory.In conclusion, we find evidence that, in trying to separate current and prospective goals in visual search, the brain stores representations within the same neuronal ensembles, but through opposite representational patterns.Twenty-four participants  participated in Experiment 1, and twenty-five  participants in Experiment 2 . For both experiments, we obtained written informed consent from each participant before experimentation. Participants had normal or corrected-to-normal vision. The experiment was approved by the Ethical Committee of the Faculty of Social and Behavioral Sciences, University of Amsterdam (where scanning took place) and conformed to the Declaration of Helsinki.On each trial, participants performed two consecutive visual search tasks of real-world objects. The object of interest  consisted of real-world greyscale photographs, selected out of four exemplars. These categories were selected to have maximal dissimilarity in representational space see . The objAs can be seen in Notice that in Experiment 1, the current and prospective templates were always drawn from separate category sets within a trial. Specifically, in current trials, the current item was drawn from one of the three categories in the variable template set  and the prospective item was always the same constant template flower. In prospective trials the category sets reversed, with the prospective item being the variable template. We did this intentionally; having independent sets for the two search templates - within a trial - ensured that we could unequivocally interpret the category classification accuracy as reflecting the representation of the object of interest when either current or prospective. Remember that the classifiers learned to differentiate the neural pattern of the categories of interest: cow, dresser and skate. If both templates were to be drawn from the same category set within the trial, it would be impossible to know whether category classification accuracy actually reflects the quality of the representation of the current template, the prospective template or a combination of the two.The main experiment consisted of 8 runs with 12 trials each . Each run contained equal amount of trials per condition and category of interest . Each experimental run lasted ~7 min. The total duration of a session was ~1.5 hr  and mapper run (7 min), see below).The task in Experiment 2 was similar to Experiment 1, but we included important changes see . First, Each trial started with the presentation of only one memory item  for 1500 ms, followed by a fixation display that stayed on for 1500 ms. Then, a cue indicated the relevance of the memory item. The cue could be either a 1, 2 or 0 and remained on the screen for 1000 ms. When the cue was ‘1’, participants performed the template search first and the duplicate search second, making the memorized object currently relevant (Current condition). The order reversed when the cue was ‘2’, rendering the memorized object only prospectively relevant, as observers performed the duplicate search first and the template search second (Prospective condition). Finally, if the cue was ‘0’ the object was irrelevant because participants would only perform the duplicate search (Irrelevant condition) and would not be tested on the memory object. As in Experiment 1, the cue was followed by an 8 s delay with a fixation cross in the middle of the screen (‘Delay’) after which the first search display was presented. Depending on the condition, the first search display was either a template search (Current condition) or a duplicate search (Prospective and Irrelevant conditions). In the template search, participants indicated with a button press whether the memorized object of interest was present or absent among six exemplars of the same object category. Similarly, in the duplicate search, participants indicated whether a duplicate object  was present or absent, again set size for this display was six objects. After the first search display another eight seconds blank delay period followed (‘Search 1’) after which the trial either ended (Irrelevant condition) or the second search display was presented (Current and Prospective conditions). This second search display was also followed by an eight second blank period (‘Search 2’) after which the trial ended. The location of items and the duration of the search displays were the same as in Experiment 1.The main experiment consisted of 9 runs with 12 trials each . Within each experimental run, we balanced the amount of times that each relevance condition  was presented , as well as the amount of times that participants had to respond either ‘present’ or ‘absent’ in each search task. However, the relevance condition by category combinations  could not be completely balanced within runs ; nonetheless, across the whole experiment there were equal amount of trials for each combination. We also balanced the category used in the duplicate search task  across conditions and in combination with the category of the variable template . Each experimental run lasted ~7 min. The total duration of a session was ~1.7 hr .In both Experiments, the stimuli were back-projected on a 61 × 36 cm LCD screen (1920 x 1080 pixels) using Presentation  and viewed through a mirror attached to the head coil. Eye tracking data  was monitored to ensure participants were awake and attending the stimuli.At the end of each session we independently mapped the region of interest as the region that responded more strongly to intact vs. scrambled objects , within In both Experiments, the mapper run lasted ~7 min. Participants were asked to push a button when two consecutive images were identical (same exemplar) to ensure attention. The same fMRI preprocessing steps as described for the experimental task were performed for this mapper. In Experiment 1, for two participants the data recorded from this mapper was not usable, therefore, we used the anatomical mask only for these participants. In Experiment 2, due to a programming error, for the first three participants the mapper only contained three categories .Scanning was done on a 3T Philips Achieva TX MRI scanner with a 32-elements head coil. In the middle of the testing session (after four runs) a high-resolution 3DT1-weighted anatomical image  was recorded for every participant (duration 6 min).During the experimental task an object-selective cortex functional localizer, blood oxygenation level dependent (BOLD)-MRI was recorded using Echo Planar Imaging (EPI) .www.fmrib.ox.ac.uk/fsl;  This is an interesting study, on the currently 'hot topic' of where and how visual information is maintained in working memory in the human brain. The paper is well written and coherent, and the analyses are expertly done and clear. I am however worried about some alternative explanations for the results that may ultimately render them rather hard to interpret.1) Can the results be explained by BOLD undershoot?During the prospective trials, the participants see an exemplar of the category of interest  and the flower, and then they see a cue that tells them to first focus on the flower. The null hypothesis  would dictate that activity in the 'cow area' would simply decline, and become active again only during search display 2. Under this null hypothesis, cow voxels would have negative values during search display 1, which would explain why cross-classification results in below-chance performance.The authors swiftly dismiss this possibility, because, as Figure 2—figure supplement 1 shows, overall BOLD signal in the prospective condition actually goes up. However, what is plotted here is mean BOLD signal of the entire pFS area. Naturally, the flower search display will generate a large BOLD signal (in e.g. flower voxels). I think it is quite plausible that the results are explained by a BOLD undershoot of the relevant voxels, which is masked by a BOLD activation by a concomitant activation of other visually response voxels.We thank the reviewer for his supportive words. We agree that the original experiment did not allow us to fully exclude the possibility that below-chance decoding may have been caused by a voxel-specific BOLD undershoot