N = 202). The main study findings were that perceived organizational support mediated the relationship between psychological wellbeing at time point 1 and volunteer engagement at time point 2. However, perceived organizational support did not mediate volunteer engagement at time point 1 and psychological wellbeing at time point 2. There were no bidirectional effects between volunteer engagement and psychological wellbeing. The findings contributed to the existing literature, suggesting there are overlaps between support mechanisms and motivation between paid and unpaid work. The practical implications for not-for-profit organizations are the importance of providing organizational support for young adult volunteers to improve wellbeing outcomes. Limitations and future study recommendations are presented.In Australia, young adults are more likely to experience psychological distress than other age-groups. Accordingly, volunteer work engagement may act as an important tool for supporting psychological wellbeing. The present study relies on the job demands–resources model and self-determination theory to help understand the negative consequences of high work demands and the importance of effective organizational support to enhance positive mental health outcomes. To address research gaps, the current study explores these concepts for the young adulthood cohort in not-for-profit organizations. The study aims to explore the relationship between psychological wellbeing, volunteer work engagement, and perceived organizational support. The study used a quantitative, cross-lagged, longitudinal method for collecting data from two online surveys completed 4 weeks apart. The inclusion criteria of participants were volunteers who worked a minimum of 4 h a month (on average), resided in Australia, and were between 19 and 40 years old ( Over the years, philosophers and researchers have asked why does one help another when there is no visible reward? Various studies have found that helping behavior, such as volunteer work and community service increases psychological wellbeing, self-esteem, life satisfaction, and happiness . VolunteVolunteering, altruism, and prosocial behavior can be regarded as antecedents to positive wellbeing, happiness, personal health, and public health . The curThere is a range of theories that explores the human experience with volunteer work. The volunteer process model follows a series of stages that explores the volunteer experience . First, The self-determination theory SDT;  suggestsThe functional approach identifies specific types of motivations and examines how this affects the volunteers’ psychological outcomes . This apVarious studies have investigated the benefits of volunteering across different life stages. In previous research, volunteer work has predominately been measured by asking participants to recall how many hours they have volunteered . This meTo address this limitation, the current research considers volunteer work engagement as an alternative psychometric tool . Work enPrevious studies have used longitudinal data to explore the relationship between psychological wellbeing and volunteer work overtime. The findings of The longitudinal data of The job demands–resources (JD-R) model provides a comprehensive assessment of the health and motivational indicators of work-related wellbeing, and how these are a function of the work environment . In bothThe current study will extend upon existing research by using a longitudinal design with a bidirectional analysis and a sample of Australian young adult volunteers. The current research has two main objectives: first, it investigates the bidirectional relationship between psychological wellbeing and volunteer engagement , and secHypothesis 1 is that volunteer engagement at time point 1 (T1) has a significant positive direct effect on psychological wellbeing at time point 2 (T2). Hypothesis 2 is that psychological wellbeing at T1 has a significant positive direct effect on volunteer engagement at T2 see . HypotheM = 27.7 years, SD = 5.2) residing in Australia. The exclusion criteria were those who did not meet the criteria and were not proficient in English. The sample comprised 138 men (68.3%) and 64 women (31.7%). Further general demographic information is presented in n = 244) were excluded. In total, two surveys were completed by the participants in 4 weeks. This was managed through the Qualtrics survey platform, whereby participants who consented to participate in the second survey were automatically sent an email 4 weeks later with a new survey link. A total of 524 volunteers participated in survey 1, of which 446 provided consent to receive the link for survey 2. Of those 446 participants, 252 completed survey 2. After cleaning the data and matching the participants’ responses from survey 1 and survey 2, 202 participants completed both surveys and consented to their data being used. This led to 404 numbers of occasions.Participants were a convenience sample from the public made up of active volunteers who volunteered a minimum of 4 h a month (on average). The inclusion criteria were individuals aged between 19 and 40 years  and volunteered on average 4.14 h per month (SD = 2.90) between January 2021 and July 2021 when there were minimal COVID restrictions. In total, 34% of participants (n = 67) identified as having reduction in volunteer hours due to COVID-19, while 66% (n = 130) said they did not. From July 2021, the participants indicated they volunteered an average of 2.20 h per month (SD = 1.17). For the second survey (4 weeks later), the participants volunteered 14.17 h in the month (SD = 2.03), with an average of 1.75 organization (SD = 1.15). The participants had been volunteering an average of two and a half years  Human Research Ethics Committee , the overall mediated path (the a*b path), and finally, the direct effect of the predictor on the outcome after controlling for the mediator (the c′ path). We chose this mediation procedure as traditional mediation tests have low power  = 87.51, p = 0.720  = 7.81, p < 0.01, R2 = 0.04, adjusted R2 = 0.035. The regression coefficient  indicated that an increase in volunteer engagement at T1 corresponded, on average, to a decrease in psychological wellbeing at T2 of 0.15 points. Hypothesis 2 stated that psychological wellbeing at T1 would be positively significantly predicted by volunteer engagement at T2, to test whether a linear regression analysis was used. The hypothesis was not met, psychological wellbeing at T1 was not significantly predicted by volunteer engagement at T2, R2 = 0.01, F = 1.65, p = 0.201, 95% CI .Hypothesis 1 was tested using linear regression analysis. The results showed that volunteer engagement at T1 had a negative significant effect on psychological wellbeing at T2. Although a significant direct effect was found, the hypothesis was not met since this relationship is negative. Volunteer engagement at T1 had a negative significant relationship on psychological wellbeing T2, R2 = 0.008. There was a significant positive direct effect between perceived organizational support and psychological wellbeing at T2. In addition, volunteer engagement at T1 and psychological wellbeing at T2 had a significant negative effect when mediated by perceived organizational support, R2 = 0.14*** (weak effect). Contrary to prediction, the indirect effect of perceived organizational support on volunteer engagement at T1 and psychological wellbeing at T2 was not significant, b = −0.02, BCa 95% CI .Hypothesis 3 stated that volunteer engagement at T1 and psychological wellbeing at T2 would be mediated by perceived organizational support. The first model which tested Hypothesis 3 resulted in no mediation see . There wR2 = 0.15*** (weak effect). In addition, a significant positive direct effect of organizational support on volunteer engagement T2, R2 = 0.04* (weak effect). The pathway (c′) between psychological wellbeing at T1 and volunteer engagement at T2 mediated by perceived organizational support was not significant. Following the prediction, the indirect effect of perceived organizational support on psychological wellbeing at T1 and volunteer engagement at T2 was significant, b = 0.12, BCa 95% CI .Hypothesis 4 stated that psychological wellbeing at T1 and volunteer engagement at T2 would be mediated by perceived organizational support. The second model which tested Hypothesis 4 yielded a full mediation effect see . There wA multiple linear regression analysis was conducted to investigate perceived organizational support as a predictor of psychological wellbeing at T1 and T2, and volunteer engagement at T1 and T2. R2 = 0.14, F = 6.43, p < 0.001]. COVID-19 significantly predicted a decrease in volunteer engagement at T1  and a significant increase in psychological wellbeing at T2 . COVID-19 did not significantly predict psychological wellbeing at T1  and volunteer engagement T2 .An additional multiple linear regression was run to identify the impact of whether a reduction of volunteer hours due to COVID-19-predicted change in psychological wellbeing at T1 and T2, as well as volunteer engagement T1 and T2. The model was significant and indicated the four predictors explained 14% of the variance [The objectives of this research were to explore whether volunteer work had a significant personal benefit for young adults. Specifically, perceived organizational support indirectly affected the relationship between volunteer work engagement at time point 1 (T1) and psychological wellbeing at time point 2 (T2). We found no evidence to support a bidirectional relationship between volunteer work engagement and psychological wellbeing. There was a negative relationship found between volunteer work engagement at T1 and psychological wellbeing at T2. Furthermore, we found no significant relationship between psychological wellbeing at T1 and volunteer work engagement at T2. Regarding indirect effects, psychological wellbeing at T1 did not have an indirect effect on volunteer work engagement at T2 through perceived organizational support; however, volunteer work engagement at T1 had an indirect effect on psychological wellbeing at T2 through perceived organizational support. This study addressed research gaps because previous studies had not used these variables in the model, did not use a time lag of 4 weeks, or the young adulthood sample.Although the relationship between volunteer work engagement at T1 and psychological wellbeing at T2 was significant, no bidirectional relationship between volunteer work engagement and psychological wellbeing was found. Therefore, the results of this study do not support previous studies that explored the bidirectional effects between volunteer work and psychological wellbeing over longer periods of 3 and 10 years . The dirVolunteer work engagement at T1 and volunteer work engagement at T2 were significantly negatively correlated, which shows a negative relationship with engagement over time. This suggests that there is likely variability in how volunteers engage and how their wellbeing changes across different time points. There may be contributing organizational factors, such as unexpected changes in the volunteer work environment that limited the participants’ ability to develop high levels of engagement. In addition, there was no correlation between psychological wellbeing at T1 and T2. Previous research has found psychological wellbeing to be variable overtime, although it is relatively stable over shorter periods . In addiPrevious studies applied the JD-R model  to underHypothesis 3 was not supported as there was no indirect effect of wellbeing and engagement through perceived organizational support. The unexpected results may be due to a weak correlation between engagement at T1 and T2 in the current study, which likely resulted in limited interaction with the organization. During the period of data collection (July – October 2021), there were higher rates of COVID cases, alongside local government lockdowns. Thus, volunteers likely spent less time with organizations in general. In the model, perceived organizational support significantly predicted psychological wellbeing at T2, which suggests that those who observed that the organization valued their contributions were more likely to present with an increase in psychological wellbeing at T2. Moreover, through further analyses, perceived organizational support had a positive relationship with psychological wellbeing at T1 and psychological wellbeing at T2, although a weak negative relationship with volunteer work engagement at T1. These findings suggest that perceived organizational support may have a greater effect on wellbeing than engagement.Hypothesis 4 was supported as perceived organizational support had a significant indirect effect on psychological wellbeing at T1 and volunteer work engagement at T2. These findings are in line with previous studies as they suggest that perceived organizational support contributes to volunteer mental health through its effect on wellbeing and engagement . WindsorThe contributions of the present research are the previously unexplored variables of work engagement in the volunteer context for the young adulthood cohort tested using the regression and mediational