Antimicrobial resistance (AMR) is an escalating global crisis with serious health, social, and economic consequences. Building social-ecological system resilience to reduce AMR and mitigate its impacts is critical.The aim of this study is to compare and assess interventions that address AMR across the One Health spectrum and determine what actions will help to build social and ecological capacity and readiness to sustainably tackle AMR.We will apply social-ecological resilience theory to AMR in an explicit One Health context using mixed methods and identify interventions that address AMR and its key pressure antimicrobial use (AMU) identified in the scientific literature and in the gray literature using a web-based survey. Intervention impacts and the factors that challenge or contribute to the success of interventions will be determined, triangulated against expert opinions in participatory workshops and complemented using quantitative time series analyses. We will then identify indicators using regression modeling, which can predict national and regional AMU or AMR dynamics across animal and human health. Together, these analyses will help to quantify the causal loop diagrams (CLDs) of AMR in the European and Southeast Asian food system contexts that are developed by diverse stakeholders in participatory workshops. Then, using these CLDs, the long-term impacts of selected interventions on AMR will be explored under alternate future scenarios via simulation modeling and participatory workshops. A publicly available learning platform housing information about interventions on AMR from a One Health perspective will be developed to help decision makers identify promising interventions for application in their jurisdictions.To date, 669 interventions have been identified in the scientific literature, 891 participants received a survey invitation, and 4 expert feedback and 4 model-building workshops have been conducted. Time series analysis, regression modeling of national and regional indicators of AMR dynamics, and scenario modeling activities are anticipated to be completed by spring 2022. Ethical approval has been obtained from the University of Waterloo’s Office of Research Ethics (ethics numbers 40519 and 41781).This paper provides an example of how to study complex problems such as AMR, which require the integration of knowledge across sectors and disciplines to find sustainable solutions. We anticipate that our study will contribute to a better understanding of what actions to take and in what contexts to ensure long-term success in mitigating AMR and its impact and provide useful tools  to guide management and policy decisions.DERR1-10.2196/24378 Antimicrobial resistance (AMR) is a global health crisis that impacts the health and well-being of people and is projected to cause significant social and global economic losses -4.One Health approach, to sustainably address AMR and protect future generations from a post–antibiotic era [AMR weakens the effectiveness of many antimicrobial agents  used to treat infectious diseases in both humans and animals and is hard to contain because resistance spread between humans, animals, and the environment ,6. Throuotic era ,7,8.One Health, a paradigm and an approach, recognizes how the health of people is connected to the health of the environment and animals. It emphasizes multisector and transdisciplinary collaborations to comprehensively understand issues and develop sustainable solutions to achieve the health and well-being of people, animals, and the environment ,9-11.In addition to needing a One Health approach, AMR also benefits from being viewed through a complex adaptive system (CAS) lens . CASs arStaphylococcus aureus hospital outbreak is resolved). Transformability is the capacity of a system to fundamentally change in the face of unsustainable conditions . These concepts can be related to ideas of proactive and reactive resilience in supply chain studies [By viewing the factors that impact AMR as a CAS, we can then apply the lens of social-ecological system (SES) resilience, where resilience is the capacity of a system to cope, adapt, or transform itself into a new state, to manage disturbances in its environment ,22. Resi studies  and adap studies . In the effectiveness in preventing or controlling AMU or AMR, or transforming a system to ensure desired AMU or AMR levels, and (2) the factors that contribute to their success as a way to understand system resilience. As resilience is an emergent property of a system, interventions can be used to measure or test a system’s resilience to AMR. If an intervention is successful, it means that the intervention within the system improved some parameters related to mitigating AMR; in this case, understanding the details of the intervention can help uncover what parts of the underlying system contributed to that resilience. In contrast, if an intervention is not successful, it means that the intervention within the system did not improve parameters related to mitigating AMR; in this case, understanding the details of the intervention can help uncover gaps or deficiencies in the system, including aspects we need to build into the system to bolster resilience  .This paper describes a study that aims to examine the effects of interventions on AMU and AMR and identify the key factors that influence our ability to address AMR. Interventions will target regional, national, and subnational levels , across the One Health spectrum in high-income  and low- or middle-income  regions of the world. We selected these regions because Europe has undertaken several efforts to address AMR , Asian cTo address our study aims, we will complete the following objectives:Identify interventions addressing AMU or AMR and determine the factors that challenge or contribute to the success of interventions.Quantify and validate the ability of interventions to prevent or control rising AMU or AMR or transform the system from persistently high to lower levels of AMU or AMR.Assess whether the types of national and regional indicators that are currently available can predict national AMU and AMR trends across animal and human health.Create causal loop diagrams (CLDs) that depict the system of factors that influence AMR in a high-income region  and a low- or middle-income region (Southeast Asia).Describe the potential long-term impacts of select interventions that aim to reduce AMR under alternate future scenarios.Our study frames AMU and AMR as part of CAS. Within this framework, we will apply 3 different tools from the respective fields of our interdisciplinary research consortium , as follows.Although ecological resilience stresses the capacity of a system to withstand shock and maintain function, SES resilience theory posits that a system can have varying capacities to cope, adapt, and transform when disturbances or shocks arise in its environment  ,22,40. TIntroduction section); (5) learning and experimentation; (6) broadening participation; and (7) promoting polycentric governance [The first 3 principles represent the key SES properties to be managed to enhance resilience: (1) diversity and redundancy, (2) connectivity, and (3) slow variables and feedback. The remaining 4 principles reflect the attributes of the governance system that manages the aforementioned SES properties: (4) understanding CAS (as described in the vernance  (Multimevernance -49). Somvernance ,44,49. OThe Driver-Pressure-State-Impact-Response (DPSIR) framework is an analytical tool