Police‐initiated pedestrian stops have been one of the most widely used crime prevention tactics in modern policing. Proponents have long considered police stops to be an indispensable component of crime prevention efforts, with many holding them responsible for the significant reductions in violent crime observed across major US cities in recent decades. Critics, however, have taken issue with the overuse of pedestrian stops, linking them to worsening mental and physical health, attitudes toward the police, and elevated delinquent behavior for individuals directly subject to them. To date, there has been no systematic review or meta‐analysis on the effects of these interventions on crime and individual‐level outcomes.To synthesize the existing evaluation research regarding the impact of police‐initiated pedestrian stops on crime and disorder, mental and physical health, individual attitudes toward the police, self‐reported crime/delinquency, violence in police‐citizen encounters, and police misbehavior.We used the Global Policing Database, a repository of all experimental and quasi‐experimental evaluations of policing interventions conducted since 1950, to search for published and unpublished evaluations of pedestrian stop interventions through December of 2019. This overarching search was supplemented by additional searches of academic databases, gray literature sources, and correspondence with subject‐matter experts to capture eligible studies through December 2021.Eligibility was limited to studies that included a treatment group of people or places experiencing pedestrian stops and a control group of people or places not experiencing pedestrian stops (or experiencing a lower dosage of pedestrian stops). Studies were required to use an experimental or quasi‐experimental design and evaluate the intervention using an outcome of area‐level crime and disorder, mental or physical health, individual or community‐level attitudes toward the police, or self‐reported crime/delinquency.g effect sizes, depending on the unit of analysis and outcome measure. We also conducted sensitivity analyses for several outcome measures using robust variance estimation, with standard errors clustered by each unique study/sample. Risk of bias was assessed using items adapted from the Cochrane randomized and non‐randomized risk of bias tools.We adopted standard methodological procedures expected by the Campbell Collaboration. Eligible studies were grouped by conceptually similar outcomes and then analyzed separately using random effects models with restricted maximum likelihood estimation. Treatment effects were represented using relative incident rate ratios, odds ratios, and Hedges' p < 0.001) reduction in crime for treatment areas relative to control areas. These interventions also led to a diffusion of crime control benefits, with a statistically significant 7%  reduction in crime for treatment displacement areas relative to control areas. However, pedestrian stops were also associated with a broad range of negative individual‐level effects. Individuals experiencing police stops were associated with a statistically significant 46%  increase in the odds of a mental health issue and a 36%  increase in the odds of a physical health issue, relative to control. Individuals experiencing police stops also reported significantly more negative attitudes toward the police  and significantly higher levels of self‐reported crime/delinquency , equating to changes of 18.6% and 15%, respectively. No eligible studies were identified measuring violence in police‐citizen encounters or officer misbehavior. While eligible studies were often considered to be at moderate to high risk of bias toward control groups, no significant differences based on methodological rigor were observed. Moderator analyses also indicated that the negative individual‐level effects of pedestrian stops may be more pronounced for youth, and that significant differences in effect sizes may exist between US and European studies. However, these moderator analyses were limited by a small number of studies in each comparison, and we were unable to compare the effects of police stops across racial groupings.Our systematic search strategies identified 40 eligible studies corresponding to 58 effect sizes across six outcome groupings, representing 90,904 people and 20,876 places. Police‐initiated pedestrian stop interventions were associated with a statistically significant 13% (95% confidence interval [CI]: −16%, −9%, While our findings point to favorable effects of pedestrian stop interventions on place‐based crime and displacement outcomes, evidence of negative individual‐level effects makes it difficult to recommend the use of these tactics over alternative policing interventions. Recent systematic reviews of hot spots policing and problem‐oriented policing approaches indicate a more robust evidence‐base and generally larger crime reduction effects than those presented here, often without the associated backfire effects on individual health, attitudes, and behavior. Future research should examine whether police agencies can mitigate the negative effects of pedestrian stops through a focus on officer behavior during these encounters. However, people subject to stops are associated with significantly less desirable mental and physical health outcomes, attitudes toward police, and self‐reported crime/delinquency. For some outcome measures, the negative effects of pedestrian stops are considerably more pronounced for youth, though the data did not permit a comparison of individual effects by race.1.2Police stops have become one of the most controversial yet widely‐used crime prevention strategies in modern policing. This intervention involves the police‐initiated stop of an individual  on the street, for the purpose of investigation and/or questioning. Police stops have been commonly used as a tactic to combat violent and gun‐related crime.What is the aim of this review?This Campbell systematic review examines the effects of police‐initiated pedestrian stops on both place‐based and person‐based outcomes. It synthesizes results from 40 studies across six outcome groupings. Studies were predominately conducted in the USA.The current review assesses the effect of police stops (used interchangeably here with “pedestrian stops”) on both place‐based and person‐based outcomes, including crime, spatial displacement, mental health, physical health, attitudes toward the police, and self‐reported crime/delinquency.1.3Forty studies published between 1970‐2021 are included in this review. Eligibility was limited to experimental and quasi‐experimental studies with a treatment group of people or places that experienced police stops and a control group of people or places that did not experience police stops (or experienced a lower dosage of stops).Studies focusing only on police‐initiated traffic stops were excluded from this review. Only one eligible study was a randomized controlled trial, 33 studies were conducted in the USA, and seven were conducted in Europe.1.4Police stop interventions lead to significant reductions in area‐level crime with evidence of a diffusion of crime control benefits to nearby areas. However, methodological difficulties limit the strength of the causal inferences derived from these studies; further research is needed.Individuals stopped by police are associated with significantly higher odds of both mental and physical health issues, significantly more negative attitudes toward the police, and elevated levels of self‐reported crime/delinquency. The impact of a direct stop experience on mental health issues is also considerably larger for youth, compared to adults.Despite this finding, place‐based studies incorporating community surveys suggest that stop interventions do not impact community‐level attitudes toward the police, and thus the negative effects of these interventions may be limited to the individuals directly experiencing them.The findings of this review should be interpreted with caution, however, as only one randomized experiment assessing crime prevention outcomes was identified, and person‐based studies were often unable to establish temporal ordering between the treatment and outcome measures.1.5Policing efforts focused on high‐volume pedestrian stops are likely to reduce crime but may do so at the cost of negative health outcomes, negative attitudes toward the police, and higher levels of delinquency for individuals subject to the intervention. Given the net‐widening effects of pedestrian stops , these interventions may produce more harm than good. Police agencies should carefully weigh the potential benefits and harms associated with these interventions.Furthermore, recent reviews on tactics such as hot spots policing and problem‐oriented policing have demonstrated larger reductions in crime without similar backfire effects. The evidence‐base for these tactics is also of considerably higher methodological rigor, generating stronger conclusions regarding program effectiveness. While it is possible that police agencies can mitigate the negative effects of pedestrian stops through a focus on improving officer conduct during police‐citizen encounters, this review is unable to provide evidence of this effect.1.6The authors of this review employed search strategies intended to capture studies through December 2021.22.1Terry stops, street pops, stop and search, street stops, etc.) is often defined as the process by which “officers stop, and potentially question and search, people in the communities they are patrolling” ” . Thus, it is important to note that while pedestrian stops are often reactive in nature, in that they require prior indication of suspicious behavior or criminal activity, they may also be used proactively. In this regard, it is important to distinguish between pedestrian stops at the individual level and pedestrian stops as employed in proactive policing interventions. Proactive policing involves “policing strategies that have as one of their goals the prevention or reduction of crime and disorder and that are not reactive in terms of focusing primarily on uncovering ongoing crime or on investigating or responding to crimes once they have occurred”  on the street for the purpose of investigation and/or questioning Matched control group designs with or without pre‐intervention baseline measures Unmatched control group designs with pre‐intervention measures Unmatched control group designs with pre‐post intervention measures which allow for difference‐in‐difference analysesUnmatched control group designs without pre‐intervention measures where the control group has face validityRaw unadjusted correlational designs where the variation in the level of the intervention is compared to the variation in the level of the outcomeBoth randomized and quasi‐experimental research designs were considered eligible for inclusion Citizens Places .Given our interest in examining the impacts of pedestrian stops on crime, the community, and the individuals subject to these stops, this review includes the following populations:4.1.3Studies that evaluated interventions in which police‐initiated pedestrian stops of individuals or groups of individuals  were carried out as a major component of a policing intervention were considered eligible for this review. As previously noted, the term “intervention” included natural variation in general policing approaches throughout a jurisdiction and/or natural variation in exposure to police stops among samples of individuals. That is, any comparison