following the initial sensory stimulation of the to-be-remembered target. To rule out this interpretation, we performed a second experiment that specifically addresses this and related concerns. As in our initial experiment, observers committed an object to memory and were cued with regard to the relevance of that object prior to performing the first search task. Critically though, we included a third condition that was identical to the Prospective condition, but in which the item was no longer relevant in the remainder of the trial. We explain the logic behind adding this Irrelevant condition in the introduction of Experiment 2:“To address this, Experiment 2 sought to replicate and extend the main findings with a number of design changes. […] For the same reason, neither can any differences between irrelevant and prospective representations be attributed to passive, sensory-related adaptation or BOLD undershoot.”Importantly, Experiment 2 showed below-chance decoding during the Search 1 interval for the Current-Prospective decoding scheme, and more strongly so than for the Current-Irrelevant scheme, for which classification performance did not reliably differ from chance .same category compared to when of a different category, such differential dissimilarity did not occur for irrelevant representations .In addition, the representational dissimilarity analyses of Experiment 2 show that while Prospective representations differ more from current representations when of the One might argue that because the memory item was not a target in the irrelevant condition, the initial BOLD response may have been weaker in the Irrelevant than in the Prospective condition, leading to a similarly weaker undershoot. However, in Experiment 2 there were no differences in the magnitude of the mean BOLD response during the delay between the Irrelevant and Prospective conditions  and therefore, it is reasonable to assume that any sensory-related undershoot would also be of the same magnitude.t(24) =2.56, p = 0.017, d = 0.51, see Figure 4A). So if initial sensory encoding strength caused a subsequent representation-specific undershoot, this should have been at least equally the case for irrelevant items.In a similar vein, peak classification accuracy in the within relevance decoding scheme did also not differ across conditions during the delay; if anything, peak classification accuracy was somewhat stronger in the Irrelevant than in the Prospective condition in TR1 . We now write in the Discussion:“An alternative possibility is that local, and arguably more passive mechanisms cause the change in responsiveness. […] Thus, the representational differences we find are determined by the observer’s goal state, and not automatically induced by the sensory representation of the stimulus. “2) Can the results be explained by systematic differences in search display 1 between current and prospective trials?Another alternative explanation  of the anti-decoding during search 1, is that prospective trials are trials in which the search 1 display is always the 'flower search display'. Current trials are trials in which something else than flowers is presented. Is it possible that the below chance decoding means that flowers don't resemble the other categories? In other words, is it possible that below chance decoding has nothing to do with the memorized target, but with the presented search display? interacted with any one of the categories of interest, leading to a particularly strong bias against one or two of the categories, which then shows in the average.We fully understand this comment. First, let us remark that although the design of the original experiment may appear suboptimal in some ways, we explicitly chose it to be optimal in other ways. Specifically, we chose the categories of interest  to be the same for each subject and condition on the basis of earlier studies showing that these categories could be well dissociated , and were moreover different from the flower category. We did this with the purpose to maximize the chance of decoding the item of interest even when prospective. Moreover, keeping the categories the same for every subject facilitated the RDM analyses. Second, note that our dependent measure was classification performance across the three categories of interest , while the flower remained constant regardless of which of these three categories was in memory. Classification performance, whether below chance or above chance, could thus not have been caused by the flower per se. What could have happened though is that the flower differentially We therefore believed it would be prudent to also tackle this issue head on in Experiment 2, by replacing the flower search with a search for a range of new objects, i.e. trees, butterflies, and motor bikes. Each of these categories was paired with each category of the variable template . Moreover, the category sets for each type of tasks had one animate and two inanimate categories, making them more comparable. We also changed the other search task – which was not of interest – from a constant template task to one in which no template was needed. We briefly explain the main design changes in the introduction of Experiment 2:“Furthermore, we simplified the design by keeping the variable template search (here referred to as simply “template search”), but replacing the constant template task of Experiment 1 [i.e. the flower search] with what we call a “duplicate search” task, in which participants indicated whether or not one of the objects in the search display appeared twice . […] Finally, just like the stimuli for the template search were drawn from three different categories , we varied the stimuli in the duplicate search such that they were also drawn from three different categories , in order to assess whether the (below-chance) decoding of prospective representations during search generalizes across a range of different categories.”Note that we still decided to keep the primary objects of interest for our decoding and RDM analyses the same , as a) they worked in Experiment 1, and b) the category-specific RDM analyses require the categories to be identical across subjects.Given that we found the same pattern of results as in Experiment 1, we believe our results were not specific to the flower, or in general to the use of a single search category when maintaining a prospective item. Furthermore, the difference between the Prospective and the Irrelevant condition (see earlier point) also argues against the effects being specific to the categories used.3) Design choiceThe authors designed their task such that one target was 'of interest' (could be of three categories) whereas the other was always exactly the same item (the flower). I am wondering whether this is a good design choice. One of the potential problems of this design choice is already mentioned in point 2. But it seems to create other forms of imbalance. For example, a 'prospective trial' may be more difficult than a 'current' trial. In prospective trials, there is a longer WM load, in current trials there is a reduced WM load .I don't really understand what advantage the current design has, over a design in which e.g. two exemplars would be shown from the three categories employed by the authors. This would be a more balanced design, and it would allow the authors to truly compare current vs. prospectively relevant items, in a design in which these trials are of equal difficulty.We agree with the reviewer that we did not clearly explain in the manuscript the reasoning behind the design choices in Experiment 1, in particular, the reason why we used systematically different categories for the current and the prospective items within each trial. See also our response to the previous point. We now make our reasoning explicit in the Materials and methods section of Experiment 1:“Notice that in Experiment 1, the current and prospective templates were always drawn from separate category sets within a trial. […] If both templates were to be drawn from the same category set within the trial, it would be impossible to know whether category classification accuracy actually reflects the quality of the representation