Implementing clinical guidelines is challenging. To facilitate uptake, we developed a model of care (BetterBack Model of Care) and an implementation strategy to support management of low back pain in primary care. The aim of this study was to evaluate physiotherapists´ confidence, attitudes and beliefs in managing patients with low back pain before and after a multifaceted implementation of the BetterBack Model of Care. A further aim was to evaluate determinants of implementation behaviours among physiotherapists.This clinical trial was an experimental before and after study within a hybrid type 2 effectiveness-implementation trial. The primary outcome was Practitioner Self-Confidence Scale (PCS), secondary outcomes were the Pain Attitude and Beliefs Scale for Physiotherapists (PABS-PT) and Determinants of Implementation Behaviour Questionnaire (DIBQ). Data was analysed using repeated measures ANOVA and pairwise comparisons.ηp2 = 0.197, p < 0.001). Changes in PABS-PT were only significant after 12 months with higher biopsychosocial orientation,  and lower biomedical orientation, . Directly after the workshop, after 3 and 12 months, physiotherapists had high ratings on all DIBQ domains, (scores > 50) implying that all were potential facilitators of the implementation. However, after 3 months, all domains had significantly decreased except for organisation, social influence and patient expectation domains. However, after 12 months, organisation and social influence domains had significantly decreased while domains such as knowledge, skills and beliefs about capabilities returned to initial levels.One hundred sixteen physiotherapists answered a questionnaire before, directly after, as well as 3 and 12 months after implementation of the Model of Care. PCS improved over time with a large effect size post implementation (Physiotherapists´ confidence and biopsychosocial orientation increased after implementation and may have the potential to improve management of low back pain in primary care. The implementation behaviour showed mostly facilitating patterns but changed over time, pinpointing a need to repeatedly monitor these changes. This can inform the need for changes of implementation efforts in different phases and support sustainability strategies.NCT03147300 3 May 2017, prospectivly registered.ClinicalTrials.gov  Low back pain (LBP) is the leading cause of disability globally and one of the most common causes of visiting primary care . InternaIn Sweden, direct access and first line treatment by PTs is common. This patient management has been found to be cost effective, safe and well received by patients , 14. HowThis clinical trial was an experimental before and after study within a hybrid type 2 effectiveness-implementation trial. This hybrid design allows a dual testing of clinical and implementation interventions . The BetEligible participants were registered PTs regularly working with patients with LBP in all 15 primary care rehabilitation clinics in the Östergötland public health care region of Sweden. These 15 clinics formed three rehabilitation units based on municipal and geographical area and organisational structure in Östergötlands health care region. To be included, PTs had to attend a mandatory 2-day BetterBack☺ MoC workshop between March 2017 January 2018. Longitudinal follow-ups were done both by sending out questionnaires and by collecting questionnaires during outreach visits.Detailed information about how the content of BetterBack☺ MoC was developed and how the implementation strategy was planned can be found in the protocol . A multiWHY: To improve HCP confidence and biopsychosocial orientation in treating LBP through adoption of the BetterBack☺ MoC.WHAT: This would require the contents of the MoC to change impeding barrier behaviours such as: 1) Low confidence in skills/capabilities for LBP patient management; 2) Use of a biomedical treatment orientation rather than a biopsychosocial orientation; 3) Low awareness of the model; 4) Beliefs of negative consequences of the model.HOW: BetterBack☺ MoC content that differs from routine practice and is used to overcome the modifiable barriers are the following support tools: evidence based guideline recommendations; patient-centred coordinated care pathway; structured subjective and objective assessment proformas; STarT Back Tool ; clinicaWHEN/HOW MUCH/TAILORING: Intervention delivery and dosing is stratified based on the PTs` clinical reasoning regarding risk of pain persistence and progression towards individualised goals.PROCEDURE: Procedural descriptions for delivering the BetterBack☺ MoC are included in the following multifaceted implementation strategy. The MoC support team designed and implemented a 2-days workshop 13½ h) for the PTs. This was initiated between March 2017 and January 2018 with 12 to 22 PTs on each occasion in their own clinics. The workshop learning goals and learning activities were based on the Behaviour change wheel  and the 3½ h for The following is a description of the BetterBack☺ MoC content structured according to the TIDieR template for intervention description and replication .WHY: ToThe participants answered questionnaires before, directly after as well as 3 and 12 months after the workshop  mean change from before to 3 months after the workshop . The PCSThe determinants of PTs' behaviour during the implementation of the BetterBack☺ MoC were monitored directly after the workshop as well as after 3 and 12 months using the Determinants of Implementation Behaviour Questionnaire (DIBQ). The questionnaire was originally constructed based on the 18 domains of the Theoretical Domains Framework (TDF)  coveringηp2), where ηp2 = 0.01 is considered a small, ηp2 = 0.06 is a medium and ηp2 = 0.14 a large effect size [The PTs' baseline characteristics are presented as means with standard deviations (SD) and proportions (%). All PTs' outcomes were then analysed as differences between before or after workshop and 3 as well as 12 months after workshop  based on previous literature described in our study protocol [N = 50. RMANOVA pairwise comparisons with Bonferroni correction would require a sample size of at least N = 73.Considering a statistical significance of protocol . An inteprotocol . A similFigure ηp2 = 0.197. Pairwise comparisons directly after as well as 3- and 12-months post workshop (n = 116) showed that the PCS was significantly improved compared to before workshop, effect size ηp2 = 0.344, 0.277 and 0.454 respectively (p < 0.001) , while the PABS-PT biopsychosocial domain had significantly increased 12 months post workshop effect size ηp2 = 0.071, (p < 0.01).The RMANOVA displayed significant improvement in PCS over time with effect size p < 0.01). PTs ratings of patient expectations was maintained at 3- and 12-months follow-up  implying that all were potential facilitators of implementation with varying strengths. Directly after the 2-day workshop, expected determinants of implementations behaviour domains such as behavioural regulation, organisation, patients and beliefs about capabilities were small-moderate (DIBQ between 50 and 75) potential facilitators of the implementation process Table . Furtherup Table .Table 4The key findings in this study were improved confidence, change towards a more biopsychosocial orientation and high determinants of implementation behaviours among PTs after implementation of the BetterBack☺ MoC. Our hypothesis that physiotherapists' confidence, attitudes and beliefs in managing patients with LBP improve after a multifaceted implementation of the BetterBack☺ MoC was confirmed. The PTs´ confidence in managing patients with LBP improved already at directly after, 3 as well as at 12 months after the implementation. A study of PTs treating osteoarthritis had used a modified version of PCS and showed similar result after implementing a new physiotherapy training program in managing patients with osteoarthritis . In thatA change in attitudes and beliefs towards a more biopsychosocial orientation and less biomedical orientation was found at 3 months but were statistically significant first at 12 months after implementation. The PTs in our study reported a high biopsychosocial orientation from the start, leaving less room for improvement, but despite this a significant increase was still obtained. Several studies have demonstrated that HCPs´ attitudes and beliefs are associated with the treatment recommendations they give to the patients and that this may transfer to a better management of LBP in primary care , 53. A qAccording to the diffusion theory, people in a group vary from “early adopters” to “laggards” who adopt late to innovations . For exaIn the initial implementation process, PTs had high expectations for the BetterBack☺ MoC, and all DIBQ domains could have a potential facilitating role on the implementation process. This overall pattern of expected facilitating determinants of implementation behaviour directly after the 2-day workshop could be a result of successful motivation and initiation of the implementation. However, after 3 and 12 months of applying the model (volition stage), the initial expectations decreased in most DIBQ domains but maintained a potential facilitating role. Therefore, PTs initially high expectations may facilitate the implementation process to start with, but the decrease of these expectation during the volition phase underscores a need of sustainability strategies.With regards to capability related determinants of behavioural change goals “COM-B Capability”, linked DIBQ domains showed a pattern of short-term decrease followed by long-term sustainability. For opportunity related determinants of a behavioural change goal “COM-B Opportunity”, PTs maintained views that patients have positive expectations for the MoC (DIBQ patient domain) both in the short- and long-term. However, the organisation and social influence DIBQ domains showed a pattern of short-term sustainability but decreased in the long term. Furthermore, the innovation DIBQ domain decreased both in the short and long term. Similarly, for motivation related determinants of behavioural change goals “COM-B Motivation”, all DIBQ domains showed a decrease both in the short and long term except for beliefs about capabilities that returned to initial levels after 3 months.In contrast to the present findings, organisational resources and support are usually described as barriers to implementation efforts , 59, 60.The current implementation strategy showed that the impact of a well-planned guideline development, education and use of clinical champions gave good effect and mostly facilitated the implementation process. The implementation strategy did not fully meet the demands on the organisational level since these aspects, as measured by the DIBQ, showed a decreasing facilitation trend over time. Literature also supports assessing sustainability over multiple time points to capture its possible dynamic nature . The useThis is the first study using the DIBQ with longitudinal monitoring. A strength in this study was that it is representative for the health care region, with 123 eligible PTs and 121 educated and 116 analysed. The present study contained publicly financed PTs, however a previous study has compared PTs working privately and those publicly financed and differences in orientations emerged . The stuA possible strength in this study design was the use of a theory driven multifaceted implementation strategy . A recenPhysiotherapists' confidence and biopsychosocial orientation increased after implementation of BetterBack☺ MoC and may have the potential to improve management of LBP in primary care. PTs' high expectations measured by determinants of implementation behaviour may facilitate the implementation process to start with, but decreased during the volition phase, underscoring a need of sustainability strategies. The use of repeated screening with the DIBQ can support continuous adaptation towards a more focused implementation strategy.