of people or places with differential exposure to pedestrian stops was considered an intervention for the purposes of this review. It is important to note here that our focus was on pedestrian stops, and as such, we excluded studies that were solely or primarily focused on traffic stops. More specifically, our interest was in isolating interventions consistent with the concept of SQF, which is traditionally associated with pedestrian stops , but rather focused on the intent of the program in which pedestrian stops were a component. This review was not limited to interventions targeting specific types of crime or disorder , or any specific type of overarching policing tactic . However, we did exclude studies employing pedestrian stops in a minor capacity relative to other policing tactics (as the effects of the stop component would be difficult to isolate from the other components of the policing intervention).4.1.4Crime and disorder (including displacement)Incidents of violence in police‐citizen encountersOfficer misbehaviorFear of crimeAttitudes toward or perceptions of the police Mental health issuesPhysical health issuesThis review included the following outcome measures. All outcomes were considered primary, and eligible studies were required to report at least one of these measures for inclusion:Crime/disorder and displacement outcomes were considered eligible if measured using official data , unofficial crime data , and systematic social observations of crime. All types of crime and/or disorder were included in this review .We anticipated that incidents of violence in police‐citizen encounters would be measured through police use‐of‐force reports , and we included data measured at both the individual‐ and community‐levels of analysis. For the purposes of this review, mental health issues were defined as symptoms or diagnoses related to an established mental health condition or a “clinically significant behavioral or psychological syndrome or pattern that occurs in an individual” . There are no restrictions on the type of policing technique, the type of outcome measure, or the language of the research  and Queensland University of Technology (Angela Higginson). The GPD is a web‐based and searchable database designed to capture all published and unpublished experimental and quasi‐experimental evaluations of policing interventions conducted since 1950 National Criminal Justice Reference Service Abstracts (EBSCO)SocINDEX (EBSCO)Criminal Justice Database (ProQuest)Sociology Database (ProQuest)Sociological Abstracts (ProQuest)PAIS Index (ProQuest)Policy File Index (ProQuest)ProQuest Dissertations and ThesesTo extend the timeframe of the GPD search, we conducted an additional search for studies published between January 2020 and December 2021. This search included the same parameters and keywords as those used in the GPD search and utilized the following databases  reviewed the same subset of 25 titles/abstracts to establish inter‐rater reliability. Afterwards, the remaining results were double screened by both authors. All abstracts were reviewed using 4.3.2a.Reference information b.Nature and description of site selection, group, targeted outcome etc.c.Nature and description of selection of comparison group or periodd.The unit of analysise.The sample sizef.Methodological type g.A description of the pedestrian stop interventionh.Dosage intensity and typei.Implementation difficultiesj.The statistical test(s) usedk.Reports of statistical significance (if any)l.Effect size/power (if any)m.The conclusions drawn by the authorsEligible studies were double coded by authors KP and SF using the coding sheet in Appendix D. Our coding protocol captured various items related to:EpiData Software (https://www.epidata.dk/index.htm) was used to digitize coding forms and facilitate data entry.The research team met frequently to discuss coding items and any discrepancies in coding were discussed among all review authors before coming to a final coding decision. 4.3.3Six items adapted from the Cochrane randomized and non‐randomized risk of bias tools  was coded “probably yes” or “yes” if there was evidence of important baseline differences between groups that were not controlled for statistically. Otherwise, this item was coded as “probably no.” The appropriateness of the statistical analysis (item C) was coded as “probably yes” for quasi‐experimental studies using multiple regression or ANCOVA models, and “yes” for quasi‐experimental studies using strong statistical matching procedures . Quasi‐experimental studies that did not control for confounding factors were rated as “probably no” or “no” for this measure. For experimental studies, the appropriateness of the analysis was coded as “yes” so long as a statistical significance test was used that did not appear to violate any necessary distributional assumptions . Implementation failures and data missingness (items D and E) were coded as “no” if there was high program fidelity and no evidence of missing data. Similarly, this measure was coded as “probably no” if there was no evidence that implementation issues or data missingness favored one group over the other. Finally, the ability of researchers to establish temporal ordering (item F) was coded as “no” or “probably no” for cross‐sectional studies , and “probably yes” for longitudinal studies. Only longitudinal studies that could definitively separate the intervention and the outcome in time were coded as “yes” on this measure.At the study‐level, place‐based quasi‐experiments reporting evidence of uncontrolled baseline differences between groups were rated as “high risk” of bias. Quasi‐experimental studies that reported either no evidence of baseline differences between groups or that statistically controlled for baseline differences were rated as “some concerns”. Only place‐based studies using random assignment were rated as “low risk” of bias, so long as the authors did not report evidence of significant issues with the assignment process, analysis, or program implementation.For person‐based studies, any study coded as “No” or “Probably no” on our temporal ordering measure was rated as “high risk” of bias . Studies coded as either “Yes” or “Probably yes” on our temporal ordering measure were rated as “some concerns,” so long as these studies used analytic methods that controlled for possible confounding variables . Only longitudinal studies using strong statistical matching techniques  with clear separation of treatment and outcome measures across time were rated as “low risk” of bias for person‐based studies.4.3.4The protocol for this review outlines the anticipated approach for effect size calculations based on the expected nature of the outcome measurements . These studies predominately reported count data for treatment and control groups during pre‐ and post‐intervention periods . Given that Cohen's , Wilson  suggestsXˉk is the average count for treatment and control areas across both pre‐ and post‐intervention time periods, Sk is the standard deviation for each average count, and nk is the number of counts (contributing to the mean) for both treatment and control groups across pre‐ and post‐intervention periods. If the Φ value is greater than one, then the variance is multiplied by the Φ value to adjust for overdispersion. Unfortunately, the necessary data to correct for overdispersion was only available in a subset of our eligible place‐based studies. To adjust the variance for the remaining effect sizes, we simply used the mean value of Φ across the studies that presented sufficient data to calculate it.However, given that overdispersion is common in count data . We note here that risk ratios may have been preferable given their ease of interpretation .All effect sizes were calculated using functions manually built in R statistical software , which is a method capable of analyzing statistically dependent data structures in meta‐analysis , the I2 value represents the percentage of total variance attributable to variance between studies, and the τ2 value represents the magnitude of the random‐effects variance component  for each model was derived using restricted maximum likelihood estimation. These primary analyses were conducted in R statistical software using the metafor package Geographic size (micro place vs. neighborhood/police beat vs. district/precinct vs. entire city)Geographic location (US vs. Europe)Per the protocol for this review Sample demographics (youth sample vs. adult sample)Geographic location (US vs. Europe)For studies assessing mental health outcomes, these moderators included:Research design (“adjusted” vs. “unadjusted” estimates)Sample demographics (youth sample vs. adult sample)Geographic location (US vs. Europe)For studies assessing individual attitudes toward the police, these moderators included:We did not employ moderator analyses for physical health outcomes or self‐reported crime/delinquency, given the small number of studies included in these models.4.3.11In addition to the RVE models previously described .Our protocol also stated that risk of bias ratings would be determined using the Cochrane risk of bias tools , and two studies involved respondents from multiple European countries including the United Kingdom  measured treatment by asking youth whether they had ever been stopped by police “while on the street, at school, in a car, or some other place”  helps to reduce this concern, there remains a potential for reverse causality. That is, the presence of mental health issues or negative attitudes toward the police may lead to increased police stops, rather than vice versa. Only one study measuring attitudes toward the police incorporated both a pre‐ and post‐stop outcome measure , representing 90,904 people and 20,876 places. The summary effect sizes for each outcome can be seen in Table 5.3.1Figures p < 0.001) for treatment areas relative to control areas. CIs for this outcome suggest a crime reduction effect ranging from 9% to 16%. There is also a notable lack of heterogeneity in these effect sizes. All effect sizes tend to favor treatment with overlapping CIs, and between‐study heterogeneity was not statistically significant, as indicated by the Q statistic.As seen in Figure p < 0.001) decrease in crime for treatment displacement areas relative to control areas, with CIs ranging from a 4% decrease in crime to a 9% decrease in crime. There was also a lack of significant or excess heterogeneity in this model, as indicated by the Q and τ2 statistics. It is important to note, however, that these results seem to be driven by one study receiving a large amount of weight in the analysis  increase in the odds of a mental health issue, with CIs ranging from a 24% increase to a 72% increase. All effect sizes favored control groups, though there was significant heterogeneity in effect sizes estimates, as roughly 78% of the total variance could be attributed to between‐study variance.The eight effect sizes shown in Figure p < 0.001) increase in the odds of a physical health issue for treatment groups relative to control groups, and the CI for this outcome suggests that likely effects range from a 14‐62% increase. All four studies showed significant effects favoring control, though there remains statistically significant between‐study heterogeneity. Despite the strong and significant backfire effects indicated by these mental and physical health analyses, it is important to reiterate the inherent difficulties and potential biases involved in measuring these outcomes. Causal interpretations should be made