for analyzing environmental problems . AdoptedDrivers exert pressures  that change the state of the environment, which corresponds to the level of AMR measured in different pathogens, which leads to an impact , and results in a response by society to minimize the impacts by addressing any part of the DPSIR causal chain [Within DPSIR, drivers are social activities that can increase or lessen a particular behavior . al chain . BuildinCLDs are models that help visualize how different factors in a system are related . Systemscase review), a web-based survey to collect information about interventions in the gray literature, participatory workshops, time series analysis, regression modeling of indicators, and scenario modeling of interventions. We will identify published and unpublished interventions addressing AMR or its key pressure AMU via a case review and web-based survey. Intervention impacts and the factors that challenge or contribute to the success of interventions will be determined, findings triangulated against expert opinion during participatory workshops, and complemented using time series analysis methods. Regression modeling to identify indicators that can predict national AMU and AMR dynamics across animal and human health will follow. We will then bring AMR experts  and experts in other content areas  who are not traditionally engaged in discussions about AMR together in participatory workshops to create CLDs of the factors influencing AMR in the European and Southeast Asian food systems. Using relevant data collected throughout our study, we will quantify and convert these CLDs into compartment models to simulate the long-term impacts of selected interventions on AMR under alternate future scenarios, re-engage the aforementioned workshop participants to validate simulation findings, and explore the actions and conditions that decision makers should consider to sustainably limit AMR under each future scenario. We will conduct this study by applying the SES resilience theory for the first time in an explicit One Health and participatory context using mixed methods. Our approach includes 6 interrelated data collection and analysis activities: a review of interventions published in the scientific literature (herein termed Escherichia coli).We will search for interventions addressing AMU or AMR published at any time in the scientific literature using indexed search terms in PubMed and Scopus. The titles and abstracts of each publication will be screened for relevance, and for further screening, retained articles will be read in full. We will include articles that focus on interventions addressing AMU or AMR at subnational, national, or regional levels and exclude articles that are theoretical or policy comparisons or focused on recommendations . Additional articles will be identified through reference lists of retained publications and articles recommended by the members of our research consortium. To manage the anticipated large volume of relevant interventions, we will focus on interventions that target important One Health organisms that are most likely to cross between human, animal, and environmental systems and can cause disease in humans and animals  social system , (2) bioecological system , (3) triggers and goals of the intervention , (4) implementation and governance of the intervention , and (5) assessment  . Two resTheoretical Framework and Tools section, and the additional factors that emerge from the data and are not already captured by this theory, to identify the factors that challenge or contribute to the success of interventions. Where quantitative data exist, appropriate statistics will be applied .Analysis will involve coding data from each intervention against the 7 principles of the SES resilience theory, highlighted earlier under the successful interventions likely exists, (2) high-income versus low- or middle-income contexts , and (3) settings  to determine if any factors that challenge or contribute to the success of interventions differ by context. These factors will then be compiled and compared with the SES resilience theory, further characterizing the 7 resilience principles highlighted under the Theoretical Framework and Tools section, adding any additional factors that emerge from the data, and ultimately revising the theory about what factors enhance system resilience to AMR.We will determine the factors that cross-cut interventions and then categorize interventions based on (1) an intervention’s success in achieving intended outcomes with recognition that a publication bias toward The following lists the outputs for the case study for use in other data collection or analysis study activities: (1) factors that challenge or contribute to the success of interventions that address AMU or AMR found in published scientific literature; (2) revised theory and hypotheses about the factors that enhance or challenge SES resilience to AMR based on case review findings; (3) a list of interventions identified in the published scientific literature that have shown success or less or partial success in preventing or controlling rising AMU or AMR and in transforming a system to low AMU or AMR; and (4) if available, a list of factors that contribute to AMU or AMR and any qualitative or quantitative data that describe identified factors and any relationships between factors.A voluntary web-based survey will be used to collect interventions that address AMU or AMR in the gray literature to expand the understanding of factors that lead to the success of interventions beyond case review findings.yes or no to participate before starting the survey. Data collection will cease when (1) no new interventions are reported across participants or (2) within 1 month of the survey implementation, whichever comes first.As the survey is an environmental scan of existing interventions with no hypotheses to be tested, no sample size calculation is necessary. All survey activities will adhere to the approved procedures outlined by the University of Waterloo Research Ethics Committee. To identify potential survey participants, we will develop a matrix of regions based on the WHO’s definition of regions   and popuCase Review, including the following: (1) social system, (2) bioecological system, (3) triggers and goals of the intervention, (4) implementation and governance of the intervention, and (5) assessment. One question will be presented per screen, and the not applicable response option will be offered. On the basis of input from the type of people that will complete the survey, we will ask survey respondents to coordinate reporting with their collaborators to ensure that a given intervention is reported once, share documents relevant to each reported intervention, and provide permission to include their reported intervention(s) and contact information in the web-based learning platform [This survey will collect the same information as the data extraction framework described under platform .Up to 5 individuals, identified by the research consortium as highly knowledgeable about AMU and AMR interventions, will be invited through email to pretest the web-based survey and complete a 30-minute follow-up telephone interview to determine if they interpret and answer questions as intended and obtain their impressions about the web-based survey and its contents. Research team members will test the functionality of the survey. Feedback will inform survey revisions.successful interventions likely exists, (2) high-income