After more than four decades of research and almost 100 attachment studies, the mechanisms of intergenerational transmission of attachment still remain unclear. To better understand the mechanisms moderating the associations of attachment orientations from one generation to the next, this empirical study examined the roles of 1) shared and non-shared environmental factors that characterize critical events in adulthood such as career choice, income and child care; 2) gender differences in attachment between parents  and their adult offspring  and their possible interactions. A sample of 321 families with G2 adults aged 18 and over and two G1 parents up to the age of 81 took part in this study. Both generations completed the Experiences in Close Relationships attachment measure as well as a comprehensive detailed measure of current core characteristics in adulthood  and demographic variables . The findings suggest that the associations between the attachment orientations of G1 and the attachment orientations of G2 were moderated by G2’s income, their G1 paternal income and employment status, whether G2 had children (G3) of their own, and their family status after controlling for the age of G2, and the age of both paternal and maternal G1. When the associations for both paternal and maternal G1attachment orientation with both their male and female G2 was analyzed separately, this accounted for 35% of the variance of males’ G2 attachment orientation. The discussion focuses on the contribution of these findings to attachment theory and draws clinical conclusions. Attachment theory attempts to explain the etiology of attachment and the manifestations of interpersonal attachment dynamics throughout the life span . AttachmThe attachment literature is divided between two empirical traditions, both claiming to derive from Bowlby’s attachment theory. The first, the developmental psychology tradition, is based on the original methodology used in in Bowlby’s studies which assessed the attachment orientations of infants using observational coding measures and especially the Strange Situation design . Later sThe second, the social psychology tradition, extends attachment theory to explore adults’ general attachment orientations with respect to relationships beyond one’s primary caregivers  using self-report questionnaires. The social psychology literature on adult attachment is vast see , 12. ForIt is important to note that although both traditions use identical terminology; i.e., secure attachment, anxiety and avoidant orientations, there may be differences in meaning. In the social psychology tradition, these terminologies are related to a more general approach towards relationship beyond the relationship with caregivers.Attachment orientations in adulthood are typically assessed on a continuum ranging from attachment-related anxiety to avoidance . AnxietyAttachment behaviors are thought to manifest when individuals are confronted with situations resulting in distress, illness or fear , 18. A pAvoidant individuals tend to deny their need for attachment, run away from emotion-laden situations and do not seek out close relationships involving interdependence , 12. AnxImportantly, evidence for a similarity of attachment orientation between infancy  and adulthood  is relatively weak e.g., –27. In aThese accounts underscore the importance of examining the factors that facilitate and inhibit the development of attachment in adulthood. Specifically, to better understand the etiology and the developmental trajectories of attachment from infancy through adulthood, it is crucial to investigate generation 2’s shared and non-shared environment in adulthood, which consistently explains a significant proportion of the variance in attachment throughout the life span.Researchers have emphasized the genome-wide evidence pointing to the important role played by children and shaping their family environment . In addiIn ancestral environments, local ecology was likely to have been similar between generations, although individual exposure  could have been unique to each individual within that ecology . HoweverThese presumed differences in the function of the attachment system over the course of development highlight broader questions concerning the core premises of traditional attachment theory that attachment is transmitted and then influences attachment relationships throughout the life span. In a recent meta-analytical review  externalenvironmentally secure base". Similarly, an "economically secure base" may provide significant support in times of need throughout life (such as grandchild care or periods of low income) and should moderate G1 attachment orientation associations with G2 attachment orientation as well.Here we suggest a different interpretation of the Verhage et al. claim and argue that the "caregiver" sensitivity still exists, but is applied to new content such as helping one’s adult offspring care for one’s grandchildren. Adult children’s needs take different forms than in childhood, such as economic support in times of unemployment or low income. In addition, we examine whether mechanisms can lead to more long-lasting associations with a child’s internal working models in adulthood in addition to one’s parents’ internal working models and the interaction between them. We expect that a more stable persistent external environment should moderate the associations of G1 attachment orientations with G2 attachment orientation in adulthood. Specifically, we argue that significantly more existential persistent experiences in adulthood  on the one hand, may serve as an "This leads to two hypothesis:H1: G2 attachment orientations will be associated with G1 attachment orientations.H2: The interaction between G1 attachment orientation and socio-demographic environmental factors should make a significant contribution to G2 attachment orientations and help explain the association of attachment orientation between G1 and G2.Although classic attachment theory is formulated in sex-neutral terms  differenResearch has shown that attachment styles also influence parenting –46. ThesThus, another important factor that may mitigate the gap in the literature regarding the transmission of attachment orientation from G1 to G2 relates to the interaction between parents’ (G1) gender and adult offspring’s (G2) gender. To the best of our knowledge, no studies have examined the extent to which G2 gender moderates the association between G1 attachment associations and anxiety or avoidance orientations in G2 anxiety and avoidance orientations. Hence, we also examined the associations between father’s and mother’s (G1) attachment anxiety and avoidance not only with a general measure of (G2) anxiety and avoidance but also whether paired attachment orientation is gender specific .Another theoretical direction that may lead to gender differences in the association of attachment between father’s and mother’s attachment and that of their offspring is the psychological differences of modeling and identity with one’s same gender parent. As far back as Freud’s psychosexual developmental model, theories have argued that males and females have different mechanisms for personality development as a function of gender. Hence, offspring may manifest differences in their tendencies to mimic, identify or build their identity based on their gender. Socialization processes may also come into play.Social Role Theory  accountsAccording to the evolutionary approach, at least in patriarchal communities men have a different role than females in general and regarding familial life in particular. In this framework, fathers are more responsible for the materialistic aspects of food and shelter, or in modern terms, economy and security. Mothers are focused on raising children and taking care of their relationships, or in modern terms, education. This evolutionary approach may seem outdated and definitely not politically correct. However, there is no question that to some extent this approach still prevails in certain sectors of society, in particular among older adults in a conservative culture such as Israel.These considerations led to two hypotheses:H3: G1 attachment orientations will have a stronger association with G2 same gender than with the orientation of G2 other gender.H4: Fathers’ anxiety orientation will be associated with anxiety orientations of their G2 [male]. Mothers’ avoidant orientations will be associated with the avoidant orientations of their G2 [female].H5: The interaction between fathers’ attachment orientation in general and anxiety orientation in particular and economic environmental factors will make a significant contribution to their G2’s attachment orientations.H6: The interaction between mothers’ attachment orientation in general and avoidant orientation in particular and familial environmental factors will make a significant contribution to their G2’s attachment orientations.The institutional review board and ethics committee of Ono Academic College have approved the study.SD = 15.9; mean age for the offspring sub-sample = 25.86, SD = 6.09; mean age for fathers’ sub-sample = 57.21, SD = 8.32; mean age for mothers’ sub-sample = 54.12, SD = 7.68) took part in this study. Vukasovic and Bratco , or medium (SD), β = .26, p < .001  but not for fathers (G1) with high incomes (+1SD), β = .11 p = .18  , β = .31, p < .001  or mothers in the middle age range (SD), β = .16 p = .08 , but not for younger mothers (-1SD), β = .04, p = .58   β = .30, p < .001 , or medium incomes (SD) β = .18 p = .001 , but not for fathers (G1) with low incomes (-1SD), β = .07 p = .37  , β = .27, p = .001 , but not when the father was not employed (-1SD), β = .06 p = .42   anxiety scores but not with mothers’ (G1) anxiety, supporting Hypothesis 4. However, surprisingly, disconfirming part 2 of Hypothesis 4, G2’s avoidance was positively correlated with fathers’ and mothers’ (G1) anxiety scores but not with fathers’ or mothers’ (G1) avoidance scores. G2 avoidance or anxiety scores were not associated with any of the environmental or demographic variables.Two hierarchical regression analyses for G2’s attachment avoidance and G2’s attachment anxiety were conducted to examine the extent to which G2’s attachment orientation could be predicted from their G1s’ attachment orientation after controlling for the age of G1, G2 and other demographic and environmental factors. None of the demographic or environmental factors predicted G2’s attachment orientations (either anxiety or avoidance). Entering the fathers’ and mothers’ (G1) attachment orientations to the regression analysis predicting G2’s attachment anxiety, after controlling for demographics and environmental factors, revealed that in support of Hypothesis 1, fathers’ but not mothers’ (G1) anxious attachment orientation (but not either parent’s avoidance