model. Findings from this study produce opportunities for further investigations into the relationship between these variables, across different time lags, and demographics. These findings contribute to both theory and empirical evidence as they provide support for The practical implications from this research suggest that on an individual level, people may experience a positive relationship between psychological wellbeing and volunteer work engagement with the presence of organizational support. For example, organizational management may proactively provide feedback to volunteers to show them that their work is being appreciated and recognized. In addition, organizations can provide weekly or monthly “check-ins” by assigned mentors where volunteers can express any concerns they may have. There are potential implications for the young adulthood cohort in Australia that encourage volunteer participation. The present findings depict an increase between levels of freely chosen volunteer involvement from time point 1 to time point 2. This suggests that young adults that freely choose to volunteer may derive personal benefits from the experience. Organizations may choose to provide greater resources to support the young adulthood demographics to increase volunteer work engagement and wellbeing. These findings may inform not-for-profit organizations on how to develop better recruitment and volunteer retention strategies for the young adulthood cohort.The macro-level implications of the study are the Australian ideologies for prosocial and helping behavior in the local and wider community. Not-for-profit organizations would benefit from increased funding to hire high-level professionals, such as organizational psychologists to advise on how to best support their volunteers. There may be smaller volunteer organizations that cannot learn and create avenues that will ensure professional development, organizational support, and subsequently volunteer retention. Thus, the present study contributed to the volunteer and not-for-profit organizational research field from a multitude of levels that can lead to further research.The present study did not use a control group of non-volunteers. Therefore, the study was not able to assess whether the results were significantly different from the natural changes of engagement, wellbeing, and support over time. Hence, the current study does not imply causation, and the findings were correlational. The current model can be used in future research to assess whether similar findings are present in a non-volunteer sample working in for-profit organizations. In addition, there are limitations in generalizing Western findings to non-western populations. Hence, future research can investigate how the current model fits with young adult samples from different ethnic and cultural backgrounds. There was a negative correlation of volunteer engagement between the two time points, as well as no correlation between psychological wellbeing across time points. These unexpected results represent a limitation of the cross-lagged effects as they do not explain weekly or monthly variations, in comparison to multilevel analyses accounting for lagged effects. Therefore, future research should measure across multiple time points to assess the variability over time of volunteer engagement and psychological wellbeing for the young adulthood cohort.The study was not able to measure the emotional and cognitive impacts of the COVID-19 pandemic. The current study measured the behavioral impact of COVID-19 through the reduction in hours of volunteer work. However, it is important to acknowledge that the volunteer experience may have been impacted to a greater extent by COVID-19, for example, by necessitating work from home with limited social interaction and the fear of contracting COVID-19. The period of data collection may have contributed to the unexpected findings, and future research should explore the psychological impacts of COVID-19.The type of volunteer work was not measured in this study. Therefore, the study cannot provide organizations with specific recommendations that reflect the nature of their volunteer work. Future studies should focus on types of volunteers, for instance, comparing the levels of engagement and wellbeing between emergency service volunteers and volunteer telehealth workers. Finally, there is much research required on the impact of volunteer work in young adulthood such that future research should explore the relationship between wellbeing and other psychological states associated with unpaid work. For example, burnout can contribute to the understanding of how to improve volunteer retention and organizational commitment, further enhancing the volunteer experience for this age-group.This study was the first to investigate the bidirectional effects between psychological wellbeing and volunteer engagement across a time lag of 4 weeks, and the mediating effect of perceived organizational support of psychological wellbeing and volunteer engagement for a young adulthood sample. The findings highlighted the importance of organizational support in mediating the relationship between psychological wellbeing and volunteer engagement, providing new insights into the volunteer experience of young adults. Future research should replicate the present model and include new variables to assess the psychological impacts of COVID-19, re-testing the variables across multiple time points, and exploring new volunteer-related outcomes in the young adulthood life stage. Nevertheless, the present findings are significant and contribute to a richer understanding of the factors supporting volunteer work in young adulthood. Indeed, it appears that helping others can help oneself, with the appropriate support resources in place.The original contributions presented in this study are included in the article/The studies involving human participants were reviewed and approved by Australian College of Applied Professions Human Research Ethics. The patients/participants provided their written informed consent to participate in this study.GD, MG, and JH contributed to the conception and interpretation of the study and results. MG developed the study design. JH assisted with framing the literature review. GD managed the data collection, performed the analysis, and wrote the completed drafts of the manuscript. All authors contributed to manuscript revision, read, and approved the submitted version.


Growing interest in social neuroscience requires the development and refinement of social behavior paradigms and novel analyses of the manner in which the neural activity in one brain may reflect the behavioral activity of self or of other, or the activity of each of two interacting individuals. This study documents an approach to analyze such interactions in a social dominance encounter between rodents. The study of social dominance interactions between animals offers a window onto the decision-making involved in establishing dominance hierarchies and an opportunity to examine changes in social behavior observed in certain neurogenetic disorders. Competitive social interactions, such as in the widely used tube test, reflect this decision-making. Previous studies have focused on the different patterns of behavior seen in the dominant and submissive animal, neural correlates of effortful behavior believed to mediate the outcome of such encounters, and interbrain correlations of neural activity. Using a rigorous mutual information criterion, we now report that neural responses recorded with endoscopic calcium imaging in the prelimbic zone of the medial prefrontal cortex show unique correlations to specific dominance-related behaviors. Interanimal analyses revealed cell/behavior correlations that are primarily with an animal’s own behavior or with the other animal’s behavior, or the coincident behavior of both animals (such as pushing by one and resisting by the other). The comparison of unique and coincident cells helps to disentangle cell firing that reflects an animal’s own or the other’s specific behavior from situations reflecting conjoint action. These correlates point to a more cognitive rather than a solely behavioral dimension of social interactions that needs to be considered in the design of neurobiological studies of social behavior. These could prove useful in studies of disorders affecting social recognition and social engagement, and the treatment of disorders of social interaction. Fmr1-/y) rats, that repeated testing over several sessions revealed a relative but not absolute social dominance of WT animals over the mutants, with gene-related differences in intersession stability. We also witnessed the development of inflexible behavioral habits that enabled overcoming failures of social awareness in some encounters. These findings are consistent with the socially withdrawn or otherwise aberrant behavior seen in humans with ASD and in animal models of Fragile-X . Social interactions occur between pairs of rodents taking part in a variety of social tests used in laboratory settings , 2, inclragile-X  and Rettragile-X , 9.2+ transients in the prefrontal cortex or anterior cingulate gyrus are likely very important mediators of dominance and known to be on the causal chain of brain network activity mediating dominance and submissiveness  rats (n = 12). These are part of an ongoing study that will grow to much larger groups of animals over several years, sufficient to make detailed WT vs. FXS comparisons (not yet feasible). A total of 913 cells were identified from 11 of these animals, of which five pairs were analyzed in detail. We outline the protocol and histochemical analyses, then turn to quantification of the behavior, to the global ΔF/F measure, and, finally, using event processing software, to the patterns of events across multiple individual cells.The data take the form of CaLeft; 20×). NeuN and GAD67 expression (Left Middle), DAPI (Right Middle), and exemplars of superposition (Right) show that virus expression was in excitatory neurons (identified with NeuN and GAD67) without affecting nuclear integrity (observed with DAPI). 2+ imaging between the animals of a pair. position D, Right Top), one pair of animals displayed consistently strong dominance  while the other two pairs showed a more varied pattern of moderate dominance (dark and light orange); in the KO cages (Bottom), two pairs of animals showed strong dominance, and only one pair showed a more moderate pattern (H4090/H4089). Different genotypes were hereafter pooled, but distinct dominance status retained.KO cages A, BottomP = 0.003) but no significant difference between strong vs. moderate. For normalized duration, nonparametric Kruskal–Wallis tests comparing winners and losers showed that winners spent significantly longer time in MOVE FORWARD but shorter in RETREAT and WITHDRAWAL in the strongly dominant/submissive pairs . In the more moderate dominance animals, only MOVE FORWARD showed a longer duration for the winners (P < 0.05).Detailed frame-by-frame video analysis of the behaviors B reveale2+ imaging analysis that follows.In 2+ transients from individual cells were secured , with successful longitudinal registration gradually declining across successive sessions from >90% on successive session to >60% comparing session 1 (S1) and S5 (Successful stable recordings of Ca) and S5 A. As we y axis) reveal diverse patterns, including periods of quiescence, activity, and activity bursts (Top), notably during two long PUSH periods by the winning competitor (Bottom). Later in the same trial, PUSH by the winner is associated with RESIST/RETREAT and eventually WITHDRAWAL by the loser, with clearly detectable changes in the pattern of mean activity at any one time point. However, from T2 onward, it is quite striking how much time is spent by the winning animal (Bottom) in STILLNESS (blue). Two examples of the complexity of the pattern are as follows: 1) In T4, the second period of PUSH by the imaged animal is accompanied by a stable low value of ΔF/F until the competitor starts to RESIST, whereupon the mean activity rises dramatically; 2) in T5, the winning animal is in STILLNESS, whereupon, at 320 s, it shows a dramatic rise in mean activity which coincides with RESIST by the other animal and then subsides, with the trial ending through WITHDRAWAL of the loser. Thus, careful inspection reveals that perception of the social dimension was apparent even in this normalized multicell activity.The first step was to examine the neural activity of individual cells using the ΔF/F measure B and they bursts C. The diy bursts C, Top, nmpetitor C, Bottomg animal C, Bottomn = 6) are more equivalent with, if anything, the trend paradoxically showing losing animals to have slightly higher mean activity. Inhomogeneity of variance precluded the use of an ANOVAR, so we used a nonparametric test with the strong subgroup. These revealed that the difference in mean activity between winners and losers was highly significant in the case of strong dominance pairs . The comparison of winners and losers in the moderate subgroup was not significant . t test t = 6.69, df = 130, P < 0.0001). The difference appears strongest in the contrast between strong winners and losers.SI Appendix, Fig. S1). This was computed independently for all six behaviors, with cells classified as unique for two behaviors in a single animal and categorized as “mixed cells” (P = 0.41), or to the strength of this status (ANOVA F < 1). P = 0.002). P < 0.0001). Moreover, the reliability of the unique RESIST cells was circa 60%, whereas the reliability of these same cells during other behaviors was low . Both results together illustrate the accuracy of the cell classification based on MIS.Using Online Active Set method to Infer Spikes (OASIS) of ΔF/F signals, we inferred time-stamped activity corresponding to the sharp rise in amplitude that we hereafter consider as cell firing “events” (d cells” D. The toLeft) shows this cell firing when the other animal pushed with the recorded loser sometimes resisting or retreating. This pair of animals was classified as showing only moderate dominance, with the recorded animal losing on T1, T3, and T5 but winning on T2 and T4 . The final measure calculated was intersession consistency of the two types of unique cells. Whereas reliability refers to cell/behavior correlations within a session, consistency is a measure of engagement across sessions with respect to encoding of any behavior (orange) or of a specific behavior such as PUSH, RESIST, or RETREAT. Left gives examples for seven cells from one animal (H4011). Cell 71 is an example of high consistency across sessions (5/5) but in which the specific behavior encoded changes from session to session. In contrast, C87 shows lower consistency with respect to encoding any behavior (2/5 sessions). Right shows the intersession consistency which was circa 30% for the overall encoding  and 20% for the specific cells per behavior, both measures being much lower than for reliability.Up until now, we have referred to unique cells as being those whose activity has an MIS score above the 95th percentile for a specific behavior. 