versus low- or middle-income contexts , and (3) settings  to determine factors unique to these contexts that challenge or foster the success of interventions. These factors will be compiled and compared with the SES resilience theory, providing information that further characterizes the 7 principles highlighted under the Theoretical Framework and Tools section, adding any additional factors that emerge from the data, and developing hypotheses about what factors enhance system resilience to AMR. We will use the Checklist for Reporting Results of Internet E-Surveys as a guide to inform the reporting of methods and analysis in future publications of the web-based survey [The analysis will involve coding reported intervention data against the 7 principles of SES resilience theory and any additional factors that emerge from the data to determine the factors that challenge or contribute to the success of interventions. Where quantitative data exist, appropriate statistics will be applied . We will determine the factors that cross-cut interventions and then categorize interventions based on (1) an intervention’s success in achieving intended outcomes with recognition that a publication bias toward d survey .The following lists the outputs from the web-based survey for use in other data collection and analysis study activities: (5) list of factors that challenge or contribute to the success of interventions in gray literature; (6) revised theory and hypotheses about the factors that enhance or challenge SES resilience to AMR based on survey findings; (7) a list of interventions that have shown success or less or partial success in preventing or controlling rising levels of AMU or AMR and transforming a system to lowered levels of AMU or AMR from the gray literature; and (8) if available, a list of factors that contribute to AMU or AMR and any qualitative or quantitative data that describe identified factors and any relationships between factors.Workshops are a type of research methodology that brings groups of people together to learn from one another, problem-solve, or innovate and, through the process, generate integrated knowledge about a domain of interest to fulfill a research purpose . We willFor both workshop types, we will select participants from across Europe and Southeast Asia who represent diverse perspectives. For the expert feedback workshops, we plan to recruit 12 to 28 participants in total (n=6-14 in Europe and n=6-14 in Southeast Asia) representing the human, animal, and environmental sectors. These participants will have a broad understanding of interventions addressing AMU or AMR in animals, humans, or the environment and will include stakeholders who work directly with end users . For the model-building workshops, we plan to recruit new participants for each workshop session and aim for 24 to 56 different participants in total (n=12-28 in Europe and n=12-28 in Southeast Asia) that ideally represent an equal distribution of experts in AMR  and experts in other areas of content , who may not usually be considered in discussions about AMR, but may directly or indirectly impact AMR. AMR experts will provide a deeper understanding of the AMR context in Europe’s and Southeast Asia’s food systems whereas nontraditional experts will help to advance the current understanding of a broader range of factors that may generate AMR, beyond what AMR experts already know. Participants will be identified from (1) the research consortium’s networks and (2) web-based search engines , professional networking sites , social media sites , and websites of professional, governmental, nongovernmental, and industry organizations that address AMR in human, animal, and environmental sectors.Both workshop types will be led by a facilitator, guided by a semistructured interview guide, audio recorded, and involving note takers to record discussion points. We anticipate that recruited participants will speak English, and we will provide translation if needed.Key informant interviews may be conducted after both workshop types to capture additional perspectives identified during workshop discussions as important to fill knowledge gaps. Interviews will follow the same semistructured interview guide used in the workshop and be conducted over the phone, 60 minutes in duration, and audio recorded. All participants will receive via email an anonymous and voluntary web-based survey to evaluate the extent to which the workshop or interview approach fosters open dialogue and learning and participants’ intentions to apply what they will learn in their work. Data will be descriptively analyzed, and findings will inform improvements to future workshops .Workshop sessions will begin by welcoming participants and describing the workshop objectives and agenda. After presenting the findings from the case review (output 1) and web-based survey (output 5), experts will discuss whether they agree or disagree with the findings and identify any missing factors that may challenge or contribute to intervention success based on their expert opinion. Discussions will continue until no new information emerges.Transcripts from workshops and any interviews will be coded and compared across coders  for consistency and thematically analyzed to identify the factors that challenge or contribute to the success of interventions. We will compare these findings to outputs 1 and 2 from the case review and outputs 5 and 6 from the survey to further refine the theoretical framework of factors influencing the success of interventions and draw hypotheses about what factors enhance system resilience to AMR.The following lists the outputs from the expert feedback workshops for use in other data collection and analysis study activities: (9) factors that challenge or contribute to the success of interventions based on expert opinion and (10) revised theory and hypotheses about factors that enhance or challenge SES resilience to AMR.Workshops will begin by welcoming participants, describing the workshop objectives and agenda, and providing background information on AMR as participants will have varying levels of understanding about the topic. The facilitator will introduce a previously developed CLD of factors influencing AMR in Canada’s food system , and parTo build these CLDs, 1 researcher (MC) will extract every factor from the workshop and any interview transcripts—any descriptions about the direction and nature of relationships between factors  and potential interventions to address AMU or AMR mentioned by participants. Missing information will be added to the model produced during the workshops using appropriate software. Each relationship between factors will be depicted by an arrow (→) to denote its direction, and a positive (+) or negative (−) sign will be added to the arrow to illustrate the nature of the relationship. A positive relationship indicates that 2 factors are moving in the same direction . A negative relationship indicates that 2 factors move in opposite directions . Researchers  will review the transcripts and model and discuss areas requiring clarification regarding the placement of factors and relationships. Disagreements will be resolved through consensus. Each factor in each CLD will be measurable  and written as a short textual phrase. Transcripts will be thematically analyzed (IAL) to describe key findings from the workshops. The CLDs and a workshop summary of key themes will be sent to participants for validation via feedback.The following lists the