orientation) predicted G2’s anxiety, supporting H4.Thus, for males, in contrast to the general sample, mothers’ anxiety (G1) did not predict male G2’s anxiety orientation. Using the same analytical procedure in predicting G2’s attachment avoidance revealed surprisingly that fathers’ anxiety (G1) but not avoidance predicted male G2’s avoidance. Thus, whereas fathers’ (G1) anxiety but not avoidance predicted G2’s avoidance, mothers’ (G1) attachment orientation did not predict any of the attachment orientations in male G2’s attachment orientations.Disconfirming Hypothesis 5 regarding males, no interactions of demographic or environmental factors were found between G1s’ attachment orientations in predicting male G2’s anxiety. However, in support of Hypotheses 2 and 6, two interactions accounted for 15% of the variance of male G2’s avoidance. Mothers’ avoidance (G1) interacted with whether her G2 had children or not in predicting her G2’s attachment avoidance. Fathers’ anxiety (G1) interacted with their employment status in predicting their G2’s attachment avoidance.SD), β = .43, p = .009 , but not when G2 did not have children (-1SD), β = .08, p = .51  , β = .43, p = .002 , but not when fathers were unemployed (-1SD), β = .08, p = .49   avoidance scores. In addition, female G2’s anxiety was significantly correlated with both fathers’ and mothers’ (G1) anxiety scores supporting Hypotheses 3 but not with fathers’ and mothers’ (G1) avoidance scores. Female G2s’ anxiety was associated with their education level and female G2s’ avoidance was positively associated with mothers’ (G1) employment status, supporting H2.Two hierarchical regression analyses for G2’s attachment avoidance and G2’s attachment anxiety were conducted to examine the extent to which G2’s attachment orientation could be predicted from their G1’s attachment orientation after controlling for the age of G1, G2 and other demographic and environmental factors. Only female educational level and her number of children predicted females’ G2 attachment anxiety but not avoidance level. Mothers’ (G1) employment status, whether she was employed or not predicted her female G2’s avoidance. No other demographic or environmental factors predicted female G2s’ attachment orientation. Entering the fathers’ and mothers’ (G1) attachment orientations to the regression analysis after controlling for demographic and environmental factors in predicting female G2’s attachment anxiety revealed that fathers’ (G1) and to a lesser extent mothers’ anxious (G1) attachment orientation (but not either G1’s avoidance orientation) significantly predicted female G2’s anxiety, confirming H4. In contrast, in partial support of Hypothesis 4, using the same analytical procedure in predicting female G2’s attachment avoidance revealed that fathers’ and mothers’ (G1) avoidance only marginally predicted female G2’s avoidance (both beta’s < .01).In support of Hypothesis 5, fathers’ income was found to interact with fathers’ (G1) attachment anxiety in predicting female G2’s attachment anxiety. This interaction contributed 3% of the variance of female G2’s anxiety. In addition, when predicting female G2’s avoidance, there were three interactions that together accounted for 6% of the variance of female G2’s avoidance. These were fathers’ (G1) avoidance interactions with their female G2’s income in predicting female G2’s attachment avoidance, mothers’ (G1) anxiety interacted with their female G2’s marital status in predicting their female G2’s attachment avoidance and finally fathers’ (G1) anxiety interacted with their income in predicting their female G2’s attachment avoidance.SD), β = .46, p < .001 , or medium (SD), β = .27, p < .001  but not for fathers (G1) with high incomes (+1SD), β = .07 p = .55  , β = .31, p < .001  or medium (SD), β = .20, p = .004 , but not low (-1SD), β = .08, p = .39  , β = .24, p = .007 , but not for unmarried females (-1SD), β = -.12, p = .26  , β = .36, p < .001 , or medium (SD), β = .24, p < .001 , but not when the fathers’ income was high (+1SD), β = .12, p = .15  ; p = .390; RMSEA = .010; CFI = .999; NFI = .977), supporting the main hypotheses predicting G2 attachment anxiety and avoidance  was used for a comprehensive examination of the model. It was made up of 6 constructs: fathers’ and mothers’ attachment anxiety and avoidance, and G2 attachment anxiety and avoidance. A confirmatory factor analysis (CFA) was conducted. The CFA model exhibited good fit with the data  during adulthood or an inhibitor of adult G2’s attachment tendencies and their associations with their G1s’ attachment orientations.The main goal of this study was to identify the reasons for the weak correlations between G1s’ attachment orientations and their G2’s attachment orientations in the social psychology literature. Here, we found interactions with environmental factors but also show the importance of gender in adult G2’s attachment orientations. The findings also suggest that an "We found that G1s’ attachment orientations (based on genetics combined with shared environment) significantly contributed to explaining the variance in G2’s attachment orientations. In addition, the inclusion of interactions with environmental factors increased the explained variance to 35%, which is close to the level reported for twin study designs in heritability research explaining males’ attachment avoidance, and added significantly to females’ attachment anxiety and avoidance variance. These results suggest that there is a significant contribution of environmental effects in the association of attachment orientations between G1s and G2 which are also dependent on G1s’ and their G2s’ gender.The findings suggest that these environmental characteristics are not necessarily determined early in life but rather at later stages for both G1s and G2. Thus, this research extends attachment theory to the moderating mechanisms that occur in adulthood which facilitate or inhibit the associations of G1s’ attachment orientation with their G2s’ attachment orientations. These results may also be in line with the view that genetically influenced attachment tendencies have a greater likelihood of being expressed in adulthood since individuals exert greater control over their own environments .secure economic base" whereas mothers are more traditionally responsible for helping extend the family to the next generation and providing more emotional support. These results do not conflict with Bowlby’s original theory of the role of the significant other, but point to additional functional and gender components that affect the accumulation of internal working models.The connections between parental attachment orientations and environmental characteristics may have important theoretical implications regarding the evolutionary origins of attachment. The results of this study support the traditional gender roles e.g. –54 of faWe speculate that fathers are more "responsible" for causing their offspring to be more or less anxious, perhaps due to the historic evolutionary perspective in psychology where the father is the anchor for consistency, food and shelter. Among male G2s, fathers’ anxiety (G1) was the only significant association with his male G2s’ anxiety. For female G2, the father (G1) had a greater association with the anxiety of G2 but the mother (G1) also contributed to the variance. Moreover, for male G2s, only their fathers’ anxiety (but not avoidance) (G1) was associated with their male G2s’ avoidance. For females, (G2), fathers’ and mothers’ avoidance (G1) were only moderately associated with G2 avoidance. The more in-depth analyses showed that these effects were dependent on environmental factors. These included mothers’ (G1) age , the G2s’ income , the fathers’ stability at work (employed)  thus, perhaps enabling G2 to avoid their anxious ("needy") father, whether the male offspring had children—that is G3—, for G2 females, the fathers’ income  and the female G2s’ income , for females who are married, . These results are also consistent with an evolutionary perspective. Mothers may be more "responsible" for attachment avoidance since from an evolutionary perspective they are more often in positions of nurturing and provide more "social" support, which when not available may be associated with more avoidant and distancing goals. Thus, whereas fathers were more highly associated with their G2’s anxiety, since they may serve, at least in paternalistic cultures, as a secure base and shelter, mothers’ avoidance associated more with their G2s’ avoidance since they play a more "social" role.We also speculate that the fathers’ contribution to their male G2s’ attachment anxiety may be driven as well by mimicking the male same- gender parent orientation. Identification processes with the same gender parent may be another explanation for the fathers’ contribution to their male G2s’ attachment orientation.However, the attachment process may be even more complicated. Fathers’ anxiety but not mothers’ anxiety also contributed to male G2s’ avoidance since a more anxious father may be associated with his G2’s feelings of insecurity that may lead to more autonomic and distant relations.Finally, the results suggest that these functional layers of parental roles remain important even for adult G2s. In this regard, the results are in line with Erikson  who streThe current research makes significant theoretical as well as practical contributions. It provide empirical evidence that can significantly narrow the gap in the literature by explaining the associations of attachment orientation between parent and offspring e.g. –9 by empeconomically secure base" which represents an individual’s needs through adulthood may be a moderator of attachment similar to the basic needs of food and shelter. Our battery of questionnaires included stressful and distressing situations that characterize adulthood that might create a more informative window on attachment related parenting.It has been argued that although every infant is born with the bias to become attached, the environment and in particular caregivers are critical in shaping individual differences in the quality of the attachment relationship . Simpson and Belsky  suggesteProximity seeking thorough adulthood to one’s parents is based on other environmental factors. Analogous to concrete nurturance and care in infancy, this is replaced by financial support and caring for grandchildren for adult G2. As early as 1990, Hazan and Shaver empirically showed the importance and association of attachment processes to the working environment. Here, we reveal an additional perspective on this association as a developmental life-long process. The same parental sensitive responsiveness and the mechanisms leading to a sense of secure base and a safe haven in infancy emerge in different contents to fulfill the same basic needs but with different means.The findings also shed light on gender differences in the association of gender differences in attachment styles . They goWhereas classic attachment theory is formulated in sex-neutral terms, and does not predict or explain the emergence of sexually differentiated styles  we extenOur findings run counter the assumption that the association of attachment from caregiver to G2 (to the extent that this occurs) has substantial implications for attachment relationships throughout the life span . We provIn addition to the theoretical contribution of