2 and T4 A. We int2+ activity in specific longitudinally registered regions of activity and specific behaviors. Unique cells were identified that correlated with an animal’s own behavior (unique-own) or the other animal’s behavior (unique-other). Coincident cells were also observed reflecting the joint behavior of the two animals competing in a tube test contest. We also made observations of within-session reliability and intersession consistency of the cell behavior correlations.There were two main findings. First, the mean cell activity (ΔF/F) across all cells in PrL of the mPFC of individual animals reflected relative dominance, being higher in strongly dominant and lower in highly submissive animals than in pairs of animals displaying more-moderate and interchanging patterns of dominance. Second, interanimal analyses revealed exacting MIS-computed correlations between CaDetailed inspection of the data revealed that specific behaviors, such as pushing, resisting, and stillness, were frequent . RetreatWhereas single-cell recording has classically been examined in relation to the perception, location, actions, or decisions of an animal from which recordings are being taken –17, a soKingsbury et al.  were the2+ signals in these different lines with those of WT normal rats. This analysis will be complemented by studies in other social protocols such as sociability and social memory, both of which are relevant to a better understanding of the neurobiology of ASDs.We began this program of research to investigate possible disorders in social dominance in rat models of ASDs, finding, as expected, that both Fragile-X and SynGap mutant rats are socially submissive but with dominance patterns influenced by experience , 23. In Contact for Reagents and Resources Sharing.r.g.m.morris@ed.ac.uk).Further information and requests for resources and reagents should be directed to and will be fulfilled by the lead contact, R.G.M.M.  male Long-Evans Fmr1em1/PWC hooded rats, hereafter referred to as WT or Fmr1 KO, were used, aged 3 mo to 4 mo at the start of surgery, weighing circa 350 g. The rats were housed in groups of two per cage, with ad libitum food/water and a 12-h light/dark cycle. The Fmr1 KO rats were generated by mating female heterozygous rats with male WT Long-Evans hooded rats obtained from Sigma Advanced Genetic Engineering (SAGE) laboratories, now part of Horizon Discovery, and the offspring of the female Fmr1 heterozygotes crossed to WT Long-Evans hooded rats (Charles River Labs), thus FMRP-/Y. The WT rats were littermate matched. There were three WT cages with n = 2 per cage, and three Fmr1 KO cages (ns = 2). All experiments were done blind to genotype, with animals being given a colored spot on their fur  and their cage number on their tail to identify them. The code was retained by someone independent of the study.Adult , according to the regulations of the Animals (Scientific Procedures) Act 1986 and under the supervision of the named veterinary surgeons of the University of Edinburgh.https://www.addgene.org) diluted with saline to a final dilution of 1:5 (titer: 5.77 × 1012 GC/mL), was infused using the 30G needle and a nanoinjector pump . A volume of 250 nL per site was infused at the rate of 50 nL/min (5 mins). A further delay of 5 min to allow the virus to diffuse was allowed before the needle was retracted slowly. Beginning with an immediate postoperative injection of rimadyl, the animals were allowed to recover for 2 wk, with postoperative care undertaken to minimize the discomfort to the animal as per institutional guidelines.The animals were anesthetized using isoflurane (1 to 3%) and mounted in a stereotaxic apparatus (Kopf). A hole was drilled in the skull, targeting the prelimbic (PrL) region of the mPFC at stereotaxic coordinates AP −3.2 mm, ML 0.8 mm , and DV at both 3.7 and 4.2 mm. The virus, AAV1.CamKII.GCaMP6f.WPRE.SV40  through the hole in the brain at the target site (DV 3.25 mm from skull surface), we prepared for lowering the GRIN lens. When the needle reached its target site, we paused for 5 min, and then the needle was retracted slowly; this created a cavity which was almost the size of the lens (1-mm diameter). Using a holder assembly, the GRIN lens was lowered into the brain slowly until it reached a site 3.25 mm below the skull surface. Dental cement (Super Bond) was used to secure the lens to the skull and four skull screws that had previously been drilled into location as solid anchor points. The lens was then covered with silicon to protect it until the final step of baseplate implantation.A period of ∼3 wk after lens implantation is required for the lens to become clear (clearance of implantation-associated gliosis). Using anesthesia again, the baseplate containing a working endoscope was lowered so as to target the center of the GRIN lens. We followed Inscopix guidelines to lower the baseplate until the circular border of the lens is seen, and then moved up slightly until we saw cells and/or blood flow, whereupon, at this point, we used dental cement to fix the baseplate. When the cement had dried and hardened, the scope was removed gently by undoing the baseplate screw, leaving the baseplate with the GRIN lens. The baseplate was then covered with the baseplate cover.2+ recordings. We used our three-dimensional (3D)-printed protector [called iHELMET; see Saxena et al.  associated with a GRIN lens are used to acquire calcium imaging videos at 20 Hz , 25. The2+ imaging . The animal from which recordings were being taken alternated across days. Video recordings were taken at the same time.A tube test contest consisted of two rats being placed in the holding boxes, one on each side. Both rats had an iHELMET (red or green) of which one contained a working camera and the other a dummy camera. Recordings were taken from both rats on alternate sessions . The rats then entered the tube and met in the middle at the metal grid which acted as a barrier. The trial started when the moveable barrier was unlocked. During the trial, the rats competed for dominance, during which a variety of behaviors were observed (identified blind with respect to genotype). Typically, the animals were together and relatively still for a few seconds. Thereafter, either one rat pushed the subordinate out of the tube (dominant) or the other rat withdrew of its own accord (subordinate), but various other behaviors occurred during such encounters. MOVE FORWARD occurred when either or both animals moved toward each other; PUSH occurred when animal A pushed against the metal grid and caused it to move, often stopped by animal B showing RESIST behavior. The roles of animals A and B in executing PUSH and RESIST generally alternated, but not exactly in time with each other. If animal A was showing PUSH behavior and animal B being pushed backward, this was called RETREAT ; however, if animal B withdrew of its own accord, this was classified as WITHDRAWAL . There were sometimes periods when neither animal moved or did much else; these were classified as STILLNESS, but this could include sniffing, grooming, or other activities that do not involve forward or backward motion. A trial was defined as ending when the first rat retreated or withdrew into the holding box from which it started. This rat was recorded as the “loser,” and the other was recorded as the “winner.” Each pair of rats underwent five trials each session, alternating their starting positions from left or right, to obtain a secure measure of dominance . One session of each of 10 sessions was completed each day. Randomization of the pair sequence and cage sequence was applied so that both changed every session. Trial latency was taken as the time (seconds) to complete one trial. All trials included both behavioral observation and CaThe data analysis proceeded in stages. With respect to behavior, the videos of all four animals were analyzed using BORIS software , with th2+ recordings, the field of view was preprocessed by first applying a low and high spatial band-pass filter (σlow = 0.0005 and σhigh = 0.5), then motion correcting by aligning each frame to a manually selected high-activity reference frame  to obtain a time series of instantaneous firing rates. The Kraskov MIS was then calculated independently between each neuron and each behavior . NeuronsFor all behavioral encoding neurons, the “reliability” was calculated as the percentage of within-session behavioral occurrences that a neuron fired, and their “consistency” was calculated as the percentage of sessions in which the neuron was classified as encoding a specific behavior. The event rate was calculated as the number of events divided by the duration of behavior (seconds) to yield events per second—these were then averaged across behavioral occurrences and across all sessions.2+ imaging studies for defining specific terms. When the camera displays a Ca2+ transient, we refer to this as an event, and events occur at a certain rate. Such transients happen in an ROI on the screen image of the endoscope, and these ROIs are considered as cells. Thus, events are cell firing. Longitudinal registration with respect to the x,y location of the ROI, identified via the stable physical location of ROIs in the camera image  and fixed using 4% formaldehyde in PBS . After cryopreservation in 20% sucrose, coronal sections (60 µm thick) were cut on a cryostat and then visualized  using a Nikon A1R confocal microscope. In all animals, the lens was targeted correctly to PrL as identified from the Rat Brain Atlas. For immunofluorescence staining, slices were permeabilized in PBS 10% NDS  0.3% Triton X-100 (Sigma Aldrich T8787) for 30 min, then incubated in PBS 10% NDS 0.1% Triton X-100 with 1:1,000 mouse Anti-GAD67 Antibody (Sigma Aldrich MAB5406 clone 1G10.2) or 1:500 guinea pig Anti-NeuN (SYSY 266 004) overnight at room temperature. The next day, three 10-min washes with PBS 0.1% Triton X-100 were performed, then slices were incubated in PBS 0.1% Triton with 1:200 donkey anti-mouse Alexa 647 (Thermo Fisher A-31571) or 1:200 goat anti-guinea pig Cy3 (Abcam ab102370), After three washes in PBS, slices are mounted in Fluoroshield with DAPI (Sigma Aldrich F6057). Confocal images were acquired with air objective 20× Plan Apo VC/NA 0.8, and the pinhole was set to 1.5 AU; 405-, 488-, 561-, and 633-nm laser lines were used to acquire the DAPI, GCaMP, Cy3, and Alexa 647 channels, respectively. Image processing was done with ImageJ (NIH).Standard statistical tests were used, as data demanded, with the SPSS statistical package v25 used for these tests. Statistical significance was defined with α < 0.05, with all F values and degrees of freedom stated. Specifications were described in the respective results and figure legends. All of the bar graphs with error bar represent mean ± SEM. Line graphs with error bars represents mean ± SEM.Supplementary File