of the current template, the prospective template or a combination of the two.”In Experiment 1, we used a constant template search task , as a way to limit memory load and to maximize the chances of decoding the category of interest when prospective (see Results section of Experiment 1). However, we also agree with the reviewer that this in itself created a difference in difficulty between the two types of search tasks. In Experiment 2, we replaced the flower search with a duplicate search task that we have successfully used before , with one of the reasons being an attempt to obtain comparable levels of difficulty across the different type of task. Behavioral results indeed showed that there were no significant differences in participants’ accuracy between the two types of task, and that RT differences now went in the opposite direction compared to Experiment 1. Yet the fMRI results remained the same, and thus cannot be explained by differences in difficulty across tasks.Finally, because the duplicate search itself requires no template, in Experiment 2 memory load was always one. It is still true though that, compared to the current condition, in the prospective condition participants held the item in memory for longer throughout the trial. Nevertheless, behavioral results in Experiment 2 also showed that participants were equally accurate at finding the template object regardless of the search order, albeit they were slower when the template search was performed second . This suggests that the quality of the memory representation did not decay when it was prospective (and it had to be maintained for longer), but that retrieving it for Search 2 required additional time. So, any differences between the current and the prospective representations cannot be explained by participants being worse in remembering the prospective item.Reviewer #2:"Current and future goals are represented in opposite patterns in object-selective cortex," van Loon, Fahrenfort, Olivers. This manuscript introduces what may well come to be accepted as an important principle in the representational dynamics of information held in versus out of the focus of attention in visual working memory, with information that will be needed to accomplish a future goal, but not the most proximal one, represented in a format that is anticorrelated with format in which it will be represented when it is of highest relevance. The approach taken by the authors represents an important advance, in that it addresses how information is maintained in different states of priority, rather than "just" where.  Overall, the manuscript is very clearly written and argued, and my comments will mostly highlight points where clarity could be improved.We thank the reviewer for his appreciation of our work and are pleased to know that he finds our approach to be an important advance and the manuscript well written and argued. We hope that the changes to the manuscript have sufficiently improved its clarity.Figure 2A: It appears that, by TR4 following the cue, the prospective category is at baseline – if so, this could be consistent with a stimulus encoding response that then returns to baseline  sustained across the initial delay).We agree with the reviewer that in Experiment 1 the classification performance for the prospective condition returned to baseline at TR4 and indeed we found significant differences between the Current and Prospective conditions at this TR. We now make this return to baseline explicit in the Results section of Experiment 1:t = 8.18, p < 0.001, d = 1.67) and when prospectively relevant  = 5.67, p < 0.001, d = 1.16). […] Notably, decoding performance was higher when the item was currently relevant than when it was prospectively relevant  = 3.22, p = 0.004, d = 0.66), consistent with its importance for the upcoming search task.”“[…] during the Delay prior to search the within-relevance decoding resulted in significant above chance object category decoding both when the variable template was currently relevant . What's not clear to me, however, is whether any quantitative and/or inferential information is conveyed by the lines that cross in the middle of each MDS plot for Search 1 and Search 2.We thank the reviewer for pointing out this lack of clarity in our plots. In the new version of this figure, instead of using the same symbols for all the exemplars of the same category, we used different symbols for each exemplar within a category. The categories are still indicated by color, while the relevance conditions are still indicated by the saturation. For instance, in the new Figure 3B, cows are purple, dressers are green and skates are orange. The light and dark purple triangles are then the same exemplar, when respectively current and prospective. The same applies for the other categories.The lines that crossed in the middle of each MDS plot in the original figure were used as way to facilitate the readability of the plots, but they did not have any quantitative information; therefore we decided to remove them from the graph. The quantitative information can be found in Figure 3C, where we depict the mean dissimilarity values for current and prospective targets when drown from the same vs. different object categories."Next we wanted to investigate what turns a currently relevant into a prospectively relevant representation." This misrepresents what was accomplished in the analyses that follow, which were more along the lines of 'tracking what transformation a representation undergoes when transitioning from currently to prospectively relevant." Indeed, it is noted in the Discussion that "A crucial question that our data does not answer is what the exact mechanism is behind this transformation."We agree with the reviewer that ‘transformation’ is a better-suited term. We have changed this line accordingly throughout the manuscript. For example:“We find a pronounced anti-correlated representation for prospective targets compared to the same targets when current. Experiment 2 furthermore shows that it is not the temporary irrelevance, but the prospective relevance that causes this transformation, because little to no anti-correlation was found for items that were irrelevant altogether."And:“What causes current and prospective representations to anti-correlate? One possibility is that the brain separates current from prospective templates within the same neuronal ensembles by actively transforming the representational pattern of prospective templates to be opposite to that of current templates.”For the analyses illustrated in Figure 4, more information is needed. First, what condition labels are used for the three sets of t-values in each plot? E.g., for "cow," are the betas from the cow regressor from the GLM used for all three plots ? If this is true, it's not surprising that there's very limited variance across voxels in the latter two conditions. Would the regression be more interesting if it was "fair" to the Different category by using its regressors? Also, how does this analysis reconcile with the idea of an opposite representation? E.g., why isn't the slope of Prospective Same of the same value as Current Same, but opposite in sign? A separate question is how interpretable the classifier weights are. With logistic regression, the importance of any given voxel can vary markedly depending on the level of sparsity that is imposed . Haynes's group has described a way to get unequivocal importance values from SVM, but I'm not familiar with a comparable metric for logistic regression. If such a diagnostic can be extracted from logistic regression it would be of considerable interest, and so should be detailed.To address the reviewer’s request for clarification; reviewer 2 is correct to assume that the t-values for each predictor were indeed obtained by dividing the betas from the regressors in the GLM by the standard error. However, each line in the graph represented the t-values of a category when current (e.g. cow), the t-values for the condition in which the same category was prospective  and the average t-values of the conditions when one of the other two categories was prospective .However, we decided to remove this analysis from