outputs from the model-building workshops for use in other data collection and analysis study activities: (11) CLD of factors influencing AMR in the European food system; (12) CLD of factors influencing AMR in the Southeast Asian food system; (13) qualitative or quantitative data that describe each factor and relationship in the CLDs; and (14) a list of potentially promising interventions to address AMR.The aim of the time series analysis is to complement findings from case reviews, surveys, and expert feedback workshops by quantifying resilience and transformations using time series analysis methods. We will quantify (1) preventive resilience, approximated by the stability of resistance levels over time; (2) control resilience, approximated by the ability to lower resistance levels following a relatively large increase; and (3) transformability, approximated by the size and duration of reduction in the specific metric.We will identify different types of interventions  with quantitative data to run time series analyses. By creating and applying metrics of resilience and transformability to standardized data formats, a more objective comparison will be developed and can be applied in the future to standard time series. The widespread quantification of resilience in high-frequency time series is challenged by the limited availability of data, different formats, and separating, for example, internal seasonal dynamics from external shocks. We will overcome these challenges by devising metrics designed to standardize reporting formats and annual time series of at least 10 years of length. The methods involve (1) specification of the metrics, (2) data collection and analysis, (3) sensitivity analysis, and (4) cross-validation.E coli, an important One Health organism that can live in different places  and can cause diseases in humans and animals.We will use human data from national and regional authorities such as ResistanceMap  and the Our method quantifies stylized metrics as proxies for the 3 types of resilience and metrics that can be varied in terms of 2 or more threshold values. For example, the adapted control resilience method quantifies the years to achieve an x% reduction in AMU and AMR after a y% increase over a 1- to 5-year period. By varying y and x over the observed variation, a bivariate density distribution is produced, which can be analyzed in terms of quantiles and compared using standard nonparametric or parametric statistics, depending on skew and sample size. Preventive resilience measures the x-year increase in AMU and AMR and can, in contrast to control resilience and transformative success, be applied as a rolling metric to the time series. Transformations will be quantified in terms of duration and proportional decrease following 3 to 5 years of stable high values. For both resilience and transformation metrics, we will apply the metrics to (1) a general set of time series and (2) a subset of time series where we know of specific interventions from the case review and web-based survey (outputs 3 and 7).We will explore the sensitivity of measuring preventive resilience in absolute versus relative terms, the latter by accounting for the mean level of AMR. Similarly, control resilience can be corrected for mean levels of resistance, assuming either a first-order linear or a polynomial relationship between reduction in AMU and reduction in resistance. To overcome the challenge that many countries only have either AMU or AMR data available, but not both, the resilience metrics will be quantified for both AMR and AMU data; the latter serving as a surrogate, for example, by analyzing the ratios of second- to first-line AMU.We will cross validate the time series analyses against a subset of the particularly well-documented interventions identified through the case review (output 3) and survey (output 7). For interventions at the national or regional level, this can be done through direct comparison with the broader time series analysis. For local-level interventions, we will acquire the relevant local time series data for cross-validation.The following lists the outputs from the time series for use in other data collection or analysis study activities: (15) any new interventions identified for the specific purpose of time series analysis; (16) time series data set; and (17) metrics of preventive and control resilience and transformability.To assess whether currently available indicators predict national AMR dynamics, we will identify, test, and extrapolate indicators of preventive and control resilience and transformability. This will involve (1) specifying, collecting, and analyzing national and regional indicators of resilience and transformability; (2) quantifying the explanatory and predictive capacity of indicators through regression and simulations; and (3) producing an extrapolated data set of resilience and transformability based on these indicators.We will use metrics of preventive and control resilience and transformability from time series analyses (output 17) as a basis for identifying indicators that explain variation in resilience and transformability metrics. National and regional statistics will provide the basis for selecting indicators, and these statistics will be collected from existing databases, such as those hosted by the UN and by contacting regional and national statistics offices. We will select indicators using specific hypotheses about their contribution to resilience and transformability based on findings from the case review (output 2), survey (output 6), expert feedback workshops (output 10), and previous work of coauthors (PSJ and DW) in applying resilience theory and principles in the context of AMR ,44,49.We will then code the indicators according to the DPSIR framework.Context-specific driver indicators will be added to a previously developed DPSIR framework  using thWe will use data on the key pressure variable (AMU) and the key state variable (AMR) used in the time series.Response variables will be scored from a review of national or regional policies led by coauthor DW and grouped into categories of preventive responses (addressing drivers), mitigative (addressing AMU), restorative (addressing AMR), and adaptive responses (addressing impacts). For national indicators, we will in part rely on the WHO-FAO-OIE survey of national actions to limit AMR .The explanatory capacity of indicators will be tested on a training subset of the time series and their predictive capacity tested on the full subset of the time series. On the basis of this analysis, we will use a broader data set of national  statistics and run regression modeling or simulation modeling to perform an extrapolative analysis predicting the resilience and transformability of countries and regions without the necessary time series data available.The following lists the outputs of the regression modeling of national and regional indicators of AMU, AMR, and impacts: (18) data set of social-ecological indicators; (19) explanatory capacity of indicators; (20) predictive capacity of indicators; and (21) extrapolated data set of national resilience and transformability.We will conduct 2 types of scenario modeling: (1) mixed methods simulation modeling using fuzzy logic and (2) group-based scenario modeling using a participatory approach ,74 to exhigh, medium, and low categories) to create differential equations [We will use the CLDs of AMR in the European and Southeast Asian food systems (outputs 11 and 12) and fuzzy