Cardiometabolic disease (CMD) increases the risk of mortality, but the extent to which this can be offset by adherence to a healthy lifestyle is unknown. We aimed to investigate whether and to what extent a combination of healthy lifestyle is associated with lower risk of total mortality that related to CMD.2, and regular physical activity. CMD was defined as any of incidence of diabetes, coronary heart disease and stroke at baseline. Cox proportional hazards models were used to calculate hazard ratios (HRs) and confidence intervals (CIs) of the associations of CMDs and lifestyle factors with total mortality.Data for this prospective analysis was sourced from the UK Biobank with 356,967 participants aged 37 to 73 years between 2006 and 2010. Adherence to a healthy lifestyle was determined on the basis of four factors: no smoking, healthy diet, body mass index < 30 kg/mDuring a median follow-up of 13 years, a total of 21,473 death events occurred. The multivariable-adjusted HRs of mortality were 1.49 (95% CI 1.53–1.56) for one, 2.17 (95% CI 2.01–2.34) for two, and 3.75 (95% CI 3.04–4.61) for three CMDs. In joint exposure analysis, compared with CMDs-free and a favorable lifestyle, the HRs of mortality were 2.57 (95% CI 2.38–2.78) for patients with CMDs plus an unfavorable lifestyle and 1.58 (95% CI 1.50–1.66) for those with CMDs plus a favorable lifestyle. A favorable lifestyle attenuates the CMDs-related risk of mortality by approximately 63%. The mortality risk of CMDs-free people but have unfavorable lifestyle was higher than those who have over one CMDs but have favorable lifestyle.The potential effect of an increasing number of CMDs on total mortality appears additive, adherence to a healthy lifestyle may attenuate the CMDs-related mortality risk by more than 60%. These findings highlight the potential importance of lifestyle interventions to reduce risk of mortality across entire populations, even in patients with CMDs.The online version contains supplementary material available at 10.1186/s12967-022-03439-y. The prevalence of cardiometabolic diseases (CMDs), which defined as diabetes, coronary heart disease (CHD) and stroke, is increasing rapidly –3. An anLifestyle is an essential modifiable risk factor for risk of mortality. Emerging prospective studies have ascertained that adherence to a healthy lifestyle, including no smoking, regular physical activity, and healthy diet, was substantially associated with lower risk of mortality –12. At tIt might be hypothesized that CMD significantly increases the risk of total mortality, but adherence to a healthy lifestyle can help compensate this risk. The current study aimed to determine whether there is evidence of a potential additive effect of three CMDs on total mortality, and further to explore whether and to what extent a healthy lifestyle may counteract the risk of mortality associated with CMD using the 11-year follow-up data from the UK Biobank cohort.This is a prospective, population-based cohort study of participants enrolled in UK Biobank. Between April 2006 and December 2010, UK Biobank recruited 502,528 adults aged 40–69 years from the general population, participants attended one of 22 assessment centers across England, Scotland, and Wales, where they completed touchscreen and nurse-led questionnaires, had physical measurements taken, and provided biological samples . Since r2, and moderate physical activity two or more times weekly [We defined four healthy lifestyle factors on the basis of the American Heart Association guidelines: no current smoking, a healthy diet, body mass index < 30 kg/ms weekly , 15. Thes weekly –18. DetaCMDs included any of diabetes, CHD and stroke. Information on a baseline history of diabetes, stroke, and CHD was available by self-reported and hospital inpatient records . UK Biobank undertook comprehensive data linkage for mortality status. Date and cause of death were obtained from the National Health Service (NHS) Information Centre for participants from England and Wales, and from the NHS Central Register, Scotland, for participants from Scotland. The date of the latest mortality follow-up was September 30, 2021.Covariates including socio-demographics, behavioral and lifestyle factors, biological indicators, and comorbidities were adjusted in our analyses. Information on participants’ age, sex, ethnicity, qualifications, employment status, smoking status, alcohol intake frequency, hypertension, aspirin use, insulin use were collected using a self-reported questionnaire at baseline. The Townsend deprivation index was assigned as a continuous measure based on postcodes, which were derived from census data on housing, employment, social class and car availability, a higher index indicated more deprivation . Blood sThe characteristics of the sample are presented as mean ± standard deviation (SD) or percentage when appropriate across CMD numbers. Person-year was calculated from the date of recruitment to date of the death or censoring date, whichever occurred first.Cox proportional hazard regression models with age as underlying timescale were utilized to estimate hazard ratios (HRs) and 95% CIs for the associations of CMDs status and lifestyle factors with total mortality. The proportional hazard assumption was checked by tests based on Schoenfeld residuals, and the results indicated that the assumptions had not been violated. The basic model was adjusted for age , sex, Townsend deprivation index, qualifications, employment, ethnicity, cholesterol, C-reactive protein, triglycerides, hypertension, use of aspirin, and use of insulin, and we further fitted an additional model that CMDs and lifestyle were adjusted for each other. To further investigate whether and to what extent healthy lifestyle can counteract the harmful effect of CMDs on mortality risk, we created a variable with 6 categories, which combines CMDs  with the lifestyle factors . Last but not least, to distinguish the beneficial effect of a favorable lifestyle on mortality reduction among people with and without CMDs, the associations between lifestyle factors and mortality stratified by CMDs were explored, and interaction effect between lifestyle factors and CMDs status was tested by included an interaction term in Cox model.1 × factor1 + β2 × factor2 + β3 × factor3 + β4 × factor4) × (4/sum of the β coefficients). This weighted lifestyle score has considered magnitudes of the adjusted relative risk for each lifestyle factor as a combination of four factors. In consideration of environmental factors have been implicated in CMDs, air pollution  was additionally adjusted in the multi-variable models. Meanwhile, family history of CHD, stroke and diabetes was further adjusted in the sensitivity analyses.Subgroup analyses were further conducted to examine possible moderating effects of sex and age on the associations of CMDs status and lifestyle factors with mortality. Several sensitivity analyses were conducted to test the robustness of our results. We repeated all analyses after excluding all participants who died during the first 2 years of follow up to reduce the possibility of spurious associations due to reverse causation. We used a multiple imputation approach to impute the missing values for the non-systematically missing covariables. Five imputed datasets were generated and estimates were combined using Rubin’s rules. We further constructed a weighted lifestyle score on the four lifestyle factors by using the equation and repeated the main analyses: weighted healthy lifestyle score = . All Completed data in our final analysis were available for 356,967 participants in the UK Biobank. Table P for trend < 0.001).The mortality rates per 1000 person-year were 4.0 (95% CI 3.9–4.1) in participants without CMDs, 23.0 (95% CI 21.6–24.5) in those with more than one CMDs (Table 2) was associated with 7%-53% lower risk of mortality  and intermediate lifestyle  compared with those with an unfavorable lifestyle. The cumulative mortality rate of unfavorable lifestyle category was obviously higher than intermediate and favorable lifestyle Fig. . The samP < 0.001) .Further analyses stratified by CMDs status category with unfavorable lifestyle as the reference group confirmed that a favorable lifestyle was associated with a lower mortality risk regardless of CMDs status Table . Among pP for interaction > 0.05) , and there were significant interactions between CMDs or lifestyle and age in relation to mortality . In addition, the joint risk of CMDs and unfavorable lifestyle differed by different age subgroups (P for interaction < 0.001) . Results of associations of CMDs status and lifestyle factors with total mortality with multiple imputed data showed that the HRs remained significant (Additional file We further explored the stratified and combined associations of CMDs status and lifestyle factors with total mortality by sex and age subgroups. Results have not changed substantially. The association of CMDs and lifestyle with mortality was not varied across sex (In this large-scale population-based cohort study, we found that CMDs were associated with higher risk of total mortality, and the risk was dose-dependently related to the number of CMDs, there is an additive effect of three CMDs on total mortality. Furthermore, adherence to a healthy lifestyle was associated with a lower risk of mortality regardless of CMDs status, and the detrimental effect of CMDs on mortality might be offset by adherence to a healthy lifestyle by 63%. In addition, we found that the mortality of CMDs-free people but have unfavorable lifestyle was higher than those who have over one CMDs but have a favorable lifestyle.In our study, in patients who had only 1 CMD condition, we observed a HR for mortality of 1.49; for a combination of any 2 conditions, the HR was 2.17; and for a combination of all 3 conditions, the HR was 3.75. These results revealed that there was an additive effect of the association between CMDs and mortality. Given the clustering of different CMDs, it is important to elucidate the combined contribution of CMDs to risk of mortality. A large-scale study of 474,129 participants from UK Biobank found that CMDs might contribute to cognitive decline, and the potential effect of an increasing number of CMDs consistently appeared additive . ConsequTo the best of our knowledge, prior study has not examined the association of a combination of lifestyle factors and CMDs status with risk of mortality. Our findings showed that prevalent CMDs combined with an unfavorable lifestyle profile was associated with a more than twofold higher risk of mortality compared with CMDs-free and a favorable lifestyle. A previous study from Nurses’ Health Study and Health Professionals Follow-up Study in US showed that greater improvements in healthy lifestyle factors were significantly associated with a lower risk of CVD incidence or CVD mortality . A systeDiet is one of the most important modifiable risk factors that could represent a good and relatively easy point of intervention to reduce the risk of CVD . A healtThe protective effect of healthy lifestyle on CMDs can be explained by genetic pathways. One previous study using a polygenic risk score suggested that a favorable lifestyle was associated with a nearly 50% lower relative risk of coronary artery disease among participants at high genetic risk . AnotherNotable strengths of the present study included its prospective design and relatively large sample size, which provided us with adequate statistical power to evaluate the associations of CMDs status and lifestyle factors with total mortality. Despite these strengths, several limitations of our study need to be considered. First, CMDs only included cardiovascular diseases , 34 but In the current population-based cohort study, we found that prevalent CMDs were associated with higher risk of mortality, and the risk increased in an additive dose-dependent fashion. However, adherence to a healthy lifestyle may significantly offset the risk of mortality related to CMDs. Our findings emphasized the public health priority for healthy lifestyle of both primary prevention of individual CMDs and secondary prevention among patients with one CMD who are at risk of developing a comorbid disease.Additional file 1: Text S1 Diet score extended information. Text S2 Variable assessment. Table S1. Risk of all-cause mortality according to number of lifestyle factors. Table S2. The association of separate lifestyle factors with total mortality. Table S3. Mortality rate and hazard ratio of CMDs status and lifestyle factors in relation to mortality. Table S4. The association of CMDs status and lifestyle factors with total mortality in subgroup of sex. Table S5. The association of CMDs status and lifestyle factors with total mortality in subgroup of age. Table S6. Joint effect of CMDs and lifestyle factors on total mortality in subgroup of sex. Table S7. Joint effect of CMDs status and lifestyle factors on total mortality in subgroup of age. Table S8. Hazard ratios for cancers after exclusion of first two years of follow-up across CMDs status and lifestyle factors. Table S9. Joint associations of CMDs status and lifestyle factors with total mortality when missing covariates are imputed by multiple imputation. Table S10. Risk of all-cause mortality according to lifestyle factors category within CMDs status by constructing a weighted lifestyle score. Table S11. Risk of all-cause mortality according to lifestyle factors category within CMDs status after adjusting PM 10, PM 2.5, and NO2. Table S12. Risk of all-cause mortality according to lifestyle factors category within CMDs status after adjusting family history of CMDs. Figure S1. Participant flow diagram.