these findings to attachment theory, they also make a practical contribution by providing guidance to explain G2 attachment. Another important practical contribution is in identifying mechanisms that can facilitate a more secure base that is not only dependent on emotional mechanisms but also on more concrete (i.e. economic) factors to promote greater psychological well-being. These suggestions should be further developed through future empirical research.Some caveats must be noted when interpreting the results. First, we used retrospective measures in a cross sectional design. Thornberry  defines Moreover, although it is not possible to conclude pure inter-generational transmission since we did not use a longitudinal design, we did show that demographic and socio-economic factors are likely to be important in our cross-sectional study and may account for part of the association between adults’ experience in close relationships and that of their adult offspring since both were assessed concurrently.Under this framework, we believe that our study design complied with Thornberry’s central design criteria. Thus we are in a strong position to draw conclusions not only with regard to the continuity of attachment orientations but also on mediating and moderating processes.Second, our study was conducted in a Western patriarchal society. Therefore, it is important to examine our evolutionary suppositions in a matriarchal or more gender-equal society. In particular it would be interesting to examine whether differences in parental roles and gender interconnections would affect the results in other cultures.Third, although we used a large dataset with a full range of controls we did not control for spouses’ characteristics or spouses’ parents’ characteristics.The current study points to significant environmental factors that moderate the cross-generational association of attachment orientation, and identifies factors that may narrow the unexplained gap in this association. The findings highlight the importance of gender in achieving more accurate insights into cross-generational attachment association. These more comprehensive mechanisms of attachment association between G1 and their G2 contribute to the literature and may also suggest more efficient ways to provide better consultancy intervention guidelines.S1 Fig(DOCX)Click here for additional data file.S2 Fig(DOCX)Click here for additional data file.S3 Fig(DOCX)Click here for additional data file.S4 Fig(DOCX)Click here for additional data file.S5 Fig(DOCX)Click here for additional data file.S6 Fig(DOCX)Click here for additional data file.S1 Table(DOCX)Click here for additional data file.S2 Table(DOCX)Click here for additional data file.S3 Table(DOCX)Click here for additional data file.S4 Table(DOCX)Click here for additional data file.


Objective: We explored the physiological background of the non-linear operating mode of cardiorespiratory oscillators as the fundamental question of cardiorespiratory homeodynamics and as a prerequisite for the understanding of neurocardiovascular diseases. We investigated 20 healthy human subjects for changes using electrocardiac RR interval (RRI) and respiratory signal (Resp) Detrended Fluctuation Analysis , Multiple Scaling Entropy , spectral coherence (CohRRI−Resp), cross DFA (ρ1 and ρ2) and cross MSE (XMSE1−4 and XMSE5−10) indices in four physiological conditions: supine with spontaneous breathing, standing with spontaneous breathing, supine with 0.1 Hz breathing and standing with 0.1 Hz breathing.Main results: Standing is primarily characterized by the change of RRI parameters, insensitivity to change with respiratory parameters, decrease of CohRRI−Resp and insensitivity to change of in ρ1, ρ2, XMSE1−4, and XMSE5−10. Slow breathing in supine position was characterized by the change of the linear and non-linear parameters of both signals, reflecting the dominant vagal RRI modulation and the impact of slow 0.1 Hz breathing on Resp parameters. CohRRI−Resp did not change with respect to supine position, while ρ1 increased. Slow breathing in standing reflected the qualitatively specific state of autonomic regulation with striking impact on both cardiac and respiratory parameters, with specific patterns of cardiorespiratory coupling.Significance: Our results show that cardiac and respiratory short term and long term complexity parameters have different, state dependent patterns. Sympathovagal non-linear interactions are dependent on the pattern of their activation, having different scaling properties when individually activated with respect to the state of their joint activation. All investigated states induced a change of α1 vs. α2 relationship, which can be accurately expressed by the proposed measure—inter-fractal angle θ. Short scale (α1 vs. MSE1−4) and long scale (α2 vs. MSE5−10) complexity measures had reciprocal interrelation in standing with 0.1 Hz breathing, with specific cardiorespiratory coupling pattern (ρ1 vs. XMSE1−4). These results support the hypothesis of hierarchical organization of cardiorespiratory complexity mechanisms and their recruitment in ascendant manner with respect to the increase of behavioral challenge complexity. Specific and comprehensive cardiorespiratory regulation in standing with 0.1 Hz breathing suggests this state as the potentially most beneficial maneuver for cardiorespiratory conditioning. It is characterized by sympathetic withdrawal and small parasympathetic dominance on RRI regulation , a typical, well-characterized cardiocirculatory pattern of sympathetic dominance and vagal withdrawal on RRI regulation  and standing with slow 0.1 Hz breathing (stand01), most probably contravene the principles of proportionality and superposition . This state was identified in the intensive care practice as the state of particular benefit for cardiopulmonary rehabilitation  and long term time scales (α2).Several studies have shown DFA exponent α to have a great power for probing complexity, as self-similarity across scale  and long term time scales (MSE5−10).MSE is another measure of signal complexity  successfully applied on physiological signals  and irregularity (MSE) are critical parameters of cardiovascular and respiratory system adaptability and physiologic plasticity , cross DFA and cross MSE  as the tools for estimating the level of cardiorespiratory coupling in four different physiological states. In order to investigate scale dependent changes of cardiopulmonary coupling of both complexity patterns, we separately analyzed cross DFA and cross MSE for short term and long term time scales .Finally, physiological non-linear signals like RRI  could have different, state-dependent patterns and these patterns could scale in dependent and mutually interrelated ways.Different forms of cardiopulmonary coupling . The protocol was approved by the Ethical Committee of the Faculty of Medicine, University of Belgrade (No. 2650/IV-24). Criteria for inclusion of subjects into the study were: absence of any health problems and an age between 20 and 45 years. Exclusion criteria were: subjugation to any therapy ; a history of cardiovascular, pulmonar or any other diseases; presence of any health disorders at the time of the assessment or in the time leading up to the performance of the experimental measurements  and pathological symptoms during the experimental procedures . For female participants, an additional criterium of exclusion was the second part of menstrual cycle  were excluded because of pathological symptoms discovered during the recordings.The study protocol was performed under controlled laboratory conditions at the Laboratory for Biosignals, Institute for Biophysics, Faculty of Medicine, University of Belgrade. It was conducted in a quiet, refreshing environment at a constant temperature (22 ± 1°C) during the experimental procedures for all subjects. Experiments were undertaken between 8 and 12 a.m., in order to control the circadian rhythm variability stemming from autonomic regulation Bojić, . All subECG and respiration signal acquisition was done by means of Biopac MP100 system . Main ECG lead registration electrodes were attached on the projections of clavicle bones and the grounding on the right ankle. The belt with resistive strain gauge transducer for continuous recording of breathing was placed slightly above the costal line. Both signals were sampled with 1,000 Hz frequency rate. We adjusted filters according to biopack instructions for general measurements: gain setting 10, low pass filter with 10 Hz and without high pass filter (DC-absolute respiratory measurement).We maintained controlled conditions during the recordings. Subjects were instructed to take a comfortable position which would allow them not to make any movements during the 20 min recording session. By visual analysis we agreed that there was no need for additional filtering of ECG signals. Respiration signal was low pass filtered (4th order Chebyshev filter) in order to erase little jitters physiologicaly appearing in the minimum level of expiration, but unrelated to research results  , related to fast oscillations, respiratory and predominately vagal control , related to slow oscillations, predominately of sympathetic control , reflecting the presence  as the measure of cross correlation in MSE domain  linear measures of heart rate variability: mean value and standard deviation ), the other for log (n). After ploting log (F(n)) vs. log (n), linear fit (regression line) was computed for the first 8 sample points (corresponding to n = 4–13). The slope of this regression line is regarded as the short term fractal scaling exponent α1. The same was done for the rest of the samples (following 16 points—n > 13), regarded as the long term fractal scaling exponent α2  in regression line  or later points for α2 . This occurred especially in sessions with slow breathing. Peng and co-workers noted that not all subjects exhibit crossover . Applying an algorhithm for detrended fluctuation analysis, we obtained two numerical series: one with values of log . We defined αA1 and αA2 as short term fractal angle and long term fractal angle with the abscissa, respectively. Additionally, our analytic tool characterizes the inter-fractal angle θ as a random variable, as well as its changes under the influence of orthostasis and slow breathing, which was analyzed using a probability density estimate procedure . In order to perform this analysis, the choice of inter-fractal angle θ with respect to the α1/α2 relation bypassed the possible calculation error for the case where slopes converge to infinite values  were calculated as additional non-linear measures. They are based on the concept of sample entropy which by definition represents a “negative natural logarithm of the conditional probability that two sequences similar for m point intervals remain similar at the next point within a tolerance r”  r = 0.15. The output of the algorithm consisted of two numerical series; one representing values of sample entropy for each scale factor and the other consisting of scale factor values . MSE1−4 was calculated as mean value from 1 to 4th sample points , and MSE5−10 as mean value from 5 to 10th sample points .Multiscale entropies (MSERRI−Resp) was calculated using the following procedure: equidistant resampled RRI and respiration signals were imported in OriginPro 8.6 . Within the Origin toolbox Analysis/Signal Processing/FFT/Coherence