cautiously.As seen in Figure 5.3.3g effect sizes were used for these outcomes given their often scaled or continuous nature. Thus, effect sizes to the left of the reference line indicate worsening attitudes toward the police and are defined as effects favorable to control. Several effect sizes were reverse coded to ensure that negative values corresponded to worsening attitudes for all studies . The classification of this effect size as small to moderate is based on the conventions suggested by Cohen . Using the binomial effect size display to convert this effect into a percentage point difference suggests an approximate 15% differential between control and treatment groups. All four effect sizes reported here favored control, though there remains a statistically significant amount of between‐study heterogeneity.Results from this analysis continue to suggest deleterious individual‐level effects of pedestrian stops. Specifically, there was a statistically significant increase in self‐reported crime/delinquency for treatment groups relative to control groups . Results for self‐reported crime/delinquency remained similar in magnitude (from g = 0.30 in our main specification to g = 0.26 in the RVE model), but these results were no longer significant at a 0.05 threshold. However, the degrees of freedom for this model were fewer than four, which is considered an unreliable sample size for RVE estimation . Moreover, this effect was similar in magnitude to that of the original specification . Finally, one study measuring attitudes toward the police , this difference was not statistically significant and both categories of studies remained significantly different from 0.No eligible studies for attitudes toward the police employed matching procedures. However, several studies provided only unadjusted bivariate data from which an effect size could be calculated , suggesting that police stops may have particularly salient effects on the mental health of youth. For attitudes toward the police, there was essentially no difference in mean effect sizes between youth and adult samples .For mental health outcomes, youth samples were associated with a 74% increase in the odds of a mental health issue for treatment groups relative to control groups, while adult samples were associated with only a 32% increase. This difference was nearly statistically significant at the 0.05 level . While this suggests that pedestrian stops in European settings may impact attitudes toward the police significantly more than in US contexts, it is important to note that the mean effect sizes for both locations suggested statistically significant negative effects. Additionally, all moderator analyses were limited by a small number of studies.Per the protocol for this review, we also examined the difference in mean effect sizes by geographic location. Given that several studies used samples from multiple countries, we chose to compare studies conducted in the US and Europe. Table 5.5While the primary objective of this review was to examine the impact of pedestrian stops on crime, the community, and the individuals subjected to stops, several relevant studies and outcomes could not be included in our meta‐analysis. Since the number of these studies was small, we opted to review their results narratively. Our findings overall are consistent with those of the studies meta‐analyzed. That is, pedestrian stops appear to negatively affect individual‐level attitudes toward the police and the legal system while simultaneously producing a general deterrent effect on crime and disorder. However, place‐based studies incorporating community surveys provide additional insight to suggest that the deleterious effects of pedestrian stops may be limited to those directly subject to the intervention, rather than the community more broadly.5.5.1Five studies published between 1970 and 2021 were identified as eligible for inclusion in this review but were not meta‐analyzed due to issues that prevented the calculation of an effect size or the lack of additional studies with conceptually similar outcome measures. Alderden et al.  evaluateTwo eligible studies using the same longitudinal survey sample included an outcome of legal cynicism , funnel plots with trim‐and‐fill analyses, and Egger's regression tests. Publication bias was not assessed for displacement, physical health, or self‐reported crime/delinquency, given the small number of studies for these outcomes.Qmodel = 2.77, p = 0.096), though published studies were associated with a 7% larger crime reduction effect, relative to unpublished studies . For mental health outcomes, published studies were associated with a 17% smaller increase in the odds of a mental health issue , but this difference was also not statistically significant . No moderator analysis based on publication status was conducted for attitudes toward the police as no eligible studies were unpublished.For crime and disorder, there was no significant difference in mean effect sizes for published and unpublished studies , mental health outcomes , and attitudes toward the police  all failed to detect significant funnel plot asymmetry as well.Figure In sum, there is limited evidence of publication bias in our results. Any potential bias appears to be minor and not substantively meaningful for our overall results.66.1g = −0.38). Specifically, individuals stopped by police were associated with significantly more negative attitudes than individuals not stopped by police . However, our narrative review of studies incorporating community surveys suggests that pedestrian stops do not negatively affect attitudes toward the police at the community‐level, indicating that these backfire effects may be limited to individuals directly subject to the intervention. Finally, pedestrian stops were associated with a statistically significant increase in self‐reported crime/delinquency (g = 0.30), with individuals experiencing direct police stops reporting a higher frequency of crime/delinquency, compared to those not experiencing direct police stops .The results of this systematic review and meta‐analysis point to both intended and unintended effects of pedestrian stop interventions. Analyzing 58 effect sizes across six discrete outcome groupings, we find that pedestrian stops lead to a reduction in crime at the geographic level but produce deleterious effects on the health, behavior, and attitudes of the individuals stopped by police. Our results can be summarized as follows: First, pedestrian stop interventions were associated with a statistically significant 13% decrease in crime for treatment areas relative to control. This effect was not accompanied by similar evidence of spatial displacement, and instead, we find a statistically significant diffusion of crime control benefits, with an average 7% decrease in crime for treatment displacement areas relative to control. Second, pedestrian stops were associated with a statistically significant 46% increase in the odds of a mental health issue for individuals stopped by police relative to those not stopped by police. These results extended to physical health issues as well, with treatment individuals demonstrating a 36% increase in the odds of a physical health issue relative to control individuals. Third, there was a significant effect of pedestrian stops on individual attitudes toward the police (g = −0.23), while European studies were associated with only a 5% decrease in crime and a moderate but significant decrease in attitudes favorable to the police (g = −0.65). However, it is important to note that these moderator analyses are subject to the same concerns regarding confounding variables as other forms of nonexperimental research . The completeness of this body of research may be increased through the incorporation of additional survey samples in future studies. There were also several outcomes specified in our initial protocol that we were unable to analyze due to a lack of eligible studies. Namely, there appears to be a lack of empirical knowledge concerning the impact of pedestrian stops on outcomes such as violence in police‐citizen encounters and officer misconduct. Finally, we did not include qualitative analyses in this review, and additional insight may be gained through synthesis of existing qualitative research.6.3The overall quality of the evidence included in this review is low by conventional standards  are generally measured during the same wave of data collection as personal experience with police stops. As such, it is often difficult to determine when health issues or negative attitudes toward the police developed and whether an individual's experience with pedestrian stops preceded the development of these conditions. Given that negative health conditions and attitudes toward the police may increase the likelihood that individuals come into contact with police in general  have reported crime reduction effects that are larger in magnitude than those reported here, without similar backfire effects on individual and community outcomes  would go a long way toward establishing temporal ordering and strengthening any causal inferences related to personal attitudes and health outcomes. Additional use of propensity score matching techniques, specifically for studies examining attitudes toward the police, is also needed to limit the potential for selection bias. This is exceedingly true considering the lack of random allocation used in these studies and the feasibility issues that are likely involved in the experimental analysis of pedestrian stops at the individual‐level. Furthermore, there is an apparent lack of high‐quality research examining the effect of pedestrian stop interventions on violence and misbehavior in police‐citizen interactions. If high‐volume pedestrian stops lead to additional use‐of‐force incidents or citizen complaints, then the negative impacts of these interventions may be even broader than those presented in this review. In this regard, future efforts may benefit from including a synthesis of qualitative research that explores individuals' experiences and perceptions of police stops. Along with this, existing research has largely been limited to contexts within the United States and the United Kingdom. Given evidence that similar strategies are being used in other parts of the world . Weisburd would be comfortable with outcomes that run counter to these prior findings. He is a member of the Campbell Collaboration Crime and Justice Coordinating Group. To manage potential conflicts of interest, Weisburd will not be involved in the editorial or formal approval process for this protocol or the subsequent review.Eggins has not been involved in any evaluations of police‐initiated pedestrian police‐stops. Eggins is an Editor for the Campbell Crime and Justice Coordinating Group (former Managing and Associate Editor) and so will not be involved in the editorial or formal approval process for this protocol or the subsequent review.Mazerolle has conducted a number of evaluations of policing interventions, some of which have contained police stops as one component of the intervention. She is also a former Co‐chair of the Campbell Collaboration Crime and Justice Coordinating Group. To manage these potential conflicts of interest, Mazerolle will not be involved in the editorial or formal approval process for this protocol or the subsequent review, nor will she independently decide on study eligibility, code studies, or conduct statistical or risk of bias analyses.Supporting information.Click here for additional data file.