At bivariate analysis, the mEVs of the following NCAs were significantly cannabis related: severe microcephaly 2.14 × 1013, craniosynostosis 5.27 × 1011, nervous system 4.87 × 1011, eye 2.73 × 107, microphthalmos 4.07 × 106, anencephalus 710.37, hydrocephalus 245.64, spina bifida 14.86 and neural tube defects 13.15. At inverse probability, weighted panel regression terms including cannabis were significantly related to the following series of anomalies: nervous system, anencephalus, severe microcephalus, microphthalmos, neural tube defect and spina bifida from p = 5.09 × 10−8, <2.2 × 10−16, <2.2 × 10−16, 4.84 × 10−11, <2.2 × 10−16 and 9.69 × 10−7. At geospatial regression, this same series of anomalies had terms including cannabis significant from p = 0.0027, 1.53 × 10−7, 3.65 × 10−6, 2.13 × 10−8, 0.0002 and 9.76 × 10−12. 88.0% of 50 E-value estimates and 72.0% of mEVs > 9. This analysis therefore demonstrates both close association of cannabis exposure with multiple NCAs across space-time and also fulfills the quantitative criteria of causal inferential analysis. Conclusions. Nine NCARs on bivariate and six NCARs on multivariable regression were cannabis related and fulfilled quantitative epidemiological criteria for causality and are consistent with other series. Particular concerns relate to exponential dose–response effects demonstrated in the laboratory and epidemiological studies. Great caution with community cannabinoid penetration is warranted. Data indicate that cannabis is a significant environmental teratogen and thus imply that cannabinoids should be regulated similarly to the manner in which all other important genotoxins are carefully controlled by communities for their self-sustaining longevity and the protection of generations yet to come.Introduction. Of the many congenital anomalies (CAs) recently linked with community cannabis exposure, arguably the most concerning are neurological CAs (NCAs). We therefore conducted a detailed study of this in fourteen European nations. Methods. Congenital anomaly data were from Eurocat. Drug exposure data were from European Monitoring Centre for Drugs and Drug Addiction. Income from World bank. Results. The Netherlands, Spain, France and Bulgaria reported increasing rates of many NCAs. The NCA rate (NCAR) was higher in nations with increasing daily cannabis use when compared to those without ( With increasing recent reports describing many congenital anomalies (CAs) attributable to prenatal cannabis exposure, one of the most concerning groups of anomalies is that affecting the brain and neurological development generally ,2. With Many of these anomalies affected or were known to relate to brain development. Since congenital causes of being intellectually challenged can be severe, can remain throughout life and may often impose severe degrees of disability, this is an intrinsically severe and important group of congenital anomalies to understand. Moreover, since prenatal cannabis exposure had previously been linked with anencephalus ,7, autisBoth cannabinoid genotoxicity ,28,29,30We decided to include some eye anomalies in this group as the eye is formed as the confluence of two sets of tissues: one coming from the face, which is responsible for the formation of the anterior segments of the eye including the lens, and an outgrowth of the forebrain and optic nerve which is responsible for the posterior parts of the eye including the retina and neural elements . For thiFor all of these reasons, we were keen to study central nervous system anomalies in the European dataset both in terms of the bivariate associations and also in terms of their multivariable modelling within causal inferential and space-time paradigms. The following report sets out this detailed account. The specific hypotheses investigated were as follows: (1) Did previously described bivariate relationships between cannabis exposure and neurological CAs (NCAs) persist after multivariate adjustment; (2) Did these relationships persist when considered formally in a space-time context; and (3) Did these relationships fulfill the formal criteria of quantitative causal inferential analysis.Data on all available congenital anomaly rates were downloaded for each of 14 nations by each individual year from the European Network of Population-Based Registries for the Epidemiological Surveillance of Congenital Anomalies (EUROCAT) website  and analNations were categorized as being either high and/or rising daily cannabis use or low and/or falling daily cannabis use based on a recent European epidemiological study of cannabis use in Europe . Swedish data on cannabis resin THC concentration were not available. However, it was noted that the resin/herb THC concentration was virtually constant in nearby Norway at 17.7 so this ratio was multiplied by the Swedish cannabis herb THC concentration data to derive estimates of Swedish cannabis resin THC concentration. Similarly, Polish data for the cannabis resin THC concentration were unavailable. The resin to herb THC concentration ratio of nearby Germany was used to estimate the resin THC content in Poland from the known herb THC concentrations observed in Poland. Since geospatial analytical techniques do not tolerate missing data, the techniques of last observation carried forward or backwards for Croatia in 2018 and 2019 and the Netherlands in 2010 were used to complete missing data. Multiple imputation methods could not be applied to this dataset as it is not possible to apply multiple imputed datasets in panel or spatial multivariable regression techniques.R Studio version 1.4.1717, based on R version 4.1.1 from the Comprehensive R Archive Network and the R Foundation for Statistical Computing , was useThe presence of so many different metrics for cannabis exposure and consumption created a major problem for analysis as it was not clear which was the most appropriate metric to employ in any particular analytical scenario. Indiscriminate use of excessive covariates in a multivariable model would make models impossible to analyze by unnecessarily consuming degrees of freedom and thereby restrict ability to assess interactions. This issue was formally addressed by employing random forest regression using the R package ranger  with varR package plm  was usedThe formal tools of causal inference were used for this analysis. Inverse probability weighting (ipw) is the technique of choice to convert a purely observational study into a pseudo-randomised study and from these models it is entirely appropriate to draw causal inferences . All mulRaw datasets including 3800 lines of computation code in R has been made freely available through the Mendeley data repository at the following URLs: 10.17632/vd6mt5r5jm.1 and 10.17632/gr5ntsbp7p.1.Ethical approval for this study was provided from the Human Research Ethics Committee of the University of Western Australia number RA/4/20/4724 on 24 September 2021.Eye anomalies were included with disorders of the CNS as the eye is formed embryologically as a confluence of tissues contributed both from the face and the developing forebrain. The facial tissues contribute the anterior segments of the eye and the retina and neural tissues come from the brain. For this reason, congenital anomalies of the front section of the eye are considered in our companion manuscript looking at facial disorders and disorders of the eye as a whole are considered in the present paper.Daily drug use data were notable incomplete and is shown in The regression lines for tobacco, alcohol and amphetamine in The space-time patterns of anencephaly are as shown in The pattern of microphthalmia is shown in Rates of neural tube defects  have beeEye anomalies  were perRates of one of the compound cannabis metrics, last month cannabis use: cannabis resin THC are shown in The pattern of severe microcephaly plotted against the same compound cannabis exposure parameter is shown in When microphthalmos is considered, the areas of France and Spain are noted to have turned shades of purple across this period, which again indicates dually concordant elevated rates .The convergent pattern of neural tube defects across the continent is shown in p = 0.0265; model Adj.R.Squ. = 0.0029, F = 4.934, p = 0.0265). When this issue is considered by each anomaly, the appearance shown in p = 0.0205; model Adj.R.Squ. = 0.0360, F = 5.517, p = 0.0205).As described in the Methods section, this group of nations may be separated into those where daily cannabis use is increasing and those where it is decreasing or stable. When nations are dichotomized in this way, the appearance for all the anomalies considered together is as shown in p-values were extracted as shown in The next step is to move from the bivariate context to multivariable regression. However, it is unclear which selection of the 13 exposure covariates is most suitable for each CA as the set of independent variables. Random Forrest regression was used in tandem with variable importance plots to decide this issue. The six variable importance tables for the six CAs of primary interest are shown as −8 in the interactive model).This pattern is repeated across The next issue of relevance was to consider these data in their native space-time context, where important analytical confounding factors such as serial correlation, spatial correlation, random error structures and spatial autocorrelation could be properly and formally accounted for. For this reason, the geospatial links between countries shown in Final additive, interactive and lagged geospatial models for microphthalmos are presented in When neural tube defects are considered in Review of final models for spina bifida confirms this pattern with terms for the cannabis metric being noted in all the geospatial models studied .From each of the regression models, it is possible to extract applicable E-values. The E-value quantitates the expected magnitude of the correlation of some unknown confounding variable with both the exposure of concern and the outcome of interest to explain away an apparently causal effect. It is therefore an important part of quantitative causal inferential techniques.E-values for panel and geospatial models are presented in When these 50 E-value estimates are listed out in order as shown in 29.Similarly, these values can be ordered by the regression term involved, grouped into the primary covariate of interest  = 1.35; Neurological congenital anomaly rates were higher in nations with increasing daily cannabis use compared to those without increasing daily use (13) > craniosynostosis (5.27 × 1011) > nervous system (4.87 × 1011) > eye (2.73 × 107) > microphthalmos > (4.07 × 106) > anencephalus (710.37) > hydrocephalus (245.64) > spina bifida (14.86) > neural tube defects (13.15)  . After m (13.15) .p = 5.09 × 10−8, <2.2 × 10−16, <2.2 × 10−16, 4.84 × 10−11, <2.2 × 10−16 and 9.69 × 10−7  and fibroblast growth factor 8 (FGF8) released from midline axial structures and opposed by GLI3 dorsally. shh specifies the early forebrain and divides the visual fields into left and right halves . shh greSpinal cord formation happens under the tight control of morphogen gradients acting in each of the three dimensional planes . DorsallCannabis