we made the following parameter settings: mean RRI for sampling interval of signals and Welch method for power spectral density estimation were chosen . After the execution of the algorithm, two numerical rows were generated; one with values of frequency [Hz], the other with values of RRI-respiration cross power (variance) distributed over frequency ranges [s2/Hz]. Then, we plotted them as x vs. y coordinates, respectively, to get cross power spectrum as a function of frequency . This usually corresponds with or near the location of breathing frequency (on the x-axis). We considered then that CohRRI−Resp represented the strength of the linear cardiorespiratory coupling. Values of CohRRI−Resp over 0.8 were assumed as high level/strong cardiorespiratory coupling. For a more detailed explanation of the application of the mentioned coherence method see RRI-respiratory coherence  parameters were calculated using the procedure described in Podobnik et al.  and FDFA,y(s) are square roots of detrended variances of their partial sums. For each scaling range, both short (s = 4–13) and long (s = 14–108), this coefficient was averaged within the corresponding limits. Short term and long term scale cross MSE's  were obtained by applying our custom made MATLAB program for calculating conventional cross sample entropy on signals previously prepared by coarse-graining procedure .where post-hoc Mann Whitney test with Bonferroni's correction for multiple measurements to compare all samples (Table 2).We stored all calculated results in a dataset crated with SPSS 19 . Statistical analysis was subsequently done by means of SPSS 19 toolboxes. We applied both visual checking of Gaussian distribution  and Shapiro-Wilk normality test. Both visual checking and Shapiro-Wilk normality test of each parameter in 20 subjects confirmed that our data had non-Gaussian distribution. Therefore, we applied the non-parametric Kruskal Wallis test with It is obvious even from visual observation  that cha1 vs. y2 relationship, with superior accuracy with respect to the existing relations of slopes  was significantly increased under the influence of body posture (supin-stand), slow breathing (supin-supin01) and in the state of standing with 0.1 Hz breathing (supin-stand01). The long term scaling exponent α2 was significantly decreased in supine with slow breathing and in standing with slow breathing positions, while during orthostasis alone α2 was not significantly changed. The inter-fractal angle θRRI significantly increased in all three statistical conditions. This change was a consequence of the individual and joint change of αA1RRI and αA2RRI (A1RRI increases both as a consequence of posture change (supin-stand) and a change of breathing regime (supin-supin01). αA2RRI was decreased by slow breathing in two statistical cases (supin-supin01 and supin-stand01). Change of posture alone (supin-stand) did not result with a joint (opposite) change of αA1RRI and αA2RRI, but by increase of αA1RRI only.State dependent changes of the coefficients are reported in d αA2RRI . αA1RRI RRI1−4) was significantly decreased under the influence of body posture (supin-stand) and the change of body posture combined with the slow breathing regime (supin01-stand01). The long term multiscaling entropy (MSERRI5−10) was increased by slow breathing in standing position , and decreased by slow breathing in supine position (supin-supin01). Joint (opposite) changes of MSERRI1−4 and MSERRI5−10 happened in the case of orthostasis with controlled breathing regime (supin01-stand01). The change of breathing regime (supin-supin01) only occurred when there was a change in MSERRI5−10 (decrease). Of particular interest was the result that in stand01 both fractal (α1RRI vs. α2RRI) and irregularity properties of RRI (MSERRI1−4 vs. MSERRI5−10) are reciprocally regulated. The analysis of scale dependent patterns revealed that both short scale (α1RRI vs. MSERRI1−4) and long scale (α2RRI vs. MSERRI5−10) parameters were also reciprocally regulated , but it increased in the case of controlled breathing regime (supin-supin01). α2Resp did not change significantly either with the change of posture  and in the condition of controlled breathing in supination . A significant decrease of α2Resp was registered during the condition of standing with controlled breathing regime (supin-stand01). Joint changes of α1Resp and α2Resp were in the opposite direction. The inter-fractal angle θResp did not change as a result of body posture change (supin-stand), but only under the controlled breathing regime .In the respiratory signal, mean value and standard deviation (mResp and sdResp) changed only with the change of breathing regime (supin-supin01) and not with the change of posture (supin-stand). We also visually evaluated the respiratory signal DFA plot for the crossover point  and applanalysis , 2. DetaA1Resp did not change as the result of a body posture change (supin-stand), but significantly increased in all conditions with the controlled breathing regime (p < 0.001). The angle αA2Resp also did not respond to the posture change (supine-stand) and slow breathing regime in supine position . αA2Resp significantly decreased in the regime of slow breathing combined with standing (supin-stand01). Joint changes of αA1Resp and αA2Resp (supine-stand01) were in the opposite direction. State dependent, statistically confirmed changes of angles αA1Resp and αA2Resp were identical to the changes of the respective slopes  was significantly decreased in conditions of combined standing position with slow breathing (supin-stand01). Long term multiscaling entropy (MSEResp5−10) increased only in the condition of combined standing and slow breathing regime . In the condition of joint MSEResp1−4 and MSEResp5−10 change, the parameters changed in opposite directions.Short term multiscaling entropy (MSE1Resp vs. α2Resp) and irregularity properties of respiratory signal (MSEResp1−4 vs. MSEResp5−10) were reciprocally regulated. The analysis of scale dependent patterns revealed that in this state both short scale (α1Resp vs. MSEResp1−4) and long scale (α2Resp vs. MSEResp5−10) parameters were also reciprocally regulated  was decreased under the influence of orthostasis (supin-stand). ρ1 significantly increased during slow breathing in supine position (p = 0.003) and standing with slow 0.1 Hz breathing (significance level of p = 0.072). Our statistical approach could not confirm state-dependent ρ2 changes. XMSE1−4 and XMSE5−10 decreased and increased, respectively, in the condition of orthostasis combined with slow breathing.RRI-respiratory coherence (CohIn recent years influence of slow breathing on heart rate variability (HRV) has been the focus or research  and parasympathetic (0.1 Hz breathing) activity on RRI regulation.As stated above, the actual evaluations of physiological RRI complexity measures were performed in the conditions of selective and total pharmacological Changes of posture and slow 0.1 Hz breathing are also significantly interrelated with the breathing pattern .In order to verify the reproducibility of an autonomic pattern characteristic for supin, stand, supin01, and stand01 we calculated the following linear parameters: absolute values, changes of mean, and standard deviation of RRI. The absolute values and their changes were in accordance with the literature  with respect to the state of their joint activation in stand01, where their non-linear RRI modulation appears to be additive, in the sense of Brownian noise (p < 0.001).A change of body posture . Change of α2RRI by the change of breathing pattern , both with respect to stand and with respect to supin01 . The overall conclusion is that sympathetic and parasympathetic drive in the state of combined orthostasis and slow breathing (stand01) synergistically contribute to the increase of α2RRI randomness, with greater contribution of parasympathetic drive with respect to sympathetic .Change of body posture  between supin-stand  and maximum between supin01-stand01 . Change of posture shifted αA1RRI toward unimodality . Voluntary slow breathing induced lighter αA1RRI regime homogenization with respect to the change of posture . The most distinguished regime of αA1RRI unimodality was in the circumstance of joint orthostasis with slow breathing . αA2RRI showed fewer characteristic changes, though bimodality could be observed both in orthostasis and slow breathing  with the most distinct unimodality in stand and stand01 conditions. These results are in accordance with the results of Castiglioni et al. .In order to quantify the observed interdependence, we propose the inter-fractal angle θanalysis . Figure i et al.  that a bRRI1−4 and MSERRI5−10 also showed opposite changes in stand01 condition, suggesting that orthostasis with slow 0.1 Hz breathing was the determinant factor of this type of change. The pattern of joint MSERRI1−4 and MSERRI5−10 change was opposite to the pattern of joint α1RRI and α2RRI change (MSERRI1−4 decrease and MSERRI5−10 increase) indicating that these non-linear parameters do not reflect the same, but potentially complementary information on non-linear variability , which was suggestive of the hypothesis that the body posture might be the crucial factor for the direction of change of MSERRI5−10. To the best of our knowledge, these are the first results on individual and joint effects of body posture and breathing regime on MSERRI1−4 and MSERRI5−10.MSE1Resp, α2Resp, θResp, αA1Resp, αA2Resp, MSEResp1−4, MSEResp5−10; Resp. In the supin-supin01 case, this increase was due only to the significant increase of α1Resp, while in supin-stand01 the change was obtained by the joint, opposite changes of α1Resp  and α2Resp  (A1Resp) and long term (αA2Resp) respiratory complexities are influenced in opposite directions by slow 0.1 Hz breathing coupled with a change in posture, making the change of θResp more enhanced only with respect to the θResp change by orthostasis  could hypothetically represent the result of confluent resonant cortical influences of posture maintenance motor system and slow 0.1 Hz respiration drive on brainstem autonomic respiratory network, considered to be an informational integrator of respiratory system , while their change was obvious and expected with the change of breathing frequency. The same pattern, regarding the three statistical cases, was observed for mean values of all non-linear parameters (ααA2Resp) . This reA1Resp (A1Resp ~48°). αA2Resp PDE , to potentially 5-modal regime in orthostasis (stand). Slow 0.1 Hz breathing introduced the shift of dominant pattern toward the regime of θResp with mean of ~18° (supin vs. supin01). Standing with slow breathing induced dramatic regrouping of θResp values into one dominant regime with a mean value of 26° and a low value of the standard deviation. The general conclusion is that the individual change of posture increases the number of modalities of all three angle parameters of respiratory complexity, while the individual slow breathing regime restricts this number. The maximal, apparent synergistic reductive effect on multimodalities of Resp angles was registered in the combined (stand01) state. This was in accordance with the fact that demanding posture requirements necessitate more adaptable respiratory patterns, also in non-linear domain, while cortical influences of slow breathing impose the inhibitory effect on the brainstem respiratory neural network