Health literacy interventions and research outcomes are not routinely or systematically implemented within healthcare systems. Co‐creation with stakeholders is a potential vehicle through which to accelerate and scale up the implementation of innovation from research.This narrative case study describes an example of the application of a co‐creation approach to improve health literacy in an Australian public health system that provides hospital and community health services to one million people from socioeconomically and culturally diverse backgrounds. We provide a detailed overview of the value co‐creation stages and strategies used to build a practical and sustainable working relationship between a University‐based academic research group and the local health district focussed on improving health literacy.Insights from our experience over a 5‐year period informed the development of a revised model of co‐creation. The model incorporates a practical focus on the structural enablers of co‐creation, including the development of a Community of Practice, co‐created strategic direction and shared management systems. The model also includes a spectrum of partnership modalities , acknowledging the evolving nature of research partnerships and reinforcing the flexibility and commitment required to achieve meaningful co‐creation in research. Four key facilitators of health literacy co‐creation are identified: (i) local champions, (ii) co‐generated resources, (iii) evolving capability and understanding and (iv) increasing trust and partnership synergy.Our case study and co‐creation model provide insights into mechanisms to create effective and collaborative ways of working in health literacy which may be transferable to other health fields in Australia and beyond.Our co‐creation approach brought together a community of practice of consumers, healthcare professionals and researchers as equal partners. In the domain of health literacy, a small number of studies report on the ‘co‐creation’ of solutions to improve the design and navigation of health services and written materials with patients and consumers.This paper presents a model developed over a 5‐year period to build a practical and sustainable working relationship between a University research group and a local health district working across clinical and community services and focussed on improving health literacy  is 1 of 15 local health districts in the New South Wales (NSW) health system. WSLHD has approximately 12,000 staff members and delivers services to almost one million residents in Sydney's west. It has the highest urban indigenous population in Australia, 47% of residents were overseas‐born, and one in two speak a language other than English at home.2.2https://sydneyhealthliteracylab.org.au/). This was seen as a mechanism by which many of the local priorities and national strategic imperatives could be met. Although it was recognized that health literacy cannot compensate for health inequities created by the unequal distribution of opportunity and resources in societies, we were motivated by the belief that it is possible to optimize the contribution health literacy makes in mediating the causes and effects of established social determinants of health.Given the increasing policy emphasis and growing national interest in health literacy and the diverse needs of western Sydney, the idea of developing a ‘Health Literacy Hub’ to provide a consolidated support service for staff emerged in mid‐2017. A senior health manager identified the need for a more systematic approach to addressing health literacy in the health district and reached out to established contacts at a local university. The initial team was attracted to a co‐creation approach which brought together a community of practice of healthcare professionals and consumers with an interest in improving health literacy in western Sydney to form the Health Literacy Hub alongside an established academic health literacy team  to determine the strategy and priorities for the Health Literacy Hub. Meetings involved a structured workshop format, drawing on elements of the Nominal Group Technique.Box 11(1)Build staff capacity: Provide practical assistance to clinicians to better understand the communication needs of their patients; and equip them with evidence‐based methods and tools to optimize the impact and effectiveness of communications with patients and consumers.(2)Create a health literate organization: Establishing systems and organizational structures that enable and reinforce effective patient and public communication, including health services navigation and physical wayfinding.(3)Provide public resources, tools, support and advice to assist patients, their carers and families to communicate and connect in a meaningful way to the broader health system; specifically supporting them to understand and utilize the information provided and make informed decisions.2.2.2Successful and sustainable partnerships require resources.Initial funding was provided for 4 years to support a ‘Director of Strategy and Operations’ position for the Hub and a Senior Academic Advisor. The Academic Advisor was a senior University academic embedded in the local health system, with previous experience working in both health and academic sectors. We recognized that partnerships need boundary‐spanning leaders who understand and appreciate partners' different perspectives, can bridge their diverse cultures and are comfortable sharing ideas, resources and power.The Director of Strategy and Operations' role was to work on transformational change and to support awareness, engagement and increased capacity of healthcare staff to improve health literacy. They too acted as a boundary‐spanner working with university colleagues in the development of a supporting programme of health literacy research. The Director had connections to people, organizations and groups—including target populations, political decision‐makers, government agencies, private sector funders and other partnerships in the community—as well as ‘convening power’ to bring people together for meetings and other activities.These two positions were bolstered by early success in attracting funding for a health literacy Postdoctoral Research Fellow who would act as an important day‐to‐day point of connection between the health services Hub and the university Lab. The Research Fellow played a strategic role in brokering academic evidence and knowledge related to health literacy and bringing it into the Hub and working directly with health district staff and consumers to enable health literacy research.2.2.3To bring health staff, consumers and researchers together, we sought to develop a Community of Practice. WengerTo build the Community of Practice, we strategically designed a network of engagement platforms. See Table 2.3Having in place jointly determined priorities, continuous learning opportunities and an active Community of Practice provided a platform to facilitate the implementation of evidence‐based health literacy practices in the local health district as well as to bring together a range of health staff, consumers and researchers to co‐create research projects together from the outset. Early research partnerships were often consultative, with healthcare staff seeking feedback and advice on decisions or analyses related to health literacy research which had already been conducted Local champions—The activities of the Health Literacy Hub have been strengthened by the identification of local champions who have been proactive in advocating for cultural change and facilitating partnership projects across the District—both directly as partners themselves and indirectly through outreach activities which they have mediated.(2)Co‐generated resources—Financial and in‐kind resources are the basic building blocks of co‐creative interaction and research. Ongoing research collaborations have been facilitated by co‐generated research funding—allowing dedicated human and material resources to drive specific research projects. Funding applications have necessarily involved both health staff and academics , and have strategically included both direct research costs and budget to build capacity for research within the district through participatory approaches .(3)Evolving capability and understanding—As researchers have become increasingly engaged with the Local Health District, we have seen bidirectional learning and knowledge gain for different stakeholders, including an evolving understanding of different values, needs, and ways of working in research and clinical practice. This has been facilitated through deliberate actions such as the co‐location of research and clinical staff and the prioritization of clinical staff secondments to the Hub.(4)Increasing trust and partnership synergy—There has been a sustained commitment to building relationships of trust between researchers and communities engaged with the Health Literacy Hub. Both within and across projects, we have seen increasing partnership synergy  as researchers and health professionals have worked together over time. The synergy of collaboration is manifested in the increasing number of co‐created research projects across the District, as well as a shift in the point of engagement; rather than working with university academics at the analysis stage, health staff and researchers have increasingly come together at the earliest stages to identify problems, generate solutions and consider practical, culturally appropriate methodological approaches.As research partnerships have evolved over time, we have identified key facilitating factors for co‐creation including identification of local champions, co‐generated resources, increasing trust and partnership synergy, and evolving capability and understanding. These are discussed in turn below and supplemented by Table Increasing trust and partnership synergy between researchers and health district staff has also served to strengthen collaborations with consumers and the broader community. The District's—and, in particular, our local champions'—strong ties to the community have strengthened the capacity of the Hub to access and involve community members in the co‐creation of health literacy research. In addition to Parenting Plus, another recent example of this was the rapid mobilization of staff and consumers in Western Sydney and two adjoining health districts to co‐design and conduct the largest Australian COVID‐19 survey of people who speak a language other than English at home.3Insights from our experience over a 5‐year period have led to a refined understanding of how co‐creation is facilitated in practice, as summarized in Figure (1)Our revised model incorporates a practical focus on the structural enablers of co‐creation. The centrality of the Community of Practice, for example, reflects the key role that the iterative expansion of the Community of Practice played in enabling us to reach a broad range of consumers and health providers and to grow the Hub's presence within the district. This was key to facilitating co‐creation across multiple settings and projects.(2)By including a spectrum of partnership modalities , our model acknowledges the evolving nature of research partnerships and seeks to realistically reinforce the flexibility and commitment required to achieve meaningful co‐creation in research. We employed a range of connected strategies and invested the necessary time and resources to develop capability, trust and understanding between researchers, frontline health workers and consumers. Importantly, this foundational work enabled research partnerships to evolve over time.(3)Finally, we have brought together literature related to co‐creationOur revised model for research co‐creation builds on previous work, but advances from this foundation by focussing on practical stages, strategies and structures to build research partnerships and work towards co‐creation in real‐world health services and health systems. Three key elements of differentiation from existing conceptual models of co‐creation3.1While the process of developing the Health Literacy Hub has informed the above model of co‐creation, we have also faced challenges and barriers in the establishment and maintenance of this collaborative partnership which warrant attention. Foremost, the experience of bringing together the Hub and the Lab has highlighted the flexibility and commitment required to achieve meaningful co‐creation in research. Processes for partnering in the Health Literacy Hub have necessarily been dynamic and evolving, and this has required significant investments of time over and above other models for conducting health research. Given the inherent time commitments, one of the greatest threats to the maintenance of Hub relationships has been the turnover in staff, senior managers and executives in the health district. For example, the Lead of Child and Family Health who co‐led the development and feasibility testing of the Parenting Plus programme retired, as has the Child and Family Health nurse seconded to co‐design programme content. There has also been a turnover of Chief Executives and several Executive Directors since the establishment of the Hub. To maintain the momentum and stability of the Health Literacy Hub despite such turnover, we continue to engage broadly with staff at all levels of the organization and externally to sustain relationships and continually build new ones.3.2To date, key outcomes and achievements relate to reach and the scope of collaborative activities. There are currently over 1300 members of the Community of Practice and 11 completed or ongoing research projects which have quite literally ranged across the lifespan from early childhood/parenting education, through chronic disease management, to end‐of‐life decision‐making. The Lab/Hub collaboration has generated $1.9 million in research project funding, >15 jointly authored research outputs and has been linked to organizational‐level improvements in health communication.4The Health Literacy Hub represents a rare form of collaboration between hospitals, healthcare services, communities and health literacy researchers, which has evolved to develop innovative, practical and scalable health literacy interventions. This research ‘laboratory’ has enabled us to develop relevant, contextualized research questions and undertake applied research with clear pathways to research translation and practical implementation to benefit communities with significant social disadvantages.Our revised model for research co‐creation complements and builds on previous research related to co‐creation, cross‐sectoral collaboration and translational research. Components of our revised model are supported by both theoretical and empirical literature which highlights the importance of building and maintaining relationships of trust and ‘partnership synergy’ in collaborative research,4.1We have developed a revised model of co‐creation based on our experience in establishing the Health Literacy Hub in western Sydney, Australia. Without testing in other settings, it is not yet clear whether this model is replicable or which components are entirely necessary for similar success. In addition, this model and manuscript may not capture the perceptions of all partners engaged with the Hub. Going forward, a more formalized evaluation including all partners will be valuable. This could, for example, replicate the evaluation of a UK public health collaborative, AVONet, which used a convergent parallel mixed‐methods design with quantitative surveys and qualitative semistructured interviews to capture the experiences of all partners involved in the collaboration in some way.5A co‐creation approach—with its necessary time and resource commitments—has not always been rewarded in research. However, as researchers are pressed to highlight ‘impact’ by a growing number of funding bodies, co‐creation becomes more attractive. For the past 5 years, we have worked to build a practical and sustainable working relationship between an academic research lab and a local health system and its community focussed on improving health literacy. The goal was to improve both service delivery effectiveness and research output by leveraging combined resources, with involvement and input from all partners throughout the entire research journey. Our conceptual model reinforces core learnings from this process. We engaged broadly through the strategic development of a community of practice, with extensive commitments to build capability and relationships of trust and to progress research partnerships from relationship‐building activities to co‐creation. Partnership with local ‘champions’ and co‐generated resources helped to maintain momentum. Our co‐creation model can provide useful insight into mechanisms that have created an effective, collaborative and practical approach to researching and solving locally based problems working alongside healthcare staff and consumers.Danielle M. Muscat developed the revised conceptual model of co‐creation and drafted the manuscript. Don Nutbeam was a major contributor in writing the manuscript and revising the conceptual model. All authors played a key role in the establishment of the Health Literacy Hub and read and approved the final manuscript.D. M. M., K. M. and J. A. are joint Directors of Health Literacy Solutions Pty Ltd.; a health literacy consultancy company. The remaining authors declare no conflict of interest.