and cannabinoids disrupt many of the above-described morphogen gradients including sonic hedgehog (shh) , Wnt sigSince forebrain development is closely linked with shh signaling , shh inhCannabinoids can also interfere with morphogenetic signaling via epigenomic pathways as described below.The pattern which emerges from cannabinoid inhibition of these guiding morphogens will depend on the timing and dose exposure of the developing fetus to the xenoteratogen. As noted, the potential implications of disruption of these key controllers of embryological morphogenesis are very serious.The recent paper by Schrott and colleagues in 2021 identified several pathways as importantly disrupted in cannabis dependence in rats and humans including cerebral disorder, neurodevelopmental disorder, agenesis and organismal growth . After 1Importantly, cannabis dependence and withdrawal were associated with strong epigenomic links with many key neurotransmitter receptors of both the adult and developing brain including (Other positive hits include the following: GABR (Gamma-aminobutyric acid receptor) forms the main group of inhibitory receptors in the brain which fine tune and control the action of excitatory receptors. It is also involved in synaptogenesis ; GABRA (Details of the epigenomic DNA methylation changes at the opioid receptors are shown in Discs-large associated protein 2 (DLGAP2) is a synaptic protein previously linked with schizophrenia and autism which has been found to be epigenomically altered in cannabis dependence with changes heritable through the paternal line of rats into the nucleus accumbens of offspring . It was A powerful recent study comparing forebrain development in mouse, macaque and human embryos identified strong gradients of retinoic acid (RA) declining from the frontal pole to the premotor cortex as being primarily responsible for the surge in prefrontal cortex (PFC) neuronal numbers, spinogenesis, synaptogenesis, layer 4 lamination with its biomarker RORB and long-range connections to the mediodorsal thalamic nuclei . The RA As noted above, direct evidence for interaction by cannabinoids with RA has been previously described ,82,83.p-values of 0.0106 and 0.0291, and nine other hits for members of the ALDH1 family in cannabis dependence and withdrawal. The RA-associated genes cadherin 8 (CDH8) and protocadherin 17 (PCDH17) had one and 156 hits, respectively  and one hit each for RXRA, RXRG, RXRA and RORB in cannabis dependence or withdrawal with Bonferroni-adjusted p-values from 0.0009 to 0.0191 , which mediated the massive growth in dendritic spines and synaptogenesis of the human forebrain . RA was Cerebellin 2 (cbln2) has previously been linked with diverse neurological syndromes including obsessive compulsive disorder, Tourette’s syndrome and schizophrenia and the binding partners of cbln2, neurexins and GRID2 have been linked with autism and schizophrenia .p-value of 0.01723 (page 183) [p-value = 0.0086  and from p = 7.73 × 10−20 (page 239 in cannabis dependence). Cbln4 has a different distribution in the brain to cbln2 and mainly occupies subcortical and hindbrain sites [In the epigenomic screen of Schrott, there was one hit for a cbln2 intron in cannabis withdrawal on page 183 with a Bonferroni-adjusted age 183) . Interesin sites .The robo2-slit1 receptor–ligand system has previously been shown to be key to the exuberant development of the massive human neocortex characterized by very high numbers of neurons and synaptic connections ,114. TheOther observations from the online There are 15 references to brain disease made, including glioma , brain lesion , brain tumour , brain size , brain formation , morphology of the nervous system  and forebrain patterning . There are 67 references to neurological disease made, including organismal injury and abnormalities , brain lesion , morphology of enteric ganglia , morphology of spiral ganglia , myelination of sciatic nerve , hair cell morphology  and morphology of stratum pyramidale (3 genes). There are four references to cerebral disorders made, including cerebral disorder , development of the cerebral cortex , head development , cell viability of cerebral cortex cells . Interestingly, amongst the genes controlling cerebral cortex development is GLI3 which is a transducer of the sonic hedgehog pathway .There are 32 references to neurons made, including abnormalities of sensory neurons , neurite growth , abnormal morphology of neurons , proliferation of neural cells , neurogenesis of brain cells , morphology of neurons , neuronal development , neuronal outgrowth , cell movement of brain cells , apoptosis of cortical neurons  and developmental process of synapse formation . There are 11 references made to eyes, including eye formation , eye morphology , morphology of the eye , eye formation , eye morphogenesis , morphogenesis of camera-type eye  and morphology of eye cells . Genes controlling eye morphology include BMP4 and genes in the sonic hedgehog pathway.A rich literature indicates that both cannabinoid genotoxic effects ,28,29,30Of greater concern is that discrete jumps in the incidence of many congenital anomalies have been described for many anomalies at the highest level of cannabis exposure ,10,11. TThis study is remarkable for its notable consistency with previous reports in animals ,4,5 and Of particular importance in this discussion is the evidently graded series of NCAs from mildly impaired intellectual development to microcephaly, to severe microcephaly to anencephaly which demonstrates a clear and graded spectrum of adverse effects of cannabinoid exposure on infant and child neurological development.In view of this impressive and remarkably uniform body of work, it seems clear that the relationship described is causal in nature. Because of this notable confluence of large epidemiological datasets on this issue, the strength of both the statistical and mechanistic arguments for causality and the concordance with data from preclinical models, we feel that this relationship is widely generalizable to other places wherever data of sufficient size and quality exist for it to be reliably assessed.This study had a number of strengths including the use of one of the world’s largest and most comprehensive congenital anomaly datasets; the use of advanced statistical modelling; the use of inverse probability weighting and E-values to engage the techniques for formal quantitative causal inference and transfer the analysis from an observational study to a pseudorandomized study; the liberal use of multi-paneled maps and graphs to display multiple covariates across time and space; the use of bivariate maps, which is unusual, and the provision of extensive The present strong and now widely replicated results reported herein and in similar comparable studies internationally clearly place a major spotlight on the whole issue of the impacts of cannabinoids on neural development. Hence, the impact of the present report is to increase the research importance of these issues and justify further detailed studies in these areas. Further fine-resolution spatiotemporal and causal inferential epidemiological studies need to be performed. Mechanistic and particularly epigenomic studies need to be explored in detail to further understand these effects and are all strongly indicated.This study found that nine of the eleven NCAs in this European dataset were closely related to metrics of cannabis exposure on bivariate analysis and this relationship persisted after adjustment in all six of the NCAs selected for further detailed multivariable analysis. In this regard, these results are consistent with those from Canada, Hawaii, Colorado and the USA ,10,11 an


Staphylococcus aureus within their nasal cavity, and these colonized individuals are known to be at increased risk for SSTIs. To examine the evolution of the nasal microbiota of U.S. Army Infantry trainees, individuals were sampled longitudinally from their arrival at Fort Benning, Georgia, until completion of their training 90 days later. These samples were then processed to determine S. aureus colonization status and to profile the nasal microbiota using 16S rRNA gene-based methods. Microbiota stability differed dramatically among the individual trainees; some subjects exhibited great stability, some subjects showed gradual temporal changes and some subjects displayed a dramatic shift in nasal microbiota composition. Further analysis utilizing the available trainee metadata suggests that the major drivers of nasal microbiota stability may be S. aureus colonization status and geographic origin of the trainees. Nasal microbiota evolution within the congregate setting imposed by military training is a complex process that appears to be affected by numerous factors. This finding may indicate that future campaigns to prevent S. aureus colonization and future SSTIs among high-risk military trainees may require a ‘personalized’ approach.The human microbiome is comprised of a complex and diverse community of organisms that is subject to dynamic changes over time. As such, cross-sectional studies of the microbiome provide a multitude of information for a specific body site at a particular time, but they fail to account for temporal changes in microbial constituents resulting from various factors. To address this shortcoming, longitudinal research studies of the human microbiome investigate the influence of various factors on the microbiome of individuals within a group or community setting. These studies are vital to address the effects of host and/or environmental factors on microbiome composition as well as the potential contribution of microbiome members during the course of an infection. The relationship between microbial constituents and disease development has been previously explored for skin and soft tissue infections (SSTIs) within congregate military trainees. Accordingly, approximately 25% of the population carries  Additionally, it is clear that the presence of particular bacterial species can influence the prevalence of other species that are found within the microbiome10. These influences can be negative or positive and can occur through a variety of mechanisms. For example, Staphylococcus epidermidis, a commensal component of the human skin microbiome, produces phenol soluble modulins (PSMs) that function in conjunction with host anti-microbial peptides to kill Streptococcus pyrogenes and S. aureus13. Similarly, within the nasal cavity, members of the microbial community appear to prevent the establishment of other species; in vitro studies have shown that Corynebacterium pseudodiphtheriticum inhibits the colonization efforts of S. aureus and exhibits bactericidal activity against the pathogen16. Additionally, C. accolens produces free fatty acids that inhibit growth of Streptococcus pneumoniae within the nasal cavity17. Another skin commensal organism, Corynebacterium striatum, has a demonstrated ability to modulate S. aureus gene expression and elicit the transformation of the microbe from a virulent state to that of a commensal19. Conversely, some microbes cooperate with each other to promote colonization and sustained survival. For example, the presence of