chaotic properties and dictate a monomodal pattern of their non-linear operating mode. The state of stand01 could represent a qualitatively specific state, typical for the behavior of non-linear systems  and irregularity properties of respiration (MSEResp1−4 vs. MSEResp5−10) were reciprocally regulated. The analysis of scale dependent pattern revealed that in this state both short scale (α1Resp vs. MSEResp1−4) and long scale (α2Resp vs. MSEResp5−10) parameters were also reciprocally regulated , while this state was not characterized by the opposite change of the respective MSE scale pattern. This was also the case of the respective RRI parameters.Also in the case of respiratory signal complexity, in standing with 0.1 Hz breathing, both fractal (αegulated . OppositThese results show that:1 vs. MSE1−4) (α2 vs. MSE5−10) of both RRI and the respiratory signal in stand01 was not the consequence of calculation bias;The result of the scale dependent reciprocal pattern . The behaviorally most complex state in our experimental design, stand01, was characterized by reciprocal scale dependent and pattern specific cardiorespiratory response.RRI−Resp) appears to be sensitive on body posture change, while non-linear coupling  jointly and most dynamically change in the state of standing with 0.1 Hz breathing.Regarding cardiopulmonary coupling, our data report for the first time that these linear and non-linear mechanisms are independently and differently engaged with respect to the behavioral state, where linear coupling  was noted in supin01 (p = 0.003). This phenomenon was most probably the consequence of increased synchrony of RRI-Resp on short scales, due to the potentially maximal values of RSA in this condition.Cross DFA parameters ρ stand01 . State dand01 . A general conclusion might be that (a) ρ1, ρ2, XMSE1−4 and XMSE5−10 are not dependent on the body posture change; (b) cross DFA and cross MSE coupling regimes are most probably independently regulated, referring to different patterns of change with respect to the physiological state . The results speak for the ρ1 positive correlation with the increase of vagal modulation to the heart, while XMSE1−4 and XMSE5−10 could correlate with synergic slow breathing and posture control.Cross MSE parameters X1 = 0.072, pXMSE5−10 = 0.051) and solid statistical confirmation for XMSE1−4 (p < 0.0001), a general picture of state dependent changes of cardiopulmonary complexity identifies standing with slow 0.1 Hz breathing as the most composite but the best defined state. Regarding cardiopulmonary coupling, this state was characterized by a decrease of short scale irregularity coupling (XMSE1−4) and increase in short scale self-similarity coupling (1ρ). This opposite pattern of short scale cardiopulmonary coupling for ρ1 and XMSE1−4 was statistically confirmed only for the state of joint orthostasis with slow 0.1 Hz breathing, suggesting that only joint enhancement of volitional 0.1 Hz drive with sympathovagal modulation on the RRI could result in specific short scale coupling pattern. This cannot be attributed to vagal modulation only , but to the action of hierarchically higher structures on the sympatho-vagal pattern that potentiates short scale coupling in self-similarity and reduces short scale coupling in irregularity. The pattern of short scale cardiopulmonary coupling specific for the state stand01 could be a feedback information of particular importance for the higher order cardiopulmonary network . These results need further evaluation.The role and the presence of long range components in this pattern of cardiopulmonary coupling could be followed by statistically discrete increases of MSERRI−Resp, suggesting that state dependent cardiopulmonary interaction is a multilevel, dynamically controlled phenomenon.Finally, non-linear parameters of cardiorespiratory coupling had different patterns of state dependent change with respect to a linear effect; CohAs a limited view, when speaking about cardiorespiratory coupling, we speak about mutual, bidirectional interaction between cardiac and respiratory oscillations  is ~1:2. In order to obtain sufficiently long RRI and respiratory signals for selected analysis and in physiological steady state of cardiorespiratory regulatory mechanisms, we designed 20 min registration sessions for each physiological state. Controlled 0.1 Hz breathing with i/e 1:2 was too fatiguing for examinees and we were compelled to apply the paced breathing in i/e relation 1:1.The literature suggests that HF HRV and RSA are greater when breathing with a regime of low compared to high i/e ratio  and synergetic in the state stand01.Regarding α2RRI, individual sympathetic and parasympathetic activation contribute to the increase of α2RRI randomness, with greater contribution of parasympathetic drive with respect to sympathetic. In the state of combined orthostasis and slow breathing (stand01) this contribution appears synergetic.Regarding αA1RRI, αA2RRI, and θRRI revealed that baseline physiologic, healthy regime (supin) was characterized by the widest population (group) spectrum of α1RRI, α2RRI, and θRRI coefficients, which was in accordance with the results of  and irregularity properties of RRI (MSERRI1−4 vs. MSERRI5−10) are reciprocally regulated. The analysis of scale dependent patterns revealed that both short scale (α1RRI vs. MSERRI1−4) and long scale (α2RRI vs. MSERRI5−10) parameters were also reciprocally regulated (1RRI vs. α2RRI and MSERRI1−4 vs. MSERRI5−10 relationships) and vertical (α1RRI vs. MSERRI1−4 and α2RRI vs. MSERRI5−10 relationships).Additionally, in stand01 both fractal  were robustly sensitive only to breathing regime change, while subtle PDE changes were observed as the result of the posture change. These changes were described mostly as a different number of operating regimes induced both by the change of posture and by the voluntary breathing regime. Demanding posture requirements necessitate more adaptable respiratory patterns, also in the non-linear domain, for the expected environmental  challenges. Only one constant, long lasting and repetitive behavioral task, as was the slow 0.1 Hz breathing, qualitatively changed the feature of multimodality into a dominant monomodal respiratory pattern. Cortical influences of posture maintenance and slow breathing might jointly impose the inhibitory effect on brainstem respiratory neural network complexity properties and dictate monomodal pattern of their non-linear operating mode . The patients/participants provided their written informed consent to participate in this study.TB and MP designed the experimental protocol. TB, MP, and ZM recruited the research subjects. ZM performed the experiments and data acquisition. MP supervised the experiments. ZM performed the data analysis under MP, AK, and TB supervision. AK and MP programmed the algorithms. AK and ZM addressed the major computational tasks. ZM and TB did the scientific writing. TB gave the physiological interpretation of the data. All the authors approved the final content of the manuscript.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Arabidopsis nucleus. We show there is temporal separation of the clock control of cellular biosynthesis and transport of TDP at the transcriptional level. Taking advantage of the sole reported riboswitch metabolite sensor in plants, we show that TDP oscillates in the nucleus. This oscillation is a function of a light-dark cycle and is independent of circadian clock control. The findings are important to understand plant fitness in terms of metabolite rhythms.In plants, metabolic homeostasis—the driving force of growth and development—is achieved through the dynamic behavior of a network of enzymes, many of which depend on coenzymes for activity. The circadian clock is established to influence coordination of supply and demand of metabolites. Metabolic oscillations independent of the circadian clock, particularly at the subcellular level is unexplored. Here, we reveal a metabolic rhythm of the essential coenzyme thiamine diphosphate (TDP) in the  Arabidopsis nucleus, which is driven by light-dark cycles and not by the circadian clock. This study provides insight into our understanding of the optimization of plant fitness.Noordally et al. show that the essential coenzyme thiamine diphosphate exhibits a daily rhythm in the  It is required for functionality of key regulatory enzymes of glycolysis, the citric acid cycle, the oxidative pentose phosphate pathway1, carbon fixation through the Calvin cycle2, and the non-mevalonate isoprenoid biosynthesis pathway, from which thousands of metabolites are derived including chlorophyll and several phytohormones in plants3. However, correlation between the levels of TDP and operational output of these pathways is unknown.Metabolic homeostasis is achieved through the spatiotemporal control of the associated network of enzymes, many of which are vitally dependent on coenzymes for activity. The circadian clock is established to influence the dynamic behavior of this system, in particular coordinating the supply of metabolites with demand. The coenzyme TDP, a form of vitamin B7. Condensation of the two moieties is carried out by TH1 in the plastid to produce thiamine monophosphate (TMP)  has been recently identified, it is predominantly localized to the mitochondria11.Elucidation of the biosynthesis de novo pathway of TDP in plants has been completed over the last several years. It comprises separate branches for the biosynthesis of the thiazole and pyrimidine moieties, by the action of THI1 and THIC, respectively, in the plastid . Homologs have been recognized in Arabidopsis and maize18 named TPC1 and TPC2  that rely on it. One pertinent question therefore is how TDP, a polar molecule, is delivered to the organellar apoenzymes. However, identification of these transporters largely remains elusive and surprisingly has received little attention. To date, no transporter of TDP across plastid membranes has been identified. Although, very recently, the single member of the nucleobase cation symporter family in 19. Comparably, overexpression of transketolase results in TDP auxotrophy-like symptoms, as other enzymes are divested of the coenzyme, and negatively impacts plant health20. We must also consider that metabolite levels need to be temporally controlled to be in tune with the diurnal needs of the plant. For example, carbon metabolism needs to be coordinated such that sugars are stored during the day to be used for metabolic activities at night, and is reported to be tightly integrated with the circadian clock21. As a corollary, it follows that TDP supply needs to be tightly coordinated with the needs of enzymes dependent on it as a coenzyme. Thus far, there have been few reports on the regulation of TDP levels in plants with the predominant exception of the mechanism of control of THIC expression via the RNA aptamer in its 3′-UTR, termed a riboswitch  peaking in abundance in the first half of the day. This peak in abundance is derivative of light-dark cycles that is independent of the circadian clock regulation.Here, in addition to demonstrating the importance of TDP for central metabolism and thereby growth and development, we report on components of the intracellular transport of TDP and/or its precursors in the plant model 1 vitamer thiamine is stored, and upon germination is converted to TDP . In order to test whether core metabolism is indeed