As such, developmental mechanisms that regulate leaf venation patterning have a direct impact on physiological performance. Development of the leaf venation network requires the specification of procambial cells within the ground meristem of the primordium and subsequent proliferation and differentiation of the procambial lineage to form vascular strands. An understanding of how diverse venation patterns are manifest therefore requires mechanistic insight into how procambium is dynamically specified in a growing leaf. A role for auxin in this process was identified many years ago, but questions remain. In this review we first provide an overview of the diverse venation patterns that exist in land plants, providing an evolutionary perspective. We then focus on the developmental regulation of leaf venation patterns in angiosperms, comparing patterning in eudicots and monocots, and the role of auxin in each case. Although common themes emerge, we conclude that the developmental mechanisms elucidated in eudicots are unlikely to fully explain how parallel venation patterns in monocot leaves are elaborated.Organisation and patterning of the vascular network in land plants varies in different taxonomic, developmental and environmental contexts. In leaves, the degree of vascular strand connectivity influences both light and CO Although the leaf venation system in any plant is an integral part of the shoot–root vascular network, patterning and cell‐type differentiation in the embryo, root and stem vasculature is not discussed here because the topics have been extensively covered elsewhere , medio‐lateral (midrib to margins) and adaxial–abaxial (upper to lower) axes Fig.  are bein4 grass leaves , secondary  and tertiary (‘rank 1’ intermediates) veins forming in sequence. All of these vein ranks are classified as ‘major’ in that the ring of sheath cells encircling the vein is capped by sclerenchyma (bundle sheath extension cells) on the adaxial and/or abaxial side Esau,  Fig. 2d2d. Minoret al., et al., et al., et al., In addition to recognizable topologies in the proximo‐distal and medio‐lateral axes, leaf veins develop with adaxial–abaxial polarity. In the majority of angiosperms, xylem is positioned adaxially and phloem abaxially Fig. . Within The leaf venation patterns discussed previously have long intrigued both natural and physical scientists. Now, more than 50 years after Sachs first proposed that the phytohormone auxin induced the differentiation of vascular tissue after wounding Sachs, , there iIII.et al., de novo synthesis, storage, degradation, transport and signalling, each of which is discussed in later sections , can be synthesised de novo in plants in a tryptophan (Trp)‐dependent or Trp‐independent way. The existence of a Trp‐independent pathway was postulated because IAA was detected in Trp auxotroph mutants , auxin is partially protonated to form IAAH but in the cytosol (c. pH 7.0) unprotonated IAA‐ predominates. Neutrally charged IAAH can diffuse through the lipid bilayer from the apoplast to the cytosol whereas IAA− cannot cross from the cytoplasm to the apoplast. Passive movement therefore contributes to long‐range source‐to‐sink translocation and to short‐range cellular influx, but cellular efflux occurs almost exclusively by active transport. Interestingly, diffusion through plasmodesmata may have a ‘nonpassive’ component, with directional flux achieved by unequal deposition of callose in plasmodesmata of different cellular axes , PIN‐FORMED (PIN), PIN‐LIKES (PILS) and ATP‐binding‐cassette B (ABCB) family proteins  in their promoters Fig.  reviewe. AuxREs III..5in vivo. For example, signalling activity can be detected using fluorescent reporters that are fused to the DII degron sequence of Aux/IAA proteins  that contain several AuxRE repeats fused to the minimal CaMV35s promoter are used to drive β‐glucuronidase (GUS) or fluorescent reporter gene expression  coupled with fluorescence resonance energy transfer (FRET) allows the direct detection of auxin in vivo  is similar to the 19 in Arabidopsis, but considerably higher than the two in the moss Physcomitrella patens  evolved in the last common ancestor of lycophytes and euphyllophytes that could have facilitated deployment of the auxin pathway for the development of vascular structures. Genomic   in maize  veins is not dependent on the formation of the midvein, and that the median vein is inherently equivalent to a lateral vein.A clear distinction between midvein development in monocots and eudicots has been revealed by the identification of midribless mutants in monocot species   ‐RELATED HOMEOBOX (WOX) gene perturb leaf development and vein patterning in the eudicots Medicago truncatula and Nicotiana sylvestris  proteins , whereas veins in eudicots appear progressively with tertiary veins extending from secondary veins and so on. Given these different trajectories, relationships between growth, axis formation and venation patterning in monocots and eudicots are discussed separately below.et al., et al., et al., et al., et al., et al., Rolled1 (Rld1)  or phloem  within leaf veins. The coincidence of rolled leaf and aberrant xylem–phloem phenotypes in mutant plants suggests that adaxial–abaxial polarity within monocot leaf veins may be prepatterned by the juxtaposition of HD‐ZIPIII and KAN/miR165/166 activity within the initiating leaf primordium  gene expression is suppressed at P0 , midvein‐independent induction of auxin traces for lateral veins , and constraints on the area of the venation network imposed by cell division arrest, compared with those identified in eudicots.In the next section we assess how the patterning processes operating in monocots, that is PIN1 and the repression of KNOX gene expression in a small cohort of founder cells  define the position of provascular traces for both the midvein  in the underlying ground tissues; PEDs progress towards the midvein marking the proximal part of a future vascular loop , which encodes an HD‐ZIP III transcription factor that marks preprocambial cell identity and stabilises PIN1 localisation  transcription factor, which in turn elevates pin1,3;4;7 quadruple mutants display more perturbed venation patterns than pin1 single mutants, indicating that these other PINs may work alongside PIN1  disrupt PIN1 cycling as well as auxin signalling (Koizumi et al., et al., ATHB8 expression (Deyholos et al., et al., et al., Although the PIN1/MP/ATHB8 feedback loop is an important component of the venation patterning mechanism in Arabidopsis, other factors undoubtedly play a role. For example, et al., WOX gene family members in various eudicot species lead to leaf shape perturbations, notably a marked reduction in the leaf lamina, along with disrupted venation patterns (Vandenbussche et al., et al., WOX genes have been shown to mediate growth of the leaf lamina through localised induction of YUCCA auxin biosynthetic genes, with auxin promoting cell proliferation and inhibiting differentiation (Z. Zhang et al., et al., WOX gene expression profiles in the primordium influence leaf shape by regulating where and when cell proliferation occurs, and influence venation patterning by regulating where and when provascular traces will form. Consistently, continuous addition of new higher order veins and freely ending veinlets are observed as long as the leaf is growing (Kang & Dengler, et al., et al., et al., et al., et al., et al., et al., ATHB8 expression precedes the expression of core cell cycle genes. This observation highlights a probable positive feedback loop between the auxin regulatory network, cell divisions and vascular differentiation (Kang & Dengler, Most mutants with leaf venation defects also display changes to leaf shape, reflecting the relationship between vein patterning and the regulation of cell proliferation throughout the leaf (Petricka de novo after wounding or exogenous auxin application. By contrast, substantial evidence is available in support of the canalisation hypothesis that was first proposed by Tsvi Sachs (Sachs, et al., et al., et al., Computational analyses have attempted to reproduce the formation of eudicot leaf venation patterns, with the goal of understanding the intricate relationships between cell proliferation, auxin signalling and the self‐organisation of venation patterns. Two conceptual frameworks have traditionally been used to study the relationship between auxin and venation patterning: reaction‐diffusion and canalisation, with computational modelling and experimental evidence alternatively favouring one or the other. Reaction‐diffusion, based on Turing's original reaction‐diffusion theory, was initially applied by Meinhardt to the broader concept of biological patterning (Meinhardt, s Sachs, , 1981 ans Sachs, , 1981. CV.et al., de novo in the leaf and there is evidence of a prepatterning mechanism. To this end, advanced noninvasive techniques for live imaging of monocot leaf primordia need to be developed and the experimental data used to train computational models and simulations. Overall, there is enough evidence to suggest that knowledge gained in eudicots will only partially explain processes in monocots and therefore focussed investigations are needed to understand how developmental mechanisms regulate venation patterns, and therefore physiological performance, in monocot leaves.Understanding the mechanisms underlying leaf vein formation is of key importance given the impact of the vascular system on photosynthetic performance and leaf hydraulic capacity. Because of the potential to modify physiological performance, vein patterning mechanisms have gained much interest in the last few years as efforts are made to generate more climate‐resilient crops that have optimal photosynthetic rates and efficient distribution networks for both metabolites and water. Biophysical models have even attempted to determine which aspects of the leaf venation network optimise transport function and structural rigidity (Katifori CP and ST contributed equally to this work.