C. accolens correlates with S. aureus carriage in the nasal cavity and can enhance growth of S. aureus in vitro17. Likewise, the gut microbe, Bacteroides ovatus, induces the extracellular digestion of inulin to feed other species, such as B. vulgatus, within the intestinal tract20. This relationship between the species within the intestines has reciprocal benefits and actually increases the fitness of B. ovatus20. Thus, the interplay among the constituents of the microbial community is complex, and each member can positively or negatively influence the presence of other species within the microbial milieu.The composition of an individual’s microbiome varies widely depending on the sampled body site. Moreover, the microbial constituents that are present at various sites are influenced by a multitude of factors, including geographic location, lifestyle, ethnicity, culture, diet, and physical health7 as well as via simple exposure as an individual encounters other bacterial species within an environment and/or through interaction with others2. As such, the dynamic changes that occur within the human microbiome are being examined via longitudinal research studies that seek to examine the impact of environmental factors and/or personal interactions on the microbiome of individuals over a period of time. For example, longitudinal analysis of the gut microbiome revealed the influence of daily dietary changes on microbial composition; stability was linked to a more diverse diet21. Similarly, a longitudinal examination of the skin microbiome in adults revealed relative stability despite exposure to various environmental microbes22. In contrast, analysis of U.S. Air Force Academy cadets revealed an increase in the similarity of skin microbiomes, but not gut microbiota, among roommates upon cohabitation23. Finally, analysis of the nares of healthy individuals indicated a temporal stability in microbial composition as well as a lack of convergence of the nasal microbiomes among cohabiting couples8. Thus, longitudinal analyses of the human microbiome generate results that vary depending on the body site and cohort under examination.While a great deal of information has been gained from cross-sectional studies that define the microbiome of a particular site at a particular time, the composition and relative abundance of various species within the microbiome of an individual can change over time. These changes can be driven by host and/or lifestyle factors27. The relationship between the presence of particular microbes and disease development has been specifically examined for skin and soft tissue infections (SSTIs), which are frequently encountered maladies within the Military Health System30. Due to the close quarters and frequent physical contact associated with military training, the rate of SSTIs  is especially high among military trainees33. This high prevalence of SSTIs greatly impacts military readiness and creates an economic burden on the Military Health System30.The particular composition of an individual’s microbiome, in combination with host and environmental factors, can precipitate pathogenesis of commensal microorganisms and can result in infections. Furthermore, resident microbes can influence the ability of an encountered pathogen to colonize and cause diseaseS. aureus benignly colonizes the nares of approximately 25% of the population at any one time35, colonized individuals are known to be at increased risk for subsequent infections38. As such, S. aureus is the most frequently identified infectious agent within cutaneous abscesses40. Various studies have sought to identify links between the composition of the nasal microbiome, S. aureus colonization, and subsequent SSTI presentation among military recruits44. For example, among a cohort of military recruits presenting with SSTIs, colonization and infection with methicillin-resistant S. aureus (MRSA) appeared to be associated with a single acquisition event, thus underscoring the potential for host decolonization and environmental disinfection as strategies to prevent SSTIs in high-risk recruits43. In contrast, a separate S. aureus genomic analysis in two cohorts of high-risk recruits identified multiple transmission events of MRSA within platoons45. These two seemingly disparate analyses highlight the need to examine the microbiomes of individual recruits within a platoon to explore the potential exchange of microbes within the congregate setting experienced during military training. Moreover, given that individuals found in other congregate settings  are also at increased risk for SSTI development51, a greater understanding of microbiome evolution within the congregate setting may shed light on possible paths to prevent subsequent infections.Even though S. aureus colonization and geographic origin of the trainees may be key drivers of nasal microbiota stability within this population.Herein we investigated the evolution of the nasal microbiome of a single platoon of male U.S. Army Infantry trainees stationed at Fort Benning, Georgia. Individuals were sampled longitudinally within 24 h of their arrival at Fort Benning until completion of their training 90 days later. Given the congregate setting and uniform diet, schedule, and environmental exposures encountered by these military trainees, we originally hypothesized that we would observe an overall convergence of the individuals’ nasal microbiota to a more similar composition. However, microbiota stability varied dramatically on an individual basis; some subjects showed great stability, some subjects showed gradual temporal changes and some subjects showed dramatic shifts in nasal microbiota composition. Analysis of available patient metadata suggests that S. aureus colonization and SSTI52. Participants were recruited from four separate Infantry training companies (two in 2015 and two in 2016), each composed of ~ 200 trainees. To determine the evolution of the nasal microbiota of individuals within the congregate setting imposed by infantry training, 53 male subjects from a single platoon enrolled in 2015 were analyzed. Demographic information for the subjects is provided in Table A total of 627 male trainees were enrolled from 2015–2016 in an observational cohort study of S. aureus culture, while the second swab was stored at − 80 °C for future microbiota analysis. The day 0 swabs were used to define the baseline S. aureus nasal culture status/nasal microbiota. The subsequent swabs were used to identify any changes in S. aureus colonization status/structure and composition of the nasal microbiota. A total of 12/53 trainees (22.6%) were culture positive for S. aureus at day 0 , intermittently colonized (≥ 20% and < 80% positive cultures), or persistently colonized (≥ 80% positive cultures)  were intermittently colonized, and 9 subjects (17.6%) were persistently colonized. These numbers are in line with other studies concerning the prevalence of S. aureus in the nares54. At the individual level, we observed a variety of colonization patterns. Of the 22 intermittently colonized subjects, 12 were positive at only a single study visit. Of these, 7 were positive at one study visit, and negative at all following visits. Even among the 9 persistently colonized subjects, only 3 subjects were positive at each visit  and then longitudinally at days 14, 28, 56 and 90. On each day, one swab was immediately used for S. aureus is a known risk factor for SSTI development38. However, neither subject was colonized by S. aureus in the nares at any of the sample timepoints.Over the course of the 3-month study, 2 subjects developed a purulent SSTI: Subject 2013 on day 94; and Subject 2197 on day 22. Nasal colonization by To analyze temporal changes in the nasal microbiota, the remaining set of paired nasal swabs was subjected to DNA extraction. To reduce the likelihood of bias, the 261 swabs were randomly arrayed across three 96-well plates for the extraction. Additionally, each plate contained one well with a mock community (positive extraction control), and 2 or 3 wells containing no input (negative extraction control). Extracted DNA was used for amplification and MiSeq-based sequencing of the V1-V3 region of the rRNA gene; positive and negative sequencing controls were also included in the sequencing reactions. One sample (Subject 2103 Day 90) yielded no sequence data, even after multiple attempts; the remaining 260 subject samples yielded a total of 18,135,243 raw sequences, with an average of 69,751 sequences per subject. The three positive DNA extraction controls yielded 966,548 sequences . Similarly, the three positive sequencing controls generated 368,129 sequences  sequences. In contrast, the eight negative DNA extraction and three negative sequencing controls yielded 6299 sequences  and 12,808 sequences , respectively.55. Assembled sequences were analyzed using the R Studio implementation of DADA257. After quality filtering and chimera removal, a total of 10,265,661 sequences from subject samples remained (56.6%), representing an average of 39,483 sequences per sample. From the control samples, a total of 355,294 (positive extraction controls), 142,401 (positive sequencing controls), 787 (negative extraction controls), and 96 (negative sequencing controls) sequences remained. The sequences assembled into a total of 12,359 amplicon sequence variants (ASVs); these were aligned to the Silva database, with species designations added where possible. The percent abundance of each ASV for each sample is provided in Supplementary Table Forward and reverse sequencing reads were trimmed and assembled using FLASHCorynebacterium_1, Cutibacterium, and Lawsonella of Actinobacteria; Staphylococcus, Dolosigranulum, Anaerococcus, and Peptoniphilus of Firmicutes; and Moraxella, Haemophilus, and Neisseria of Proteobacteria. The abundance of these genera for each subject at each time point is shown in Supplementary Fig. Corynebacterium_1 was unstable, and showed a biphasic pattern of changes; a decrease was observed at day 14, followed by increases at day 28 and 56 and another decrease at day 90. The abundance of Firmicutes was relatively stable across the study, but the contributing genera shifted; a decrease in Staphylococcus and an increase in Dolosigranulum were observed. Finally, the abundance of Proteobacteria represented a mirror image of the Actinobacteria changes; there was an increase at day 14, a decrease at days 28 and 56, and another increase at day 90. Of note, the genera contributing to these increases were different over time: Haemophilus and Moraxella at day 14, but Moraxella and Neisseria at day 90. Statistical analysis revealed that the temporal changes were significant for all of the highest abundance phyla and genera with the exception of Anaerococcus and Peptoniphilus , nasal S. aureus colonization status  and subject geographic origin . As expected from a prior study41, positive nasal S. aureus culture and persistent colonization status were associated with higher abundance of Firmicutes and Staphylococcus and lower abundance of Actinobacteria and Corynebacterium_1  compared to those who were not colonized or were intermittently colonized   Fig. .Figure 359, which is calculated based