set theory -77 to buquations -77 to siWe will then use these models to explore the impact of selected interventions on AMR over a 50-year timeframe under alternate future scenarios. To select these interventions, our multidisciplinary research consortium will independently rank the list of interventions from the case review (output 3), survey (output 7), and modeling workshops (output 14) from most to least promising and come to an agreement about the top 2 interventions that may impact AMR. To develop our scenarios, we will construct a two-by-two matrix with climate change on one axis and governance structure change on the other axis, two key factors likely to impact the food system over time, to produce the 4 future scenarios illustrated in As a limited amount of data at the European or Southeast Asian food system scale make it difficult to validate the simulation model against a data set, we will take the simulation outputs to the group-based scenario workshops for participant validation ,79. If tA total of 4 virtual workshops (2 in Europe and 2 in Southeast Asia) lasting 4 days will be conducted. These workshops will bring together diverse perspectives to (1) validate the model and intervention outcomes from the simulation and (2) brainstorm what factors and conditions  are necessary to strengthen the potential for selected interventions to combat AMR over 10-, 30-, and 50-year timeframes under the alternate future scenarios in To increase stakeholder commitment and their potential to apply what they learn about AMR into action, we will invite via email selected individuals who participated in our model-building workshops described earlier for a total of 12 to 30 participants (n=6-15 in Europe and n=6-15 in Southeast Asia). These participants will represent an equal distribution of experts in AMR  and other areas that may directly or indirectly impact AMR . If a participant is unavailable, we will identify and approach a new individual representing a similar perspective using the sampling procedures described for the model-building participatory workshops.Workshops will be led by a facilitator, guided by a semistructured interview guide, and audio recorded. Members of our research consortium will take notes to record the discussion points. After the welcome, introduction, and overview sessions of workshop objectives, the facilitator will present the simulation model and how the 2 interventions impact AMR over a 50-year horizon under various scenarios. Participants will then discuss whether the model and intervention outcomes align with their expert opinions about how the system should behave to validate the model. Through facilitated discussions, participants will also discuss (1) why they believe each intervention will impact AMR under future scenarios, (2) what barriers and challenges may impact intervention success, and (3) what actions and supports  are necessary to ensure intervention success and circumvent barriers and negative consequences over time. Participants will be encouraged to bring forth all ideas until no new information emerges.A researcher (MC) will extract data from transcribed workshop audio recordings to determine whether participants agree with findings from the simulation modeling and any adjustments made to the model about each intervention’s probable impact(s) under each scenario of the future in the European and Southeast Asian context. A researcher (IAL) will conduct a thematic analysis and develop narratives that describe how each intervention will behave under each alternate future scenario, and the factors and conditions that should be implemented to sustainably mitigate AMR while maintaining social, economic, and environmental health over time, based on participant feedback.The following lists the outputs from the scenario modeling activities: (22) scenarios of alternate futures; (23) simulation model of AMR in the European and Southeast Asian food system; (24) simulation outputs of the impacts of 2 interventions on AMR; and (25) narratives that describe intervention impacts under alternate future scenarios.Ethics approval for activities involving participants has been granted by the University of Waterloo’s Office of Research Ethics (ethics clearance numbers 40519 and 41781).E coli were analyzed in full based on the SES resilience theory (reporting is underway).A total of 669 interventions have been identified. In addition, 42 interventions specifically targeting The survey has been sent to 891 individuals who work on AMR or carry out work that may impact AMR from 6 regions of the world . Survey analysis is pending.A total of 4 in-person expert feedback workshops that engaged stakeholders  representing human (n=5), animal (n=4), human and animal (n=3), and environment (n=2) sectors have been completed.A total of 4 in-person model-building workshops that engaged 32 stakeholders  representing advocacy (n=2), nutrition, food security, and food safety (n=5), economics and trade (n=2), human medicine (n=5), pharmaceutical (n=3), agricultural food and animal health (n=10), sustainable food innovations (n=2), environment (n=1), peace and leadership (n=1), and law (n=1) perspectives have been conducted. Analysis is underway.Time series analysis activities are anticipated to be completed by spring 2022.Activities are anticipated to be completed by spring 2022.Mixed methods simulation modeling activities and 4 virtual group-based scenario modeling workshops and analysis are anticipated to be completed by spring 2022.To our knowledge, this is the first study to apply SES resilience theory, systems thinking, and a One Health approach to better understand how to sustainably combat AMR. Our study is in progress and is not yet complete. However, we anticipate that our study will help make sense of the diversity of actions to tackle AMR and add to our limited understanding of which actions work under what conditions. We intend to consolidate our findings into a web-based platform that will allow stakeholders to add interventions and use the tool to determine what actions to take in their respective contexts. We also anticipate that through a series of participatory workshops that engage AMR experts and stakeholders who may not usually be engaged in discussions about AMR, our study will produce useful tools  to help build stakeholder capacity to recognize AMR as a CAS and plan interventions under uncertain future conditions. The time series and regression of indicators analyses will help to gain a better understanding of the relationships among drivers, pressures, states, and responses regarding AMR. In addition, as we quantify and carry out simulation modeling using data from our study and the literature, our study will help to identify data gaps for future research.One Health and systems thinking have gained prominence in public health but can be challenging to conduct because they necessitate collaboration and the integration of knowledge from science and practice across different sectors and disciplines. Our protocol provides other researchers with an example of how to apply these approaches to study a complex public health problem such as AMR with an interdisciplinary research team and involving AMR experts and nontraditional stakeholders. In fact, we developed this paper to help our research consortium bridge our disciplinary-specific knowledge, skills methods, and tools and make our processes transparent so that others can learn from our experiences as we implement this mixed methods study.