the manuscript altogether since we agree with the reviewer that the interpretation of classifier weights for logistic regression is not straightforward . Moreover, given that largely the same information was already conveyed by the RDMs, we believe that removing the analysis from the manuscript does not weaken the main conclusions of the paper."We found that object category information was stronger for pFs, visual cortex and IPS than for RMF, whereas the impact of relevance (Current vs. Prospective) was apparent in all ROIs throughout the trial." This is an important point, and addresses a question of considerable current theoretical interest (and controversy), and so more information should be included – enough to support critical evaluation of this statement.We agree with the reviewer on the importance of this finding. However, the addition of Experiment 2 already made the manuscript very dense, especially if we also included the same analyses again there. Therefore, we decided to remove the analyses on other ROIs  from the manuscript. Following suggestions of reviewer 3, we plan to first analyze these regions more thoroughly and then report on them in a separate paper.Reviewer #3:[…]A major question left unresolved is whether this adaptation (or top-down suppression) is item specific or general to the whole class of stimuli (category specific). Obviously this is a trickier analysis, but theoretically tractable and worth reporting whether or not it is possible. The results would be very informative, especially interpreting the effects within a working memory framework.We thank the reviewer for the supportive words, and for this interesting question. Unfortunately, to properly answer it, we would need to run our classifications schemes separately for each exemplar and, given the limited number of trials per exemplar and condition in our design, we do not have enough data to run such analysis. Also, in Experiment 1, while the categories were balanced across all cells of the design, the specific exemplars were not. In Experiment 2 we did balance the exemplars as well, but this resulted in only 3 trials per exemplar and condition combination. We now explicitly mention in the Discussion that this question is an unresolved issue that is worthy of future investigation:“An issue left unresolved in the present study is whether the observed transformation of prospective representations extends to specific exemplars. […] Unfortunately, the limited number of trials per exemplar and condition precluded a useful analysis here, and future studies should directly test the item-specificity of the transformation of prospective representations.”In general, the authors do a good job handling what feels like unexpected results; however, I think there is room to be a bit more upfront with the initial hypotheses. They are relatively clear that they did not expect a pattern reversal, and especially not during the second search. For clarity, I think it would be worth explicitly stating the initial hypotheses regarding the first delay period .We agree with the reviewer that in the first version of the manuscript our hypothesis could have been more clearly stated. We want to emphasize that we explicitly planned the cross-relevance decoding scheme, and that we hypothesized the current and prospective items to be different (dissimilar) – but not in the way we observed. Now we explicitly write our initial hypothesis for all time intervals in Experiment 1:“[…] while the within-relevance decoding scheme provides evidence for the presence of current and prospective representations, it does not reveal whether these representations are similar or different. […] Our general starting hypothesis was that while current and prospective representations would be similar during encoding, they would become increasingly dissimilar during the course of the trial, due to reduced activity or re-coding of the prospective item within the same network, while becoming similar again when the prospective memories are revived for the second task.”When discussing the below-chance decoding results of Experiment 1, we then make clear what we did, and did not expect to find:“This then raises the question as to whether the re-emerging prospective representation resembles its counterpart when currently relevant. […] The reliable deviation from chance further confirms that information on the prospective memory was present in object-selective cortex during the first search.”Things change for Experiment 2 of course, as we now had explicit predictions derived from Experiment 1. Moreover, in the introduction of Experiment 2 we explicitly mentioned the ideas about possible cognitive mechanisms (or possible stimulus induced effects) underlying the transformation of the prospective items as observed in Experiment 1, which fed into our design changes:“What causes current and prospective representations to anti-correlate? […] Specifically, presenting the to-be-memorized stimulus may result in neural adaptation  or in a BOLD-related undershoot , each of which would predict a reduced voxel response to later stimulation.”


Although self-rated health (SRH) independently predicts mortality, the biological background of this association remains unexplained. This study aimed to examine the association between SRH and serum high-sensitivity C-reactive protein (hsCRP) level.Subjects were 899 participants aged 35–69 years (237 men and 662 women) in the Daiko Study, part of the Japan Multi-Institutional Collaborative Cohort Study. They were enrolled from 2008 to 2010. Of the subjects, 666 participated in a second survey 5 years later. Lifestyle factors and SRH were assessed using a self-administered questionnaire. Serum hsCRP level was measured using a latex-enhanced immunonephelometric assay. The association between SRH and serum hsCRP level was evaluated using a general linear model with covariates. We further longitudinally investigated whether higher serum hsCRP level at baseline predicts poor SRH after 5 years using an unconditional logistic regression model.p for trend = 0.023). The age- and sex-adjusted odds ratio and 95% confidence interval (CI) for poor SRH after 5 years was 1.45 (95% CI: 0.76–2.78) for the highest tertile compared with the lowest tertile of serum hsCRP level at baseline with a significant linear trend (p for trend = 0.033), although the risk increase disappeared after adjustment for other covariates.A higher serum hsCRP level was significantly associated with poor SRH at baseline after adjusting for covariates (The present study demonstrated that poor SRH is cross-sectionally associated with higher serum hsCRP level. However, the longitudinal data did not support the relationship between serum hsCRP level at baseline and future SRH. Further longitudinal studies that include data on mortality and multiple inflammatory markers are warranted to elucidate the possible role of low-grade inflammation in the association between SRH and mortality risk. Self-rated health (SRH) is an independent predictor of mortality and disease morbidity even after adjusting for potential confounders, such as medical risk factors, in the general population; the predictive value of SRH is surprisingly robust across different countries , 2. The The present study, therefore, aimed to examine i) whether SRH is cross-sectionally associated with serum hsCRP level and ii) whether serum hsCRP level predicts SRH after 5 years.Subjects of this study were participants in the Daiko Study, part of the Japan Multi-Institutional Collaborative Cohort Study (J-MICC Study). Nagoya city residents aged 35 to 69 years were invited to participate in the baseline survey using leaflets distributed in mailboxes citywide from 2008 to 2010. A total of 5172 residents provided written informed consent and completed a self-administered questionnaire on their lifestyle, including medical history. The design and rationale of the Daiko Study have been described in detail elsewhere . Of the n = 68) and those who had not provided medical history information (n = 13). We further conducted a 5-year longitudinal analysis with a cohort that consisted of those who had participated in both the baseline and