on the structure of the community. For this analysis, beta diversity is measured between each pair of samples, and a matrix of values is returned. The resulting distance matrix was visualized using principal co-ordinate analysis (PCoA) wherein samples that are most dissimilar are plotted the farthest apart, and samples that are more similar are plotted closer together. PCoA visualization of the temporal beta diversity by individual subject is provided in Supplementary Fig. To further assess differences between the groups, beta diversity was assessed using the Bray–Curtis metricBeta diversity analysis combined with trainee metadata revealed unexpected interactions. Namely, distinct patterns were observed based on the geographic origin of the subjects; samples from the West and Northeast largely segregated to the upper quadrants and samples from the Midwest and South scattered throughout the plot . Reexamination of the participant questionnaires revealed that subject 2141 was the only subject in the platoon who self-reported the use of antibiotics in the 6 months prior to study enrollment. Thus, antibiotic use may be responsible for diversity differences observed in this individual. To eliminate this potential confounding factor, this subject was removed from downstream analysis. Indeed, upon removal of samples from this subject, the orientation of the PCoA shifted, rotating approximately 180°, with subjects from the Northeast and West now clustered in the lower quadrants of the plot . Pairwise analysis determined that the difference was driven by the comparison of Day 0 to Day 90 , the only significant comparison.To further explore the apparent divergence in the microbiota, the dispersivity of the Bray–Curtis distances over time was measured using PERMDISP analysisLastly, to measure the stability of the microbiota over time, the pairwise time point-to-time point variation between same-subject samples was calculated Fig. . This anTaken together, the temporal analyses indicated that the microbiota of the trainees did change significantly over time. However, contrary to our original convergence hypothesis, the nasal microbiota of the subjects was more similar at day 0 than at day 90. The change in the microbiota of each subject was greatest at the beginning and end of the study, suggesting a biphasic evolution in the nasal microbiota. An early change occurred (between days 0 and 14), followed by some stability in the microbiota for several weeks , and finally another large shift occurred (between days 56 and 90). Thus, nasal microbiota evolution within the congregate setting imposed by military training is a complex process that appears to be affected by numerous factors.62. Similarly, the prevalence of S. aureus nasal colonization of the trainees was comparable to previous reports54, with 39.2% of trainees not colonized, 43.1% of subjects intermittently colonized, and 17.6% of individuals persistently colonized  are clinically relevant in respiratory disease65. The reasons why some individuals are persistently nasally colonized by S. aureus while others are intermittent carriers or persistent non-carriers remains unclear. However, it is clear that the presence of S. aureus within the nasal niche has a dramatic effect on the nasal microbiome. For example, in subjects that are persistently colonized by S. aureus, the microbiota is dominated by S. aureus  from colonizing. In line with this, perhaps the nasal microbiome of intermittently colonized individuals represents a less robust microbiota that is unable to prevent temporary colonization by S. aureus, and, in the case of the trainees described here, Moraxella  and was conducted in accordance with the relevant guidelines and regulations of the institution. Informed consent was obtained from all study participants.A longitudinal cohort study was conducted, as described in detail elsewhereS. aureus by culture at the Benning Martin Army Community Hospital clinical microbiology laboratory as previously described73. The second swab was shipped to Uniformed Services University in Bethesda, MD for DNA extraction and microbiome analysis. Swabs were transported on dry ice and stored at − 80 °C until extraction, as described below.Paired nasal swabs were obtained upon arrival (day 0) and at each study visit  by study staff for a total of five study visits. At the initial study visit, trainees completed questionnaires for demographic and MRSA risk factor information. From each set of paired nasal swabs, one swab was utilized to determine the presence of S. aureus colonization status was defined based on the percent of study visits at which a subject was positive by culture, as follows: persistently colonized (≥ 80% positive); intermittently colonized (≥ 20% and < 80% positive); and not colonized (0% positive). Two subjects did not complete the five study visits, and no colonization status was assigned.8 cells were added to one empty well within each 96-well plate, for a total of three positive controls. As a negative DNA extraction control, eight wells  were left empty. The manufacturer’s instructions were used with the following adaptations. PowerSoil bead solution and Solution C1 were mixed, transferred to each well of the plate, and incubated at 65 °C for 15 min prior to mechanical lysis. DNA was eluted from the silica membrane using 100 µL nuclease-free water (IDT).DNA was extracted from the nasal swabs using the PowerSoil-htp kit (MoBio), which combines chemical and mechanical lysis. A total of 261 sample swabs were arrayed across three 96-well plates. To extract the DNA from the sample, the swab head was broken into the 96-well plate. The location within the plates was randomized, with the exception that no subject could have all five samples in a single 96-well plate to minimize any possible plate-to-plate confounders. As a positive DNA extraction control, a mock microbial community  was extracted in parallel. For this, approximately 1 × 1076. The following PCR conditions were used: 2.5 μL 10 × PCR Buffer, 4 μL dNTP Mix, 0.25 μL Takara LA Taq Polymerase (Clontech), 1 μL 27F (10 μM), 1 μL 534R (10 μM), 14.25 μL PCR Water (Qiagen), and 2.0 μL DNA. Reactions were performed in duplicate for 30 cycles, combined, purified using Agencourt AmpureXP (Beckman Coulter), and quantified using Quantifluor dsDNA Kit (Promega). Equivalent amounts of amplicons were pooled together, purified with MinElute PCR purification kit (Qiagen), and sequenced on an Illumina MiSeq along with the positive and negative sequencing controls. Due to a primer synthesis error, eleven of the patient samples yielded no reads. Thus, these samples were separately reamplified with corrected primers and sequenced. The resulting data were combined with the sequencing data from the other samples and then analyzed as described below.The V1–V3 region of the bacterial 16S rRNA gene was amplified using primers modified to include Illumina adapters, 27F (5′-AGAGTTTGATCCTGGCTCAG) and 534R (5′-ATTACCGCGGCTGCTGG) as previously described55.V1–V3 16S rRNA amplicons were sequenced (2 × 300 bp MiSeq read pairs) and demultiplexed. Read pairs were quality trimmed using trimBWAstyle.pl (-q 20), trimmed of primers using tagcleaner.pl , filtered to remove short reads (< 246 nt) and merged using FLASH 57. FilterAndTrim was executed using default parameters for maximum N (maxN), truncation after a specific quality score (truncQ), and removal of phiX; in addition, reads were of length less than 400 base pairs were removed (minLen), and the maxEE score was set to 2. The error model was performed using 2 × 108 bases, with multithread set to true. The data were dereplicated using derepFastq, and this dereplicated dataset was used for the sample inference algorithm. As the reads had already been overlapped, the step to merge forward and reverse reads was skipped. Chimeric sequences represented less than 3% of the amplicon sequence variants (ASVs), and were removed. Taxonomy was assigned using the Silva database (v132), with the optional species assignment.The 16S rRNA gene sequences were processed using DADA2 v. 1.10.177. Taxonomic filtering was performed to remove the following: ASVs from five phyla that were represented with two or fewer ASVs ; ASVs from one eukaryotic phylum ; and ASVs that did not classify to a phylum (180 ASVs). After taxonomic analysis, the positive and negative control samples  were removed from the dataset for the downstream analyses. Post-DADA2 processing, 3 samples contained less than 1000 reads, and were also removed .Processing and analysis were performed using Phyloseqphyloseq. Bray–Curtis distances were ordinated by principal co-ordinate analysis (PCoA) using phyloseq, and graphed using phyloseq and ggplot. All remaining graphs were generated using ggplot. Where shown, 95% confidence ellipses were plotted using stat_ellipse (ggplot). Graphs were arranged into grids using plot_grid (cowplots).Bar plots of percent abundance of phyla or genera were generated by agglomerating ASVs to the phylum or genus taxonomic level, keeping all taxa that classified to “NA” (NArm = FALSE), transforming read counts to relative abundance, and melting the phyloseq object into a matrix. Agglomerated phyla or genera that represented < 1% of reads at any time point were merged together using the summarize function of dplyr, as “Other”. Scatterplots of phyla or genera were generated using the agglomerated datasets. Alpha diversity  and beta diversity (Bray–Curtis) were measured using 58 was performed using the Galaxy web application . For each metadata set analyzed, the abundance of each agglomerated genus was uploaded as tabular data. Differences in abundance were tested using Kruskal–Wallis (P < 0.01) with the less strict one-against-all analysis. Differential features of 2 LDA were plotted.LEfSe analysist) or non-parametric  tests were employed. Where significant associations were found, pairwise Student’s t and pairwise Wilcoxon Rank Sum tests were employed, with Benjamini and Hochberg method (FDR) to correct for multiple comparisons. The vegan package was used to test one-way differences in beta diversity (PERMANOVA), for analysis of similarity (ANOSIM), and for beta dispersivity (PERMDISP) with one-way ANOVA to test for differences. Two-way differences in beta diversity were tested using pairwise PERMANOVA, using an adaptation of vegan’s adonis function78. Differences in Bray–Curtis distances were performed by pairwise Wilcoxon test with FDR correction for multiple comparisons.Statistical analyses were conducted using R. Differences in abundance of genera or phyla were tested using one-way ANOVA across a single grouping or using two-way ANOVA using time and another grouping. Normality of alpha diversity measures was determined using the Shapiro–Wilk test, in which only the Shannon index was found to be normal . Subsequently, parametric .Supplementary Figure S1.Supplementary Figure S2.Supplementary Figure S3.Supplementary Figure S4.Supplementary Figure S5.Supplementary Figure S6.Supplementary Figure S7.Supplementary Figure S8.Supplementary Figure S9.Supplementary Figure S10.Supplementary Figure S11.Supplementary Figure S12.Supplementary Table S1.Supplementary Table S2.Supplementary Tables.