Liquid Crystal Elastomers (LCEs) combine the anisotropic ordering of liquid crystals with the elastic properties of elastomers, providing unique physical properties, such as stimuli responsiveness and a recently discovered molecular auxetic response. Here, we determine how the molecular relaxation dynamics in an acrylate LCE are affected by its phase using broadband dielectric relaxation spectroscopy, calorimetry and rheology. Our LCE is an excellent model system since it exhibits a molecular auxetic response in its nematic state, and chemically identical nematic or isotropic samples can be prepared by cross-linking. We find that the glass transition temperatures ( Liquid Crystal Elastomers (LCEs) are lightly cross-linked polymer networks with mesogen units incorporated within the main polymer chain, or as pendant units. LCEs combine the anisotropic behavior of liquid crystals, arising from the long-range orientational order of the mesogen units, with the rubber-like elastic behavior of conventional elastomers . The macTo understand, and be able to predict material behavior, it is essential to understand the behavior of the relevant molecular or segmental relaxations. Examples include the links between relaxations and material aging and rejuvenation , the resectively ,19. In aectively ,20. Moreectively ,20. ThusThe structural ταD-values . Typicalrrhenius ,23,29. Aragility ), the T-pendence . This dyith Tα,β ,27 and with Tα,β  and a choldstein  and asso barrier .The general glass-transition phenomenology for polymeric glass-formers is similar to that of non-polymeric systems. However, due to chain connectivity and the corresponding presence of intra-molecular degrees of freedom, the inter-relation between the plicated ,34. Moretogether ,35, meanradation . Cross-leased Tg .For LCEs, there have been relatively few studies focusing on molecular relaxation behavior ,38,39. T fixed T . The stransition ,40. The h SCLCPs ,41,42,43h SCLCPs ,38,39 anong axis ,41,44,45In SCLCPs, just as for non-LC glass formers, a cross-over in dynamic behavior has been observed for 1−1.3×Tg ,46. Intengle VFT . This obnegative Poisson ratio in one of the transverse axes is observed . Th. Thtan, as well as modulated Differential Scanning Calorimetry (m-DSC), was employed to investigate the glass transitions of the isotropic and nematic LCEs. A TA Instruments Q2000 was used to perform both DSC and m-DSC. Approximately 10 mg of LCE sample was cut into 5 mm circular pieces and stacked into hermetic DSC pans. Conventional DSC was performed using a heating/cooling rate of 10 K/min. Each sample was held at 100 °C for 5 min to remove any thermal history. DSC heating/cooling runs were performed for 3 cycles between −70 °C and 80 °C, and the glass transition temperatures , Differential Scanning Calorimetry (DSC), Small Amplitude Oscillatory Shear Rheology (SAOS) and tensile Dynamic Mechanical Analysis (DMA). Due to its lack of phase transitions across a wide derstood ,19. To bFor the quiescent LCEs, we demonstrate that both the isotropic and nematic LCE samples show a similar polymers ,24. Impoc phases ,46.For comparison, a change in the 2−1.6×Tg , which i systems . Thus, tThe extent of decoupling between the ionic conductivity and the structural ectively ,40. In ak chains ,86,87.Finally, to investigate the origin of molecular auxetic behavior in the nematic LCE, the effects of the applied strain are determined. The complex Young’s modulus,  network ,105,109. strain) , we sugg


To evaluate the predictive values of femoral proximal medullary morphology for the use of subtrochanteric osteotomy (STO) in unilateral Crowe IV developmental dysplasia of the hip (DDH).Ninety four patients with unilateral Crowe type IV DDH (59 hips in STO group and 35 hips in the non‐STO group) between April 2008 and June 2019 were enrolled. All patients underwent THA using the Pinnacle acetabular shell, ceramic liner and femoral head, the S‐ROM stem with proximal sleeve. Three parameters on the standard anteroposterior hip radiographs were measured: the widths of medullary canals at 20 mm above the center of lesser trochanter (CLT)，20 mm below the CLT and the isthmus. Canal flare index (CFI), metaphyseal canal flare index (MCFI), diaphyseal canal flare index (DCFI) were calculated. A S‐ROM femoral stem was used in all patients during total hip arthroplasty (THA).The CFI and DCFI in the STO group were lower than those in the non‐STO group. However, there was no statistical difference in MCFI between the two groups. The receiver operating characteristic (ROC) curves shown that DCFI had the highest area under the curve (AUC), at 0.885. This was followed by the CFI, which had an AUC of 0.847. The AUC of MCFI was 0.579. The optimal threshold for DCFI was 1.44, which lead to a sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of 0.771, 0.898, 0.869, and 0.818, respectively. For CFI, the optimal threshold was 3.28, resulting in a sensitivity, specificity, PPV, and NPV of 0.829, 0.729, 0.878, and 0.644, respectively.The DCFI and CFI may be potent indicators in predicting the use of STO in unilateral Crowe IV DDH. The optimal threshold for CFI and DCFI were 3.28 and 1.44 and had good sensitivity and specificity for predicting the use of STO during THA. The diaphyseal canal flare index and canal flare index may be potent indicators in predicting the use of subtrochanteric osteotomy in unilateral Crowe IV developmental dysplasia of the hip. Total