dependent on TDP levels, we grew the thiC mutant in the presence of a range of thiamine concentrations (0−1.5 μM), spanning physiologically measured levels. Supplementation with thiamine had little effect on wild-type growth  is required for growth to approach wild-type levels  and TPC2 (At5g48970) have been shown to be involved in TDP transport through yeast-based assays18, their relevance was not investigated in plants. Note, Arabidopsis TPC1 and TPC2 should not be confused with the Arabidopsis two-pore channel (TPC) protein at locus At4g03560. To investigate the functional significance of TPC1 and TPC2 in planta, seeds of putative mutant lines were obtained from the available collections  and GK-236B06 (annotated tpc1-2), as well as GK-870B10 (annotated tpc2-1) and SAIL_127_G03 (annotated tpc2-2) were analyzed. In all cases, the T-DNA insertion lines could be isolated to homozygosity. PCR analysis established insertion of the T-DNA in intron 3 of SAIL_130_D09 (bp 1759), exon 6 of GK-236B06 (bp 2623), intron 2 of GK-870B10 (bp 971) and intron 3 of SAIL_127_G03 (bp 1302)  levels in the respective mutant partner line  in mutant lines by high performance liquid chromatography, which were similar to those in the corresponding wild type grown under the same conditions  for a sole embryo-based problem  in planta and yeast12, as well as in Escherichia coli13. Our attention was drawn to this transporter (using the NCS1 nomenclature from here) during the course of this study because while there is strong homology at the amino acid level between NCS1 and nucleobase permeases in Saccharomyces cerevisiae, such as DAL4, FUI1 and FUR4, there is also significant homology to THI7 , which has been shown to be involved in import of thiamine in S. cerevisiae28. Moreover, homologs of NCS1 in bacteria cluster with thiamine biosynthesis and salvage genes14. Indeed, the latter recent study indicates that NCS1 may also transport the nonphosphorylated pyrimidine moiety precursor of TDP, hydroxymethylpyrimidine . Indeed, we tested NCS1 for complementation of the yeast knockout mutant CVY4 (MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 thi4Δ::his5+thi7Δ::KanMX4 thi71Δ::LEU2 thi72Δ::LYS2)27. This yeast strain is unable to either biosynthesize or transport thiamine and can only grow upon supplementation with at least 120 μM thiamine29. Neither the full-length NCS1 sequence nor a truncated version missing the N-terminal 97 amino acids (and consequently devoid of the plastid targeting peptide) could restore the yeast CVY4 strain to prototrophy  reporter gene under control of the region immediately upstream of the transcriptional start site of NCS1 (promNCS1-GUS). In all lines examined, promoter-driven GUS activity, although generally present in leaves and cotyledons, is much more pronounced in the vascular tissue from young and mature leaves and the central cylinder of roots but is weak in flowers and developing siliques as well as developing seeds  did not reveal an incongruent phenotype under standard growth conditions on soil corroborating a previous study12, or in sterile culture 7, which cannot make HMP de novo and develops a chlorotic phenotype 6 days after germination7. As shown earlier in this study, supplementation with at least 150 nM of thiamine in the culture medium suffices to restore a wild-type phenotype to the thiC mutant but levels below this are limiting for growth. Interestingly, we noted that the double-mutant ncs1-1 thiC was not rescued to the same level of development as thiC alone upon thiamine supplementation 31 to assess the diel expression pattern of the known biosynthesis genes, as well as TPC1/TPC2 and NCS1 at the transcriptional level. While the data were not available for all genes, both THIC and THI1 the key biosynthesis genes, as well as TPC1 and NCS1 indicated rhythmic regulation among three datasets 33. While THIC oscillation was confirmed, the biosynthesis transcript THI1 also showed a clear significant oscillation, although the change in amplitude was less pronounced than that of THIC 34 34 Fig. . The lation Fig. . Interesion Fig. , reflect1 vitamers in Arabidopsis grown under circadian conditions, thiamine levels could not be detected, TDP was deemed not to oscillate and the TMP vitamer was observed to change in abundance—but only for a single period24. Thus, it could not be conclusively deciphered if B1 vitamer levels demonstrate diel oscillation. To probe this further, we first measured B1 vitamer levels in shoots of seedlings grown under equinoctial light-dark cycles. In our experiments, all three vitamers could be detected but none showed significant oscillation over 72 h , or is retained when TDP levels are low, referred to here as THIC-INTRON RETAINED (THIC-IR)  conditions by qPCR using primer pairs specific to each variant  than that of THIC-IS (8 h into the light)  reporter gene fused to the 3′-UTR of THIC with expression under control of the CaMV 35S promoter, thereby removing promoter-controlled circadian regulation but retaining TDP responsiveness , which is proportional to TDP abundance, was monitored under equinoctial light-dark cycles and circadian conditions. A robust rhythm of LUC-IS could be observed under equinoctial light-dark cycles, which peaks about 4 h into the photoperiod  Fig. . As the ant Fig. . Under eht) Fig. . Analysiust Fig. . This obrr9 Fig. , implyinant Fig. , but notant Fig. . As the ess Fig. . The abuiod Fig. . This osons Fig. . Thus, eiod Fig. . These d40. Metabolite production is inherently dependent on the activity and regulation of the corresponding biosynthesis enzymes. Regulation of enzyme activity is investigated extensively with regard to post-transcriptional and post-translational modifications. However, regulation based on the availability of the corresponding coenzyme, when used, is virtually ignored. As the level of enzyme activity that depends on a coenzyme is directly proportional to its stoichiometry with its corresponding coenzyme, i.e. amount of apoenzyme to holoenzyme, then control of coenzyme levels warrants attention. In this case, we have studied the coenzyme TDP, essential for the activity of numerous key regulatory enzymes within core metabolism. We demonstrate that supply of this coenzyme is not only essential but that its limitation also restricts the abundance of other central metabolites , pyruvate decarboxylase (glycolysis), α-ketoglutarate dehydrogenase (TCA cycle) and transketolase (oxidative pentose phosphate pathway) in all organisms. Notably, the latter enzyme is also involved in the Calvin cycle, responsible for CO2 fixation in plants. These are all key regulatory enzymes within their respective pathways and thus, the efficiency of the system is inherently dependent on TDP supply. As the final step of TDP biosynthesis takes place in the cytosol, it is presumed that it needs to be transported into the organelles to facilitate catalysis by the endogenous enzymes dependent on it as a coenzyme  are vital for the plant. On the other hand, NCS1/PLUTO appears to be dispensable for plant growth, at least under our laboratory conditions, and a bona fide transporter of TDP into the chloroplast remains to be identified. Nonetheless, it is well established that biosynthesis de novo of TDP is predominantly restricted to green tissue42; thus the abundance of NCS1/PLUTO in root tissue may be important in this context, e.g. in nonphotosynthetic plastids. Indeed, when the plant is impaired in TDP biosynthesis de novo, loss of NCS1/PLUTO compromises plant health, retarding growth and development  that spatial and temporal control of metabolite production may be key to orchestrating specific developmental and physiological outcomestes Fig. . In part43. The purpose of circadian control of biosynthesis and transport may be intertwined in the different metabolic flux requirements during the light and dark. In this context, TDP-dependent mitochondrial enzymes easily lose the coenzyme upon isolation, which suggests a weaker binding of TDP by these enzymes and thereby possible benefits of transport44. This could be important for an effective regulation of enzyme activity or for a more sensitive detection of TDP biosynthetic needs. Currently, it is not known how the organellar transporters are regulated and how the flux of TDP into the organelles is orchestrated. However, it is likely that the TPCs act as gatekeepers with respect to TDP delivery, and are involved in fine-tuning flux through enzymes dependent on it as a coenzyme. This question, while not addressed in this study, will be challenging in Arabidopsis given the observation here that the TPC transporters are essential for viability. It is interesting that only TPC1 is under control by the circadian clock and may indicate that it is the more important paralog with respect to timing of TDP delivery.Here we also show that both biosynthesis and transport of TDP are regulated at the transcriptional level by the circadian clock Fig. . While t45. Although there is established circadian regulation of transcripts for proteins involved in fluxes of various molecules46, there is little evidence of oscillations of metabolite movement at the intracellular level, mainly due to difficulties to measure metabolites in the cell compartments47. A key finding in this study is that the coenzyme TDP itself displays a diel rhythm and moreover, that it is not dependent on circadian clock function . The circadian clock anticipates these changes and its importance is well established, as growth is suboptimal if the diel period deviates from a 24-h cycle24 and secondly, that such plants are unable to achieve metabolic homeostasis when suddenly shifted to a different photoperiod length48.The TDP rhythm observed here is a function of alternative splicing of a 3′-UTR RNA aptamer and thus, we assume predominantly relates to free TDP levels in the nucleus. It is intriguing why an essential coenzyme displays a diel rhythm in the nucleus, a location where presumably there is no apoenzyme that requires it. Why not simply control expression of the corresponding genes through promoter elements? The diel peak of nuclear TDP levels may represent a “relief valve” that serves to switch off functional THIC production directly (via the riboswitch), independent of anticipation by the clock. This thereby ensures a failsafe procedure such that TDP supply is strictly coordinated with demand in the event of circadian system malfunction. That this rhythm is important is supported by the recent observations that firstly disabling riboswitch function has a severe negative impact on plant performance48. Future work will establish the boundaries to this system and how far it can be pushed by altering the sensing of TDP and also by investigating the necessity of this metabolic rhythm and if this is exclusive to the light-dark cycle coincident with metabolic reprogramming in plants and will elaborate on metabolic crosstalk across organelles.In summary, TDP is biosynthesized in the cytosol and is actively transported into subcellular organelles to be used by enzymes dependent on it as a coenzyme. Changing demands during the light and dark periods of a diel cycle are anticipated by the circadian clock and may necessitate subtle alterations in TDP levels that are fine-tuned in the nucleus at least. Excessive TDP is sensed by the riboswitch. Use of this sole natural metabolite reporter in plants has permitted us to demonstrate