the second survey to examine the relationship between SRH at the second survey and serum hsCRP level at baseline . We additionally examined whether higher serum hsCRP level at baseline predicts incidence of poor SRH after 5 years among those without poor SRH at baseline who had participated in the second survey (n = 587).The present cross-sectional study included 899 individuals (237 men and 662 women) with data on serum hsCRP level at baseline, after excluding those with serum hsCRP level of 1500 ng/mL or higher .2). Physical activity was estimated as metabolic equivalents per day from the frequency and duration of daily and leisure time activities [A self-administered questionnaire was used that included the following demographic characteristics and lifestyle factors: alcohol consumption, smoking status, education level, sleeping hours, exercise habits, psychological stress, and medical history. Psychological stress was assessed using a single question: “Have you felt stress in the last year?” with the following four response options: “Not at all,” “Not much,” “A little,” and “High.” Height and weight were directly measured on the day of survey; body mass index (BMI) was calculated as weight in kilograms divided by the square of the height in meters .SRH was assessed using a single question: “How do you rate your health in the last few months?” with the following five response options: “pretty good,” “rather good,” “neither good nor poor,” “rather poor,” and “pretty poor.” For the present analysis, those who answered “rather poor” or “pretty poor” were combined into a poor SRH group because of the small number of those who responded “pretty poor” at baseline  for the hsCRP assay was 50 ng/mL. A total of 52 subjects (5.8%) had serum hsCRP levels below the detectable value. We therefore substituted half the LOD value for values below the LOD . Serum Cn = 11).Differences in mean values among SRH levels at baseline were examined by the one-way analysis of variance, and the differences in proportions were tested using the chi-square test. Least-squared means of serum hsCRP concentration according to SRH at baseline and the second survey were estimated using a general linear model with the following potential confounders: age (as a continuous variable), sex (for men and women combined only), alcohol consumption , smoking status , education level , BMI (as a continuous variable), physical activity (as a continuous variable), psychological stress , sleeping hours (as a continuous variable), medical history , and energy intake (as a continuous variable). As a covariate, SRH at baseline  was additionally controlled in the longitudinal analysis. The linear trend between SRH and serum hsCRP level was examined using Scheffe’s test after assigning an ordinal score to each SRH group in the model. Participants with missing data for education level and psychological stress were included as additional categories in the analyses  and 95% confidence intervals (CIs) of poor SRH after 5 years were estimated for tertile groups of serum hsCRP level at baseline using an unconditional logistic regression model with the above-mentioned covariates among those without poor SRH at baseline who participated in the second survey. The linear trend for risk was evaluated using the continuous value of serum hsCRP level.p-value of < 0.05 was considered statistically significant. All statistical analyses were performed using SAS 9.4 M5, which runs on SAS University Edition .A two-sided Table p for trend = 0.015, 0.033, and 0.050, respectively). The age- and sex- adjusted means ± standard errors (SE) (ng/mL) were 304.9 ± 28.2 for pretty good, 322.9 ± 18.1 for rather good, 349.9 ± 14.5 for neither good nor poor, and 418.1 ± 26.1 for poor (p for trend = 0.001). The analysis of the least-squared means with other covariates also demonstrated that poor SRH at baseline was significantly associated with a higher serum hsCRP level in the total population (p for trend = 0.023); however, the analysis stratified by sex showed no significant association (p for trend = 0.161 for men and 0.234 for women). The relationships between SRH at the second survey and serum hsCRP level at baseline are summarized in Table n = 666). Poor SRH at the second survey was significantly correlated with a higher serum hsCRP level at baseline (p for trend = 0.025). In the analysis stratified by sex, the positive relationship was statistically significant for women, but not for men  although the direction of relationship was not different by sex. The age- and sex- adjusted means ± SE were 288.4 ± 33.2 for pretty good, 328.3 ± 20.0 for rather good, 350.7 ± 17.1 for neither good nor poor, and 412.3 ± 29.0 for poor (p for trend = 0.003). The relationship was not significant in the multivariate-adjusted model.Crude means and least-squared means of serum hsCRP level according to SRH at baseline are shown in Table n = 587). Table p for trend = 0.033). However, the crude and the multivariate-adjusted models showed no relationship.We further longitudinally investigated whether higher serum hsCRP level at baseline predicts poor SRH after 5 years among those without poor SRH at baseline who participated in the second survey (In this cross-sectional study, we found that a higher serum hsCRP level was associated with poor SRH in all the subjects. This significant association remained after adjustment for covariates including medical history, although the analysis stratified by sex showed no significant association. Poor SRH at the second survey was also associated with a higher serum hsCRP level at baseline; however, this relationship was not significant in the multivariate-adjusted model. The longitudinal analysis of participants without poor SRH at baseline showed that a higher serum hsCRP level at baseline was unrelated to poor SRH after 5 years after multivariate adjustment.The present cross-sectional findings are in accord with findings from previous studies –12. We fIt is also well known that low-grade inflammation is associated with an increased risk of all-cause mortality –33. The We were interested in whether serum hsCRP level predicts future SRH, as it is useful to clarify whether low-grade inflammation is associated with poor SRH. We therefore longitudinally investigated whether higher serum hsCRP level at baseline predicted poor SRH after 5 years among those without poor SRH at baseline who participated in the second survey. The risk of poor SRH after 5 years was significantly associated with higher serum hsCRP level at baseline in the age- and sex-adjusted model; however, the ORs were close to unity in the crude and the multivariate-adjusted model. A previous study in Sweden examining predictors of SRH after 1 year also found that serum hsCRP level was not associated with future SRH . Thus, tThe strengths of the present study include its longitudinal design and the control of several confounding factors. An important limitation was that we did not include mortality data and data for other inflammatory markers. We were therefore unable to directly examine the extent to which SRH and low-grade inflammation are independently responsible for mortality, and the association of SRH with other inflammatory markers. Longitudinal research on SRH, multiple inflammatory markers, and mortality within the same population are needed. Sample size in this study was relatively small, particularly for men, resulting in low statistical power; however, we demonstrated consistent associations between SRH and serum hsCRP level for men and women separately, and these findings were similar to those of a previous study .In conclusion, poor SRH was cross-sectionally associated with a higher serum hsCRP level in the total population after adjustment for covariates although the analysis stratified by sex showed no significant association. The longitudinal analysis demonstrated no relationship between serum hsCRP level at baseline and future SRH. Further longitudinal studies that include data on mortality and multiple inflammatory markers are warranted to elucidate the possible role of low-grade inflammation in the association between SRH and mortality risk among the general population.