hip arthroplasty (THA) with unilateral high dislocated hip is a technically demanding procedure in terms of preparation of the true acetabulum and reconstruction the equality of the leg lengthet al.et al.The shape of the femoral medullary canal is highly variable in high dislocated hipThe false acetabulum may be an influence factor in predicting the use of STO. The presence of a false acetabular stimulated the development of the proximal femur in Crowe IV DDH. Therefore, the femoral proximal medullary morphology may be a potent indicator in predicting the use of STO. The aims of this study were: (i) to investigate the canal flare index (CFI), metaphyseal canal flare index (MCFI) and diaphyseal canal flare index (DCFI) in unilateral Crowe type IV DDH on anteroposterior hip radiograph; (ii) to analyze the difference MCFI between the STO patients and the non‐STO patients; and (iii) to analyze the predictive values of the CFI, MCFI and DCFI for the use of STO.The inclusion criteria were: (i) adults patients with unilateral Crowe type IV DDH; (ii) patients who underwent THA by a single surgeon in our institution; (iii) patients divide into two groups based on whether STO or not during THA; (iv) outcomes were to analyze the predictive values of the CFI, MCFI and DCFI for the use of STO; and (v) retrospective study. The exclusion criteria included: (i) patients with a history of pelvis and hip surgery, severe hip osteoarthritis or DDH of contralateral side, residual DDH (infection and trauma), knee flexion deformity or severe knee osteoarthritis; (ii) patients with history of cerebral palsy and poliomyelitis; and (iii) patients with a proximal cone shaped sleeve at the femoral side.A retrospective analysis was performed of all patients with unilateral Crowe type IV DDH undergoing THA surgery in our hospital, between April 2008 and June 2019. A total of 94 patients with unilateral Crowe type IV DDH were investigated. The study protocol was approved by the institution review board of our hospital (S2020‐138‐01). Informed consent was obtained from all the patients enrolled in the study.The patients were divided into two groups according to the STO. In STO group, which consisted of 59 hips, the STO was performed during the THA. In non‐STO group, which consisted of 35 hips, the STO was not performed.All patients were performed by a single surgeon (Y.G. Zhou) under general anesthesia in the lateral decubitus position.The procedure had been described in detail in our previous studiesIn order to use ceramic on ceramic bearing, the shell (size range 44–46 mm) was implanted at the level of the true acetabulum by reaming acetabulum posteriorly and inferiorlyGluteus minimus and gluteal sling release, and iliopsoas tenotomy were performed. If hip reduction with a femoral trial stem was impossible, a STO would be performed for femoral shortening. The position of osteotomy was located at the distal end of sleeve. The osteotomy length equaled the distance between the true acetabular center and femoral head center during the trial reduction minus 15 mm.Prophylactic cerclage wires were placed both proximally and distally around the location of the osteotomy. A sleeve that was based on the proximal femoral intramedullary morphology was implanted. And a femoral stem was implanted based on the femoral intramedullary canal. The Pinnacle acetabular shell, Biolox delta ceramic on ceramic bearing, a S‐ROM femoral stem with proximal sleeve  were used in all patients Fig. .A standard anteroposterior hip radiograph was obtained preoperatively. All the radiographs were viewed and measured on a picture archiving and communication system . All radiographic measurements were analyzed twice, each by two investigators who had not participated in the surgery. The actual values for each measurement were obtained by calibration using the known diameter of the ceramic femoral head or marker for references. The following parameters were measured: The widths of medullary canals were measured at 20 mm above the center of lesser trochanter (CLT)，20 mm below the CLT and the isthmusCFI was calculated as the ratio of the widths of medullary canal at the level of the 20 mm above the CLT to the widths of medullary canal at the isthmusMCFI was calculated as the ratio of the widths of medullary canal at the level of 20 mm above to 20 mm below the CLTDCFI was calculated as the ratio of the widths of medullary canal at the level of the 20 mm below the CLT to the widths of medullary canal at the isthmusThe intertrochanteric distance was defined as the vertical distance from the apex of greater trochanter to the CLTt test or Mann–Whitney U test was used to compare continuous data between the STO and non‐STO groups. Receiver operating characteristic (ROC) curves were generated to determine the value of each measurement. The area under the curve (AUC) was calculated. The discriminatory value of curves was interpreted as excellent (0.9 to 1), good (0.8 to 0.89), fair (0.7 to 0.79), poor (0.6 to 0.69), or as failing or having no discriminatory capacity (0.5 to 0.59). All tests were performed using SPSS . A P‐value <0.05 was considered significant in all analysis. The figures were drawn by GraphPad Prism software .The intraclass correlation coefficient (ICC) was used to determine the variations of the different measurements. Categorical data were compared using a chi‐squared test. The independent‐samples P = 0.001) and there were no statistical