Single‐cell sequencing has scientific impacts on better understanding the immunity. There is a rapid development in single cell‐based databases and analytic tools to provide the potential of clinical and translational discovery. The understanding of single‐cell based immunity needs a strong program and solid evidence of preclinical and clinical validation and evaluation. The current special topic issue on single cell and immunity aimed to provide a strong communication for the progress of single cell‐based studies on immune cell functional diversity in development and disease. The topic has a clear scope on the application of single cell sequencing to better understand immune cell heterogeneities, functions, cell–cell interactions, responses and regulatory roles in systems immunology and diseases. The delicate balance of immunity is mediated by frequent crosstalk and complicated interactions between various cell types. Immune cells are highly heterogenous in cell types and activation status. Tissue and bulk cell sequencing methods capture the overall signatures from mixed samples, with the subtle gene signatures from rare yet functionally important cell types covered by abundant cell populations. Furthermore, intra‐ and inter‐cellular communications carrying essential clues for immune cell proliferation, differentiation, maturation, migration and transition are difficult to be characterised using traditional methods. The current special topic issue aimed to focus on the rapid progression of single cell‐based studies on infectious immunity, cancer immunology, innate immunity and immune system disorders, as shown in Figure Single‐cell sequencing has scientific impacts on clinical and translational discovery. In the past decade, single‐cell RNA sequencing (scRNA‐seq) has triggered a true revolution in immunological research. As single‐cell molecular profiling tools, scRNA‐seq technologies provide a better understanding of the heterogeneity associated with individual immune cells and the responses at the molecular levels under physiological and pathological conditions.There is a rapid progress in single cell analytic tools. A wide variety of comprehensive online databases were developed for the storage and analysis of single cell data sets.The authors declare no conflict of interest.


Much of the uncertainty that clouds our understanding of the world springs from the covert values and intentions held by other people. Thus, it is plausible that specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. To test this hypothesis, we scoured academic databases for neuroimaging studies involving learning under uncertainty, and performed a meta‐analysis of brain activation maps that compared learning in the face of social versus nonsocial uncertainty. Although most of the brain activations associated with learning error signals were shared between social and nonsocial conditions, we found some evidence for functional segregation of error signals of exclusively social origin during learning in limited regions of ventrolateral prefrontal cortex and insula. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas processing generic value representation and learning, but that a specialized circuitry might have evolved in prefrontal regions to deal with social context representation and strategic action. Much of the uncertainty that clouds our understanding of the world is caused by other people's actions and values. So there could be specialized mechanisms that compute learning signals under uncertainty of exclusively social origin operate in the brain. Our results suggest that most, but not all, behavioral adaptations to navigate social environments are reused from generic value representation and updating mechanisms. But in our increasingly interconnected society, there are fewer and fewer actions we can take which do not involve assessing how the presence of others will affect the outcome of our actions on the current state of affairs. Predicting the future requires guessing the present, but a substantial part of the relevant present is private to each of us . Thus, most decisions, such as saying what you think to a colleague or keeping it to yourself, or investing your savings in biotech stocks or real state, are fraught with uncertainty that is compounded by social factors. Understanding to what extent learning in social contexts is a specialized function is important not only because we could expedite adaptation to different social contexts, but also because this would shed light on the source of cognitive biases, many of which are grounded on social preferences. In this study, we inquire into whether there are learning mechanisms specialized in resolving uncertainty of exclusively social origin.Stochastic outcomes of know (risk) and unknown (uncertainty) probabilities can both be subjectively perceived as risk, and risk is a major modulating factor in learning  of a univariate probability distribution. This includes reward/subjective utility, monetary value, appetitive and/or aversive states  error signal, ɑ denotes learning rate, Rt is reward received by the agent as it moves into state S at time t, and V(st) represents value function over states st at time t. Q‐learning extends the TD learning algorithm from values associated with states V(st) to joint action‐value pairs  to which a value is assigned through a Q = Q function that is updated upon feedback. Action selection is accomplished through a policy function that typically assigns higher choice probability to action‐values with high Q, such as the max or softmax functions. Q‐learning is relevant because it subsumes state and action value learning, thus furnishing a unifying simple and locally implementable algorithm that treats action‐state values as the learned unit. For our purposes, this enables looking upon actions and states on equal footing, and modeling the brain decision‐making mechanism as a putative implementation of TD learning. A further generalization of adaptive learning theories comes forth by considering off‐policy counter‐factual signals  is perhaps the most prominent and researched. The keystone of the theoretical framework built around RL is arguably temporal difference (TD) learning, which is an algorithm that allows agents act based on the future state values their actions lead to, by bootstrapping itself using current estimates. State value updates are accomplished by considering the difference between the expected reward and the actual received reward, where expectations account for all possible future states , and striatum  and insula  or interactions; “Nonsocial” included the remaining studies, that satisfied the eligibility criteria. We gathered PE and expected or predicted value maps for both categories , where PE was defined as any learning signal; however, only PE maps were used in the analyses because expected value maps were not consistently reported and its number was insufficient.2.1Participants were healthy adult humans within the age range 18–65 years, in nonclinical studies.Whole‐brain results were reported. Studies that only reported region of interest (ROI) or small volume correction (SVC) analyses were excluded, unless their peak activation statistics exceeded the threshold used for whole‐brain analysis. This is akin to simulating a more conservative (whole‐brain) threshold being applied to subregions  of probability distributions, whether these referred to self or to other's attributes. This is deliberately encompassing to allow the inclusion of a large enough cohort of studies. The study should also have used these PE‐like variables as parametric modulators for the relevant task events—typically feedback—in the design matrix of a general linear model analysis of BOLD signal. Maps of variables anti‐correlated with PEs were excluded, that is, we used maps correlated with signed PE.Additionally, only for the “Social” category (26 studies found), the learning task should occur only through repeated observation of other person's actions, and for the “Nonsocial” category (30 studies), it should exclude such social proxies.We searched for studies through the websites PubMed and Web of Science, which aggregate multiple academic databases, on March 3, 2021. The keywords used to filter results were: . We selected all accessible articles that satisfied the inclusion criteria. In brief, the selection consisted of two stages: identification, or abstract‐based culling, and screening, where studies lain aside during identification are perused to checked all inclusion criteria  and 31 experiments with NPE maps . A comprehensive list is shown on Table 2.22.2.1t‐statistics and uses a mixture of Gaussian distributions discretized over voxels and centered around the reported foci locations to model the probability that specific voxels correspond to true activations. The variance of the Gaussian kernels was heuristically estimated empirically by Eickhoff et al.  correction of p <.05, which was achieved by comparing the observed cluster size to a Monte Carlo maximum cluster size distribution built from 5000 iterations of drawing random peak locations from the gray matter template. Cluster‐level FWER correction is the most appropriate method for statistical inference to ALE  by pooling the foci datasets and assigning them randomly to a group  were thresholded with a cluster‐forming level of 2.2.2CBMA methods—including ALE—test for spatial convergence of activation peaks. This roughly means that they detect regions where studies report more peaks or peaks are found more often than in other regions. But this contrasts with typical fMRI analyses, where massive univariate voxelwise tests detect voxels that activate, perhaps as differences between groups. ALE convergence of peaks test relies on spatial assumptions that data are not guaranteed to meet , and has lower statistical power when there are multiple effects, which may undermine the statistical significance of the results , small‐study effect , and excess significance (which would indicate that more studies than expected report significant results). We performed voxel‐wise effect size meta‐analyses separately for the social and nonsocial conditions . We also tested the (linear) effect of the dummy variable social versus nonsocial through a SDM‐PSI meta‐regression, which assessed the covariation of the predictor dummy variable {SPE = 1, NPE = 0} with voxel activations. The data were preprocessed with SDM‐PSI's (version 6.22) default gray matter correlation template . SDM‐PSI accomplishes FWER correction by running a subject‐base permutation test that yields a distribution of the maximum statistic that is used to threshold the meta‐analysis images. All models were estimated with 50 random imputations and statistical thresholding was performed through 1000 permutations using a voxel‐level FWE‐corrected threshold of p <.05.We employed SDM‐PSI to probe the robustness of ALE results and to capitalize on its built‐in algorithms to grade the strength of evidence via multiple robustness considerations, viz. study heterogeneity  and nonsocial prediction error signal (NPE) studies Table . For SPEThe SDM‐PSI meta‐analyses reported two surviving clusters for the SPE studies, and one for the NPE studies Figure , Table 3I2, was 17.62%, 1.66%, and 34.45% for the NPE group, SPE group, and the linear meta‐regression, respectively. The funnel plots were reasonably symmetric for all three analyses; the metabias tests, which assess small‐study effects through funnel plot asymmetry, did not support the existence of small‐study effects . Finally, there was no evidence for excess of significance .Study heterogeneity, as measured via the statistic 4The ALE meta‐analysis suggested that SPE‐related activations were a subset of NPE‐related activations in the striatum. However, the SDM‐PSI meta‐analysis revealed a more complex picture, where extensive bilateral medial frontal, anterior cingular cortices, and striatal areas were shared between SPE and NPE ; and bilateral insular regions were found only for SPE studies. The meta‐regression found a single area in right middle ventrolateral PFC (Brodmann area 45 or BA45), predictive of more activation for SPE than NPE.ALE did not find evidence in any brain region supporting a functional segregation of PEs with uncertainty originated in social versus nonsocial sources. The subtraction contrast SPE minus NPE was null, which suggests that social learning signals are a subset of general learning signals, and that value‐updating neural mechanisms are common to both social and nonsocial values. Although the nonsocial experiments selected covered a wider range of stimuli and task contexts  there were also no surviving clusters in the subtraction analysis NPE minus SPE Table . Howevert‐statistics, SDM‐PSI can implement random effects modeling, which increases reliability and accuracy  and insula. The right BA45 is a contralateral counterpart of Broca's area (left BA44 and BA45), which is active in semantic tasks. Neuroimaging studies have shown that the right vlPFC is a critical substrate of control  cognition is sensorimotor interaction of creatures with the environment . This is similar to the distinction between uniformity and association test maps on the Neurosynth database  of social versus nonsocial origin; however, we found some evidence supporting functional specialization in insula for social error signals and in vlPFC for being differentially more activated in social than in nonsocial contexts. This suggests that most behavioral adaptations to navigate social environments are reused from frontal and subcortical areas along the mesolimbic pathway processing generic value representation and learning, but that specialized structures might have evolved in the prefrontal cortex to deal with social context representation and strategic action. This has implications for social, developmental, and evolutionary neuroscience, because it suggests that the mesolimbic pathway could have been reused and deployed, with little modification, to serve the mounting computational needs of human ancestors' brains in an increasingly complex social environment.The authors declare no conflict of interest.