Complex plant tissues vary in hardness, i.e. some are succulent, while others are complex to break. Besides, plant metabolites, such as polysaccharides, proteins, polyphenols and lipids, can greatly interfere with the RNA extraction. So, in order to obtain a high-quality RNA from the complex tissues  rich in secondary metabolites, a robust method is demanded. Several methods  have been used previously for the isolation of quality RNA from the coconut tissues, but without any success. The present study will provide with the details of a new method (Quick and Reliable RNA Extraction Method or QRREM), which have efficiently isolated the intact RNA form the complex tissues of coconut compared with CTAB, Trizol and RNA plant. The method has been validated for the isolation of high-quality intact RNA from the other available plant species . The method has various advantages over the other methods in terms of time and cost effectiveness. Furthermore, the resulted RNA from various tissues of coconut performed well in the downstream experiments, i.e. reverse transcription and PCR for the production and amplification of cDNA. Many plant species contains complex tissues and higher concentrations of plant metabolites, which can interfere with the extraction of RNA. The co-precipitation of these metabolites restricts the chances of getting high-quality intact RNA . Among tRecently, a number of methodologies and commercial kits have been developed to extract high-quality RNA from various complex matrices. The commercial kits from Invitrogen TRIZOL Reagents are available for the isolation of RNA from the starch rich tissues of rice , oil ricIn the present study, a new method (QRREM) that gave consistent results is discussed; the method is robust, less time consuming , cheap and reliable to isolate RNA from the very complex matrices of coconut tissues . The method has provided with an intact RNA in high quantities, which were actively utilized in downstream experiments. This method has primarily been developed for the isolation of RNA from the coconut tissues, but exhibited promising results for the other plant species and would be worth trying on animal tissues.st strand cDNA synthesis kit was used for reverse transcription and PCR kit from Real Time was used for the amplification of cDNA.Agrose gel, chloroform, chloroform-isoamyl alcohol , acid buffered phenol (pH 4.5), isopropanol, ammonium thiocynate, guanidine thiocynate, sodium acetate, glacial acetic acid, glycerol, β-merceptoethanol, polyvinylpyrrolidone (PVP)-40, RNase free tubes, mortar and pestle, scissor/razor blade, plastic bags, freezer, spatula, pipette, Tris borate EDTA (TBE) buffer, ethidium bromide, DNA ladder and diethylpyrocarbonate (DPEC)-treated water. All the chemicals used were of high analytical purity and were purchased from Real Times and Chemical Reagents, China. TaKaRa, PrimeScript II 1Agrose gel electrophoresis systemBench centrifuge (Eppendorf Centrifuge 5804 R)Vortex Gel Doc system Nanodrop PCR machine/thermocycler The coconut tissues of freshly harvested coconuts were collected at the Coconut Research Institute, Chinese Academy of Tropical Agricultural Sciences, Wenchang, Hainan-China. Similarly, leaf tissues of betel nut, spring onion and mint were taken from the freshly sown vegetables at the vegetable farm of the same Institute. All the samples were cut into small pieces with the help of scissors or razor blade and transferred to small plastic bags. The plastic bags were then preserved quickly in liquid nitrogen and were kept frozen at −80°C till further analysis.Ammonium thiocyanate: 0.4 MGuanidine thiocyanate: 0.7 M3 M sodium acetate (pH 5): 3.3 mlPhenol: 38 mlGlycerol: 5 mlDEPC-treated water: up to 100 mlβ-Merceptoethanol: 2%PVP-40: 3%Take 30 ml of (DEPC-treated water) and add the contents to it one by one (except PVP-40), once dissolved the volume should be made up to 100 ml with DPEC-treated water. The buffer should be handled in a fume hood as it contains phenol and should be kept refrigerated in a dark bottle to avoid oxidation. The buffer can be used within 6 months of preparation.If PVP-40 is added to the buffer it will react with ammonium thiocynate, giving a pink color, which tends to decrease the performance of the extraction buffer significantly. Therefore, PVP-40 should be added to the mortar before crushing the sample in liquid nitrogen.All the steps in the protocol are summarized in a flowchart .Transfer the sample containing bag from the freezer into liquid nitrogen to avoid any thawing before the homogenization of the tissue.Pour liquid nitrogen into a clean mortar and pestle (previously autoclaved) to make it cool.Take approximately 0.08 g of the frozen tissue from the plastic bag and transfer it to the mortar containing liquid nitrogen and PVP-40 (3%). Grind the tissue to a fine powder with a pestle, top up the mortar with liquid nitrogen during grinding to avoid any thawing.(iv)Immediately transfer pulverized tissue with the help of a frozen spatula to a 1.5-ml tube, containing 1 ml of extraction buffer, vortex for 15 s, and then incubate on ice for 5 min.