that diel conditions are intimately associated with changes in TDP levels. This system can run independently of the circadian clock and serves to maintain TDP homeostasis in line with the needs of the cell and optimize central metabolismArabidopsis lines were either grown on soil or sterile culture under 12- or 16-h photoperiods (100−150 μmol of photons m−2 s−1 at 22 °C) as specified, and the corresponding dark period (12 or 8 h at 18 °C) to complete a diel cycle. Seeds used for in vitro culture were surface-sterilized, stratified for 3 days in the dark followed by growth on half-strength Murashige and Skoog medium49 in 0.55% (w/v) agar (Duchefa) plates (1/2 MS agar plates). Seedlings for global metabolite analyses were grown in sterile culture for 11 days under a 16-h photoperiod (100–150 μmol of photons m−2 s−1 at 22 °C) and 8-h dark period (at 18 °C) and in the presence or absence of thiamine supplementation as indicated. For diurnal and circadian experiments, seedlings were grown for 13 days in equinoctial conditions (12-h photoperiod with 120 μmol photons m−2 s−1 and 12 h of darkness at 20 °C) and either kept in these conditions  or transferred to constant light (circadian experiments). Shoot material was harvested from seedlings (n = 10) every 4 h at the times indicated. For the tissue expression analysis, roots, stems, flowers, siliques, rosette leaves and cauline leaves were collected (n = 10) from plants grown on soil under a 16-h photoperiod (100–150 μmol of photons m−2 s−1 at 22 °C) and 8-h dark period (at 18 °C). Cotyledons were collected at 2 weeks from seedlings grown under the same conditions. In all cases, samples of plant material were immediately frozen in liquid nitrogen and maintained at −80 °C until analysis. The ncs1-1 , tpc1-1 , tpc1-2 , tpc2-1  and tpc2-2  lines were obtained from the European Arabidopsis Stock Centre. In all cases, the T-DNA insertion lines could be isolated to homozygosity based on resistance to either BASTA™ (25 µg ml−1) or sulfadiazine (7.5 µg ml−1) as appropriate, as well as PCR genotyping. Reciprocal crosses of tpc1-1 and tpc2-1 were performed in an attempt to isolate double-mutant tpc1 tpc2 lines. The thiC line was from an in-house stock described in ref. 7, the triple-mutant prr5 ppr7 prr9 line was a gift from Kazuki Sato  and in all cases the Columbia ecotype was used as wild type. Seeds of the crossed lines thiC ncs1-1 were selected by PCR analysis as well as the distinctive chlorotic phenotype of thiC7.In general, 20 or oligo(dT)15 primers  according to the manufacturer’s recommendations. Quantification analyses were performed in 384-well plates using either a 7900HT or QuantStudio5 Fast real-time PCR instrument (Applied Biosystems) by fluorescence-based real-time PCR using Power(Up) SYBR Green master mix (Applied Biosystems) and the following amplification program: 10 min denaturation at 95 °C followed by 40 cycles of 95 °C for 15 s and 60 °C for 1 min. The data were analyzed using the comparative cycle threshold method (2−ΔCT) normalized to the reference gene PDF2 (At1g13320) or UBC21 (At5g25760), as indicated. Primers used are listed in Supplementary Table Total RNA was extracted using RNA NucleoSpin Plant kit (Machery-Nagel) following the protocol provided by the company and treated with RNase-free DNase to remove traces of DNA. A total of 1 μg of RNA was reverse transcribed into cDNA using Superscript III reverse transcriptase (Life Technologies) and either oligo(dT)NCS1 from Arabidopsis from cDNA of 10-day-old seedlings and cloned into the pENTR-D/TOPO vector using the pENTR TOPO cloning kit (Life Technologies) according to the manufacturer’s instructions and subsequently cloned into Gateway® destination vector pB7YWG250 by an LR reaction using LR clonase enzyme mix II (Life Technologies) to be expressed as a fusion protein with YFP at the C terminus (NCS1-YFP). Primers used (NCS1-YFP) are listed in Supplementary Table Arabidopsis protoplasts with the empty vector as control was carried out as described in ref. 51. For the generation of transgenic lines the NCS1-YFP construct and the empty vector as a control were introduced into Agrobacterium tumefaciens strain C58 and used to transform wild-type (Col-0) Arabidopsis plants by the floral dip method52. Transformants were selected by resistance to BASTATM, allowed to self-fertilize and homozygous lines were selected from the T3 generation according to their segregation ratio for BASTATM resistance. Eight-day-old transgenic seedlings were mounted in water in a flow chamber and subjected to fluorescence analysis  either by stereomicroscopy or on an SP2 confocal laser-scanning microscope (Leica), as described previously53. The CaMV 35S:LUC-THIC3′UTR was constructed from plasmids Topo-TA-THIC3’UTR and pBinAR-FLUC22 kindly provided by Andreas Wachter  by digestion with Sal1, which releases the THIC3′/UTR from Topo-TA-THIC3′UTR and facilitates its cloning into pBinAR-FLUC, thereby generating CaMV 35S:LUC-THIC3′UTR. Transgenic Arabidopsis carrying this reporter construct was generated by transforming wild type (Col-0) using the floral dip method52. Transgenic lines were selected by measuring the luciferase activity of 10-day-old seedlings grown on 1/2 MS agar plates and supplied with 5 mM d-luciferin 24 h prior to imaging using a CDD ORCA2 C4742-98 digital camera (Hamamatsu). Bioluminescent plants were allowed to self-fertilize and homozygous lines were obtained from the T3 generation and responsiveness to thiamine was validated by spraying with 1.5 μM thiamine 24 h prior to imaging as above.The NCS1-YFP construct was generated by amplifying the full-length sequence of NCS1 was amplified by PCR from genomic DNA and inserted into pENTR/D-TOPO (Life Technologies) followed by recombination into the destination vector pMDC162 54. The corresponding promNCS1-GUS construct was used for Agrobacterium tumefaciens transformation of Arabidopsis (Col-0) by the floral dip method52. Transformants were selected on hygromycin (50 mg l−1) followed by segregation analysis to isolate lines with a single insertion and was confirmed by PCR analysis. Between 10 and 25 lines were analyzed. Seedlings or tissues were collected in glass vials filled with ice-cold 90% acetone and incubated for 20 min at room temperature. For histochemical localization of GUS activity, the plant material was washed three times with staining buffer 6], 0.5 mM K4[Fe(CN)6], 0.1% Triton X-100, 10 mM EDTA-Na2) and afterwards infiltrated for 25–90 min (depending on the sample) with 5-bromo-4-chloro-3-indolyl-β-glucuronic acid in staining buffer (0.1 mg ml−1). Excess staining solution and chlorophyll was removed from the samples by rinsing several times with 70% ethanol. Images were captured using a Leica MZ16 stereomicroscope equipped with an Infinity 2 digital camera (Leica Microsystems).A 519 bp region upstream of the start codon of Saccharomyces cerevisiae knockout mutant CVY4 (MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0 thi7Δ::kanMX4 thi71Δ::LEU2 thi72Δ::LYS2 thi4Δ::his5+) deficient in the biosynthesis and transport of vitamin B127 and that only grows in the presence of high concentrations of thiamine (≥120 µm) was used. S. cerevisiae BY4742 (MATα his3Δ1 leu2Δ0 lys2Δ0 ura3Δ0) was used as the wild-type strain. Synthetic dextrose medium lacking vitamin B1 and uracil (Formedium) and containing 2% (w/v) glucose was used as the basic growth medium and supplemented with the indicated concentrations of thiamine. The cDNA corresponding to ArabidopsisNCS1 was amplified using the primer pairs listed in Supplementary Table pGREG506-TEF according to ref. 55 to generate pGREG506-TEF-NCS1 (AtNCS1 FL). The AtNCS1 Δ1–97 construct was prepared in a similar manner using the primers listed in Supplementary Table pGREG506-TEF-PUT3  or the thiC mutant (SAIL_793_H107) grown in the absence or presence of thiamine supplementation (0–1.5 μM) were used. Seeds were surface-sterilized with ethanol and sown on culture plates containing Murashige and Skoog (MS) medium49 and 0.55% agar (Duchefa) for growth under long day conditions  and ambient CO2. For metabolite extraction, fresh plant material (10–50 mg) was flash frozen and homogenized in 75% ethanol containing pre-warmed (80 °C) 10 mM ammonium carbonate, pH 7.5 and shaken for 4 min at 80 °C followed by centrifugation. The supernatants were analyzed by nontargeted metabolomics using flow injection analysis—time-of-flight mass spectrometry on an Agilent 6520 instrument56. After data curation and filtering, 372 ions with distinct mass-to-charge could be associated to one or more deprotonated metabolites of Arabidopsislisted by the Kyoto Encyclopedia of Genes and Genomes (https://www.genome.jp/kegg/). For all detected metabolites, we performed a differential analysis comparing thiamine supplemented samples with “0” controls. Out of all 372 ions, 24 were found to be significantly changed in at least one condition (|log2(fold-change)| > 0.5 and adj. p value < 0.01).Whole seedlings of 11-day-old 1 vitamer content was essentially carried out as described by ref. 29. Unless indicated otherwise, two-week-old seedlings (50 mg) were homogenized in 100 µl 1% TCA and incubated at room temperature for 30 min with constant shaking. Proteins were precipitated by centrifugation and the supernatant was neutralized with 1 M Tris-HCl, pH 9.0, containing 50 mM MgCl2 (10% v/v). Thiamine and its phosphorylated forms were then chemically converted to their corresponding thiochromes by alkaline oxidation with potassium ferricyanide (K3Fe(CN)6) according to ref. 57 with minor modifications: Seven microliters of a freshly made solution of 46 mM K3Fe(CN)6 (in 23% NaOH (w/v)) was added to 50 µl of extracts and then incubated in complete darkness for 10 min. Twelve microliters of 1 M NaOH and 25 µl of methanol were subsequently added. The extracts were clarified by centrifugation prior to injection into the HPLC. Quantification was carried out using the linear range of a standard curve constructed with known amounts of B1 vitamers using an Agilent 1200 series HPLC equipped with a binary pump, an autosampler and a fluorometer . Separation was achieved at 35 °C using a COSMOSIL π-NAP column . Mobile phase A was 50 mM potassium phosphate buffer, pH 7.2 and mobile phase B was methanol. Gradient steps were programmed as follows: 11% B ramped to 32% in 9.5 min, 32% B ramped to 60% in 3.5 min, 60% B ramped to 100% in 0.5 min and held at 100% B for 2 min, returned to initial conditions during 0.5 min, and equilibrated for 6 min. Analytes were eluted at a flow rate of 0.5 ml/min. Injection volume was 10 µl.Determination of Bhttps://biodare2.ed.ac.uk/)33 to identify time series that showed a circadian rhythm. Data were linearly detrended and a data analysis window of at least three consecutive cycles excluding the first 24 h of continuous light was used for period estimates. Period and phase estimates and cosine curve fitting were based on values obtained from the FFT-NLLS algorithm. Statistical analyses, t tests, one-way ANOVA and two-way ANOVA, were performed in GraphPad Prism v7.Time-series data were obtained from three biological replicates and each measured by qPCR in technical triplication. The mean values of each time series were analyzed using three different period analysis algorithms FFT-NLLS, MFourfit and MESA from Biodare2 (Supplementary InformationSupplementary Data 1Peer Review FileDescription of Additional Supplementary Files