January displayed the highest incidence rate of AML, with a minimum average difference of 7% when compared to February  and a maximum average difference of 16% compared to November  and August . Such seasonal effect was consistent among subgroups according to sex and age. Our finding that AML diagnosis is seasonal strongly implies that seasonal factors, such as infectious agents or environmental triggers, influence the development and/or proliferation of disease, pointing to prevention opportunities.Until now, the role that seasonal factors play in the aetiology of acute myeloid leukaemia (AML) has been unclear. Demonstration of seasonality in AML diagnosis would provide supportive evidence of an underlying seasonal aetiology. To investigate the potential seasonal and long‐term trends in AML diagnosis in an overall population and in subgroups according to sex and age, we used population‐based data from a Spanish hospital discharge registry. We conducted a larger study than any to date of 26 472 cases of AML diagnosed in Spain between 2004 and 2015. Using multivariable Poisson generalized linear autoregressive moving average modelling, we found an upward long‐term trend, with monthly incidence rates of AML annually increasing by 0.4% [95% confidence interval (CI), 0.2%–0.6%;  Acute myeloid leukaemia (AML) is a rare disease, yet responsible for a large number of cancer‐related deaths.Mutations that cause AML can occur due to an inherited mutant gene or exposition to certain carcinogens, such as chemotherapy, radiotherapy, ionizing radiation, tobacco and benzene.Demonstration of seasonal variation in the occurrence of AML would, firstly, provide supportive evidence of aetiology by seasonal factors, such as infectious agents or environmental factors; and, secondly, focus research onto the etiological role of such factors. The value of this approach has been recognized previously, although several early studies explored and failed to draw conclusive evidence of the potential seasonality of AML.Due to a lack of consensus on the possible seasonal variation in AML incidence and the progress made within the last decade in data recording and in the development of statistical models for analysing seasonal incidence data,https://clinicaltrials.gov (Identifier: NCT03433521).The study was approved by the Ethics Committee for Clinical Research of Hospital Universitario Fundación Jiménez Díaz (EO 100/2017_FJD‐HRJC), in accordance with the Declaration of Helsinki. The study was registered on The census of the Spanish general population was obtained from the Spanish National Statistics Institute for the period 2004–2015.Conjunto Mínimo Básico de Datos (CMBD) is a national hospital discharge database that contains anonymized data for approximately 98.0% of public hospital admissions in Spain, and it covers 99.5% of the Spanish population.The N = 3471) or that only had the first decimal place of the ICD‐9 code recorded (N = 16), were excluded.Data were obtained from the CMBD on every recorded case of AML  in Spain during the period 1997–2015. We extracted information from the register of each case about the date of admission, discharge date, anonymous identifier for each patient, ICD‐9 codes, sex and date of birth from which we derived age groups as described for the at‐risk population. ICD‐9 codes included a second decimal place indicating whether the cancer was active (.00), in remission (.01), or was considered a recurrence (.02). For patients hospitalized on more than one occasion, only the record corresponding to their first diagnosis of AML was selected. To reduce the likelihood of including patients who had been diagnosed with AML before 1997, only patients admitted to hospital and diagnosed with AML in or after 2004 were included in the analysis. Patients whose first recorded diagnosis of AML was classified as remission or recurrence  method.trend (modelled as a linear trend), monthly seasonality  and December 2015 (dummy variable with two levels). The rate of AML in December 2015 was identified as an outlier from the STL analysis. To account for such an outlier in the data and remove its effect, the variable December 2015 was tested in each model. Multivariable models underwent manual backward step‐down selection to minimize the Akaike information criterion. The selection process of the appropriate lags for the autoregressive (AR) and moving average (MA) components is described in the Appendix p < 0.05.Nine separate Poisson generalized linear autoregressive moving average (GLARMA) models were fitted to evaluate the temporal dynamics in AML incidence using data for all cases, and data for each sex and age group. A detailed description of GLARMA models can be found elsewhere,A total of 26 475 individual patients with a first diagnosis of active AML were hospitalized in Spain and registered at the CMBD during 2004–2015. Information on the sex of the patient was missing for three of the cases, leaving a total of 26 472 patients in the study population. The census of the Spanish population and stratified by sex and age for years 2004–2015 can be found as Table Descriptive characteristics of sex and age of the patients are shown in Table The mean standardized yearly and monthly incidence rates of AML diagnoses over the study period were 47.62 cases per million person‐years (standard deviation: 1.01 cases per million person‐years) and 3.97 cases per million person‐months (standard deviation: 0.43 cases per million person‐months) respectively. A higher monthly incidence rate was observed in January in the overall population and in stratified analyses of sex and the age groups 5–19, 20–49, and 50–64 years , given that the other covariates are held constant. January displayed the highest incidence rate of AML, with a minimum average difference of 7% when compared to February  and a maximum average difference of 16% compared to November  and August . The incidence rate of AML for December 2015 was 0.43  times the average incidence rate for the rest of the study period. Further results from the final Poisson GLARMA models for each sex can be seen in Table A graphical representation of the fitted values from each of the nine final Poisson GLARMA models along with the actual number of cases over time is depicted in Figure Until now, the role that seasonal factors play in the aetiology of AML has been unclear.The observed age/sex‐standardized monthly incidence rates of AML in Spain over the study period were in line with the age‐standardized monthly incidence rates found in a comparable study in the United States.N = 26 472 AML cases) and that by Calip et al.N = 21 570) which analysed, respectively, the largest series of AML cases to date in Europe and the United States, provide strong evidence of a monthly variation in AML diagnosis and suggest a seasonal peak in winter months; especially in January, when the peak months found in both studies overlapped.This study showed a clear seasonal pattern in the diagnosis of AML, with the highest incidence rates of diagnosis observed in January. These findings are broadly similar to those in a large US‐based study by Calip et al. who found a peak of adult AML diagnoses during December–January from 1992 to 2008.In contrast, however, earlier studies using cancer registry data from the United States and Europe have failed to find definitive evidence of seasonality in AML diagnosis.In stratified analysis, we observed the presence of seasonal variation in the incidence of AML among females and males with a peak in January. This is broadly consistent with previous studies elsewhere.This study found strong evidence of seasonal variation in the diagnosis of AML, which suggests that seasonal risk factors, such as infectious agents or environmental factors, influence the development and/or proliferation of AML. Our results exhibited a significant annual peak in January. While the underlying reason for this result is unknown, it is possible that the observed peak in monthly AML risk coincides with seasonal elevations in the rates of infectious disease processes which are capable of precipitating or accelerating the course of AML. Indeed, infection has long been suspected as a possible factor in the aetiology of leukaemias.It is also conceivable that the annual peak observed in January reflects an increased diagnostic activity during winter months; higher levels of healthcare demand because of winter‐related illness may lead to the diagnosis of AML. Note, however, that the incidence rates recorded in February and March were higher than the incidence rates observed in December. According to such an explanation, one expects to find higher incidence rates in December if cases that would have otherwise been diagnosed in February and March were being detected in the two months prior because of an increased diagnostic activity. Conversely, some have suggested that observed seasonal variation in certain cancers is likely to reflect the reduced diagnostic activity during vacation months.Because the data are from a population‐based registry that had a high level of completeness and accuracy throughout the study period, the main results are unlikely to have been influenced by ascertainment or recording bias. However, several potential limitations of this study need to be considered. One limitation is that AML cases were obtained from the CMBD registry as defined by ICD‐9 and no other AML classifications were available. Hence, further studies using more modern classifications of AML like the World Health Organization classificationIn conclusion, this large study provides the strongest evidence of a seasonal variation in the diagnosis of AML, with a significant peak during the month of January. Such seasonal effect was consistent among subgroups according to sex and age. These findings could be used to raise the level of AML suspicion among haematologists where a cytopenia is detected in a complete blood count during winter months, particularly in January. Taken together, previous smaller studies have provided conflicting evidence, likely due to lower power and/or to the use of less advanced statistical approaches. Most studies assessing seasonal trends in AML diagnosis do so in northern‐hemisphere countries with temperate climates. Investigation of seasonality in cohorts from a wide range of geographical areas would be informative to determine the influence of latitude and climate on AML incidence. The demonstration of seasonality in our study strongly implies that seasonal factors, such as infectious agents or environmental triggers, influence the development and/or proliferation of AML, pointing to prevention opportunities. Analyses were stratified by important person‐level covariates such as sex and age that may confound seasonal trends in diagnoses. However, future studies that include more explanatory variables at individual level such as comorbid conditions and environmental exposure would augment the current results, providing further understanding into seasonal variation of AML diagnoses and the aetiology behind the observed variation.The study was conceived and designed by Juan Manuel Alonso‐Domínguez and Fernando Sánchez‐Vizcaíno. The funding for the project leading to this publication was acquired by Juan Manuel Alonso‐Domínguez and Fernando Sánchez‐Vizcaíno. The data were acquired by Raquel Barba, Maria Dolores Martin and Juan Manuel Alonso‐Domínguez. The data curation was carried out by Fernando Sánchez‐Vizcaíno and Carmen Tamayo. The methodology, formal analysis and visualization were conducted by Fernando Sánchez‐Vizcaíno. The manuscript was drafted by Fernando Sánchez‐Vizcaíno and Juan Manuel Alonso‐Domínguez. The manuscript was revised critically for important intellectual content by Carmen Tamayo, Fernando Ramos, Daniel Láinez‐González, Juana Serrano‐López, Raquel Barba, Maria Dolores Martin and Pilar Llamas. All authors gave final approval for publication.This study was supported by research funding from Celgene S.L.Juan Manuel Alonso‐Dominguez received research funding from Incyte Corporation, Pfizer International, and Astellas Pharma Inc. outside the present work.Appendix S1Click here for additional data file.