(v)g at 4°C.Add 200 µl of chloroform and vortex thoroughly for 15 s, then incubate on ice for 10 min. Centrifuge it for 5 min at 12000×(vi)g for 5 min.Transfer the supernatant (500 µl) to a new RNase free tube and add an equal volume of chloroform/isoamyl alcohol to it and centrifuged at 12000×(vii)Carefully transfer the upper aqueous phase (400 µl) of the solution to a fresh, RNase-free tube, then add an equal volume of ice cold isopropanol (500 or 600 µl), mix the solution by inverting the tubes and incubate for 5 min on ice.(viii)g and 4°C, after centrifugation discard the supernatant. The RNA pellet is generally at the bottom of the tube. Remove all the residual isopropanol by placing the tube upside down and let the pellet dry for 5 min.Centrifuge for 5 min at 12000×(ix)Redissolve RNA with 30 μl of RNase-free (DEPC-treated) water by pipetting gently for 1 to 2 min.(x)A260 had computed the quantity of the RNA, where the purity of RNA was checked by A260/280 ratio, which is in the range from 1.8 to 2.2.Nanodrop 2000 analysis: The concentration of RNA was determined spectrophotometrically by applying 1 μl of sample to the Nanodrop 2000 and measured the absorbance at 260 and 280 nm. The absorbance at Agrose gel electrophoresis: We prepared a 0.8% (w/v) agrose gel by dissolving 0.2 g of RNA grade agrose in 25 ml of 0.5× TBE buffer in an RNase-free conical flask. The flask was heated in a microwave oven till the complete dissolution of agrose in the TBE buffer. Ethidium bromide (2 μl) was added to the content, and the flask was swirled. The mixed content of the flask was then poured into the gel tray with a comb and was left to set for approximately 20 min. After setting of the gel, RNA loading buffer (2 μl) was added to the sample (3 μl) and loaded onto the gel. The gel was run for 12 min at 120 V and 240 mA.The quantity and purity of the RNA were checked on a Nanodrop, while the quality was confirmed by running an agrose gel electrophoresis.Pulverization of sample: steps (i) to (iii) took 20 min for four samplesRNA extraction: steps (v) to (vi) took 15 minRNA precipitation: steps (vii) to (iv) took 20 minQualitative and quantitative analysis of RNA: step (x) (a and b) took 25 minst strand cDNA synthesis kit. According to the manufacturer’s protocol, the reaction of the RT was conducted as follows: approximately 2 μg of the isolated RNA was mixed with 1 μl oligo dT primer (50 μM) + dNTP mixture (10 mM) and made the volume to 10 μl with DPEC water in a Microtube. The tube was incubated at 65°C for 5 min, and then chilled on ice. In the next step, 5x PrimeScript II buffer (4 μl), RNase inhibitor (0.5 μl) and PrimeScript II RTase (1 μl) were added to the tube and the final volume was brought up to 20 μl with DPEC water. The mixture was incubated at 42°C for 60 min. To inactivate the enzymes, the mixture was incubated for further 5 min at 95°C, followed by cooling on ice.The first strand cDNA was synthesized by using TaKaRa PrimeScript II 12O in a Microtube. The tube was then placed in a thermocycler with the following settings: 94°C for 3 min; 35 cycles ; 72°C for 5 min.After the synthesis of first strand cDNA, RT-PCR was done using a kit from the Real Times to amplify the cDNA. An LPAAT-α gene was tested using primers: CnLPAAT-For: ATGGATGCTTCAGGGGCAAGTTCG and CnLPAAT-Re: TTATGAATTTGACCTTCCGCTAGCATCC for coconut endosperm and coconut apple samples, whereas a housekeeping gene Actin were tested using primers; CnACT-For: ATAAAGTATGGCTGATGCTGAGG and CnACT-Re: CAACAATGCTTGGGAACACA for coconut leaf bud. cDNAs were amplified by mixing 2 μl of cDNA, 1 μl of reverse primer (10 μM), 1 μl of forward primer (10 μM), 5 μl 10x TAQ buffer, 1 μl dNTP mix (10 mM), 0.5 μl TAQ (5U/μl) and 39.5 μl ddHA260/280 and A260/230 ratios of the extracted RNA by the QRREM method were ranged from 1.90 to 2.00 and 1.97 to 2.01 for various tissues of coconut. Moreover, A260/280 and A260/230 ratios of the extracted RNA by the QRREM method were ranged from 1.93 to 2.03 and 1.98 to 2.00 for betel nut, mint and spring onion. Similarly, the concentration of the extracted RNA from various tissues of coconut by the QRREM method was in the range from 16.2 to 63.2 µg/80 mg tissue, where for other plant species  the values were between 12.7 and 20.9 . That is the key of the QRREM method to provide with a highly intact RNA in larger amounts with in short time (1.5 h). The current method not only has been validated for the extraction of RNA from coconut tissues, but was expanded for betel nut and the available plant species (mint and spring onion) at the farm of the Coconut Research Institute, Hainan-China. The method might also be useful to extract quality RNA from various animal tissues that are rich in macromolecules and secondary metabolites. In fact, the method will serve the scientific community of the coconut producing areas around the world in order to understand various mechanisms of coconut crop physiology and to develop high yielding resistant cultivars in future.All the data are included in the manuscript.


REH lying on the right-hand hairpin (REH) of the HBoV1 genome were identified in processing viral pre-mRNA. Of note, all viral nonstructural proteins-encoding mRNA transcripts use both the proximal polyadenylation sites [(pA)p1 and (pA)p2] and distal polyadenylation sites [(pA)d1 and (pA)dREH] for termination. However, capsid proteins-encoding transcripts only use the distal polyadenylation sites. While the (pA)p1 and (pA)p2 sites were utilized at roughly equal efficiency for proximal polyadenylation of HBoV1 mRNA transcripts, the (pA)d1 site was more preferred for distal polyadenylation. Additionally, small RNA-seq analysis confirmed there is only one viral noncoding RNA (BocaSR) transcribed from nt 5199–5340 of the HBoV1 genome. Thus, our study provides a systematic and unbiased transcription profile, including both mRNA and small RNA transcripts, of HBoV1 in HBoV1-infected HAE-ALI cultures.Human bocavirus 1 (HBoV1) infects well-differentiated (polarized) human airway epithelium (HAE) cultured at an air-liquid interface (ALI). In the present study, we applied next-generation RNA sequencing to investigate the genome-wide transcription profile of HBoV1, including viral mRNA and small RNA transcripts, in HBoV1-infected HAE cells. We identified novel transcription start and termination sites and confirmed the previously identified splicing events. Importantly, an additional proximal polyadenylation site (pA)p2 and a new distal polyadenylation site (pA)d Human bocavirus 1 (HBoV1) was identified from nasopharyngeal aspirates of pediatric patients with acute respiratory tract infections in 2005 ,5,6,7,8.Bocaparvovirus of the Parvoviridae family. The two prototype members of this genus are minute virus of canines (MVC) and bovine parvovirus type 1 (BPV1)  1.5–2.5-fold more frequently than the (pA)d sites [(pA)d1 + (pA)dREH] p2 and (pA)dEH sites . All NS-pA)dREH] .REH sites at a similar ratio . Data of the small RNA-seq does not suggest that HBoV1 produce any classical miRNA, however, it revealed a small RNA hot spot located between nt 5199–5340 , which cNext generation RNA sequencing technology has been widely used in many aspects of biological research, including virology for the study of gene expression by both the host and virus. This technology is a powerful tool for the systematic and unbiased profiling of viral transcription/expression. Both mRNA-seq and small RNA-seq have been applied to profile gene expression of AAV2 in the presence or absence of coinfection of helper viruses, which identified novel AAV2 mRNA transcripts from both the positive and negative stands of the AAV2 genome, as well as viral miRNAs . In the Transcription start site. We previously identified the HBoV1 P5 promoter starts transcription at nt 282 from total RNA extracted from termini-less HBoV1 plasmid-transfected HEK293 cells  ranging from nt 5369 to nt 5499 with peak sites at nt 5443 and nt 5444, which was located in the REH. The (pA)dREH might be a universal feature of bocaviral mRNA polyadenylation. In fact, bocaparvoviruses MVC and BPV1 utilize only the distal polyadenylation sites located at similar positions on the REH (close to the turn-round or the loop) , we identified a stretch of distal polyadenylation sites [(pA)dhe loop) ,22. Howehe loop) , and HBoREH site at nt 5445 [REH of 2.09. We speculate that viral mRNA transcripts generated from a transfected plasmid are polyadenylated differently from those produced during virus infection.It has been reported that HBoV1 mRNAs, which were generated from transfection of a full-length HBoV1 clone in HEK293 cells, were polyadenylated dominantly at the (pA)d nt 5445 . HoweverHBoV1 mRNA transcripts. Identification of novel (pA)p2 and (pA)dREH polyadenylation sites make the HBoV1 mRNA transcripts more diversified. Our previous study identified that NP1 plays an important role in regulating splicing at the A3 acceptor site and facilitates HBoV1 mRNAs to read through the (pA)p sites for production of VP-coding mRNAs [REH equivalently. However, NS1-4 transcripts are preferred to use the proximal polyadenylation sites than the distal polyadenylation sites.ng mRNAs . We founAlthough the mRNA-seq showed that NS1 transcripts are expressed at the highest level among these NS1-4-coding transcripts, the NS1 protein is expressed at a much lower level than NS2-4 in HBoV1-infected HAE cells . The detHBoV1 small RNA transcripts. Recently, we identified a HBoV1 long noncoding RNA (BocaSR) located between nt 5199–5338 [199–5338 . BocaSR 199–5338 . The smade novo HBoV1 transcription map. Small RNA seq data suggest that there is likely one viral small RNA transcribed during infection. Thus, our study provides an example to determine a comprehensive and unbiased parvoviral transcription profile using RNA-seq data.In conclusion, we used both mRNA-seq and small RNA-seq data to profile RNA transcripts during HBoV1 infection of human airway epithelia. We identified novel polyadenylation sites not previously observed in other studies ,36. By u


Nucleobase transporters are important for supplying the cell with purines and/or pyrimidines, for controlling the intracellular pool of nucleotides, and for obtaining exogenous nitrogen/carbon sources for metabolism. Nucleobase transporters are also evaluated as potential targets for antimicrobial therapies, since several pathogenic microorganisms rely on purine/pyrimidine salvage from their hosts. The majority of known nucleobase transporters belong to the evolutionarily conserved and ubiquitous nucleobase-ascorbate transporter/nucleobase-cation symporter-2 (NAT/NCS2) protein family. Based on a large-scale phylogenetic analysis that we performed on thousands of prokaryotic proteomes, we developed a webserver that can detect and distinguish this family of transporters from other homologous families that recognize different substrates. We can further categorize these transporters to certain evolutionary groups with distinct substrate preferences. The webserver scans whole proteomes and graphically displays which proteins are identified as NAT/NCS2, to which evolutionary groups and subgroups they belong to, and which conserved motifs they have. For key subgroups and motifs, the server displays annotated information from published crystal-structures and mutational studies pointing to key functional amino acids that may help experts assess the transport capability of the target sequences. The server is 100% accurate in detecting NAT/NCS2 family members. We also used the server to analyze 9,109 prokaryotic proteomes and identified Clostridia, Bacilli, β- and γ-Proteobacteria, Actinobacteria, and Fusobacteria as the taxa with the largest number of NAT/NCS2 transporters per proteome. An analysis of 120 representative eukaryotic proteomes also demonstrates the server's capability of correctly analyzing this major lineage, with plants emerging as the group with the highest number of NAT/NCS2 members per proteome.