Saccharomyces cerevisiae does not appear merely to be associated with its intrinsic nucleosome‐excluding properties. In addition, we show that the GC content alone can predict more than 60% of the in vitro nucleosome map, providing further evidence that the intrinsic nucleosome positioning is more greatly determined by GC content than poly(dA) stretches. Our results are consistent with a model in which poly(dA) stretches act at two distinct levels: first, by its low GC content, which intrinsically contributes to hinder nucleosome formation, and second, by its contiguous runs of dA that selectively drive the recruitment of non‐histone proteins with structural and functional roles.The nucleosome is the basic structural element of genomic DNA packaging and plays a role in transcription, replication, and recombination. Poly(dA) tracts are considered major sequence determinants of nucleosome positioning, although their role is not well understood. Here, we show that the homopolymeric character and the low GC content of poly(dA)s play different roles in nucleosome formation. We found that the inherent low GC content of poly(dA) alone can account for the deep and anisotropic nucleosome depletion at structurally and functionally important regions of promoters and origins of replication. We also show that the level of nucleosome occupancy at poly(dA) is strongly related to the local nucleotide background and its high frequency of occurrence in  Saccharomyces cerevisiae genome.The homopolymeric nature and the GC content of poly(dA)s play different roles in the GC content explains the intrinsic asymmetric low nucleosome occupancy at poly(dA) sites.The high frequency of poly(dA) is not simply related to its intrinsic nucleosome‐excluding property.GC content can predict more than 60% of in vitro nucleosome occupancy.The nucleosome occupancy of AAAAA is strongly dependent on the background nucleotide composition. Saccharomyces cerevisiae.The cross‐correlation of dA, dT, and GC content with nucleosome occupancy in autonomously replicating sequences of in vitro reconstituted chromatin. The inherent low GC content (high AT content) of poly(dA) alone can account for the deep and anisotropic nucleosome depletion at structurally and functionally important regions of promoters and origins of replication in  The processed genome‐wide data of in vivo nucleosome occupancy mapped by micrococcal nuclease (MNase)‐seq  . Genome‐wide maps of nucleosome occupancy of in vitro‐assembled chromosomes . Wiggle (.wig)‐type files were converted into bedGraph using a simple format converter written in C# in our laboratory.The genome annotation of 2.2https://www.r-project.org). Cross‐ and auto‐correlation analyses were performed by computing the Spearman's rank (Rs) or Pearson's correlation coefficient (Rp) between two aligned genome‐wide profiles progressively shifted by 1 bp, typically from −600 to +600 bp  and YeastpRofile (https://sourceforge.net/projects/yeastprofile/), respectively. When cross‐correlation was performed on only a specified genomic category, as CDSs, promoters, or ARSs, the regions outside the category boundaries of the first profile were excluded. The analyses of Rp cross‐correlations s in ARSs, promoters, and CDSs were estimated from the native sequences after shuffling their nucleotides within each individual genomic feature. The average frequency of 100 shufflings was used in calculations.For the cross‐correlation of nucleotide occurrences, the genomic nucleotide sequence was converted into a numerical binary sequence by replacing the specific nucleotide with 1 and the others with 0.Genome‐wide DNA structural features were predicted using the DNAshape method  and GC content in the genomic arrangement of nucleosomes, we cross‐correlated the occurrence of the four nucleotides and GC content with the nucleosome occupancy of chromatin assembled in vitro from purified DNA and histone octamers  is positively auto‐correlated within about 50 nt Figure  characte3.4Rp = 0.78), indicating that GC content alone can explain more than 60% of the intrinsic nucleosome occupancy in S. cerevisiae (coefficient of determination R2 = 0.61)  sites. Above, we determined the cross‐correlation profile of GC content with nucleosome occupancy. Here, we used the 201 nt central window of that cross‐correlation profile to predict the genome‐wide nucleosome occupancy, as described in the Materials and Methods section. Surprisingly, the nucleosome occupancy predicted by GC content alone shows the same asymmetric cross‐correlation profile with dA and dT that we found at ARS and 200‐TSS sites of reassembled chromatin, as well as the symmetric cross‐correlation profiles at CDSs Figure . Moreove) Figure .Finally, when we additionally included the profiles of dT and dA in the calculations of the simulated nucleosome occupancy by GC content, we did not observe any improvement in the prediction accuracy.3.5To clarify the role of DNA sequence in the formation of NDRs, we compared the DNA sequence and the nucleosome occupancy of the regions upstream of the TSSs and TTSs. For this purpose, we computed the cross‐correlation profile of the four nucleotides, GC content, and nucleosome occupancy with the quantified TSS and TTS of the transcript isoforms s in CDSs, 200‐TSSs, and ARSs. Figure x‐axis according to their average nucleosome occupancy in the whole genome .We analyzed the relationship between nucleosome occupancy and GC content of different genomic regions to establish if the intrinsic low nucleosome occupancy of ARSs and 200‐TSSs can be explained by their GC content. The relationship between nucleosome occupancy and GC content, illustrated in Figure 3.8To test if the anisotropic position of poly(dA) relative to nucleosome depletion reflects an anisotropy of the DNA structure, we cross‐correlated the in vitro nucleosome occupancy with the structural features predicted by the DNAshape method s relative to nucleosome‐depleted tracts in in vitro‐assembled chromosomes. It is unclear how this in vitro asymmetric positioning is generated and if the enrichment of poly(dA) at 200‐TSS and ARS sites is determined by its nucleosome‐excluding property. Since two major characteristics of poly(dA) tracts are the low GC content and dA‐homopolymeric nature, we evaluated the hypothesis that these two sequence features could play distinct roles: the low GC content could be the primary determinant of the intrinsic nucleosome arrangement at poly(dA) sites including the asymmetric nucleosome depletion, whereas the dA‐homopolymeric trait may be the target of functional and structural non‐histone proteins. All our results were consistent with this hypothesis.First, we found that the nucleosome occupancy simulated using only GC content can predict not only the in vitro genome‐wide occupancy with 60% accuracy Figure  and the Our analyses also showed that the mere presence of a dA‐homopolymeric tract is not enough to generate the asymmetric nucleosome occupancy. Indeed, we found that the pentamers AAAAA and TTTTT are positioned asymmetrically with respect to nucleosome arrangement in ARSs and 200‐TSSs and symmetrically in CDSs Figure .Additionally, we found that the high frequency of occurrence of poly(dA)s of various lengths in ARSs and 200‐TSSs Figure  does notRp = 0.98)  Figure . FinallySchizosaccharomyces pombe, poly(dA) tracts affect, but do not deplete, nucleosomes  in the intrinsic nucleosome positioning and stability. For example, in mice and humans, which have histones very similar to yeast, the trend of biased nucleosome occupancy around poly(dA) and poly(dT) is opposite  seems to be barely relevant compared to GC content in in vitro nucleosome formation, the role of poly(dA) turns out to be more important in in vivo nucleosome arrangement. Indeed, poly(dA)s form an asymmetric barrier to the nucleosome transit mediated by ATP‐dependent chromatin remodelers  contributes to the intrinsic nucleosome depletion due to its inherent low GC content and not its homopolymeric nature. In ARSs and promoters, the asymmetric positioning of poly(dA) stretches relative to the NDRs reflects its asymmetric positioning relative to regions with low GC content. The frequent occurrence of dA‐homopolymeric stretches in promoters and ARSs could reflect their double role of disfavoring nucleosome formation by their poor GC content and constituting a directional signal for non‐histone proteins implicated in both the stabilization of nucleosome‐free regions and the regulation of DNA transcription or replication.The author declares no conflict of interest.Supporting information.Click here for additional data file.