differences in gender, age, and body mass index.The characteristics of the patients were shown in Table P < 0.001). However, there was no statistical difference in MCFI between the two groups (P = 0.204). The intertrochanter distances in STO group were higher than that in non‐STO group (P = 0.004) , and negative predictive value (NPV) of 0.771, 0.898, 0.869, and 0.818, respectively. For CFI, the optimal threshold was 3.28, resulting in a sensitivity, specificity, PPV, and NPV of 0.829, 0.729, 0.878, and 0.644, respectively.Both the intraobserver and the interobserver agreement were found to be nearly perfect for all of the measurements Table .This is the first study on the influence of femoral proximal morphology on STO in total hip arthroplasty for unilateral Crowe type IV DDH. In our study, we select four parameters, including CFI, MCFI, DCFI and intertrochanteric distance, to represent the femoral proximal morphology, and found the DCFI and CFI had good performance for predicting the use of STO in THA for Crowe type IV DDH.et al.et al.The anatomical abnormalities of proximal femurs caused by DDH make THA particularly difficult to perform. The intramedullary femoral canal had reduced mediolateral and anteroposterior dimensions in DDH patientset al.One of the difficulties during THA for Crowe type IV DDH is to safely reduce the hip to the true acetabulum and protect the neurovascular structureset al.et al.Therefore, there was controversy about the application of osteotomy during THA. And there was no study to discuss the influence of femoral proximal medullary morphology on STO in THA for unilateral Crowe IV DDH. In our study, the CFI and DCFI in the STO group were lower than those in non‐STO group. However, there was no statistical difference in MCFI. The proximal femur in STO group was stovepipe shaped, with a smaller CFI (2.8 ± 0.6), compared with that in non‐STO group (3.9 ± 0.9). The abnormality of femoral proximal medullary morphology in the two groups arise within the diaphysis between the 20 mm below the CLT and the isthmus. The ROC curves also shown CFI and DCFI had good performance for predicting the use of STO in THA for Crowe type IV DDH. The optimal threshold for CFI and DCFI was 3.28 and 1.44, respectively. And MCFI had poor predictive value. In another study, there was no significant difference in MCFI between the Crowe IVA  and the Crowe IVB  DDH. However, there was a significant difference in CFI and DCFI between the two Crowe IV DDHsThere are several limitations in this study. First, this was a retrospective study. Second, all measurements were based on the plain radiographs not the CT and three‐dimensional computer reconstruction models. Third, only one single femoral component  was used in all patients. The results may not be applicable to other types of femoral component. Finally, the long‐term results of this study may need further observation between the two groups.The current study reveals that the DCFI and CFI may be potent indicators in predicting the use of STO in unilateral Crowe IV DDH. The optimal threshold for CFI and DCFI was 3.28 and 1.44 and had good sensitivity and specificity for predicting the use of STO during THA.


GC-MS usually employsa 70 eV electron ionization (EI) ion source,which provides mass spectra with detailed fragment ion informationthat are amenable for library search and identification with namesand structures at the isomer level. However, conventional EI oftensuffers from low intensity or the absence of molecular ions, whichreduces detection and identification capabilities in analyses. Inan attempt to enhance the molecular ions, several softer ion sourcesare being used to supplement standard EI, including chemical ionization(CI), atmospheric pressure chemical ionization (APCI), field ionization(FI), photoionization (PI), and low electron energy EI. However, themost advantageous way to enhance molecular ions is to use cold EI,which employs 70 eV EI of cold molecules in supersonic molecular beams.Cold EI yields classical EI mass spectra with highly enhanced molecularions, which still provides high detectability and library-searchablemass spectra. In this paper, we explain and discuss why cold EI isnot a supplementary ion source to standard EI, but rather it is ahighly superior replacement to standard EI. With cold EI, there isno need for standard EI or any other supplemental ion source. We describe16 benefits and unique features of cold EI that not only yield betterresults for existing applications but also significantly extend therange of compounds and applications amenable for GC-MS analysis. Sample detection andidentification by GC-MS typically uses electronionization (EI) with sample identification based on an EI mass spectralibrary database search. This approach excels in providing matchingfactors and identification probabilities of compounds with their namesand structures including at the isomer level. However, such sampleidentification is also confronted by low intensity or the absenceof molecular ions in many EI mass spectra, which impedes the effectivenessof library-based identification and prevents the possibility of gettingelemental formula information. Thus, several softer ion sources arebeing used including chemical ionization (CI), atmospheric pressurechemical ionization (APCI), field ionization (FI), photoionization(PI), low electron energy EI, and EI of cold molecules in supersonicmolecular beams (cold EI). Among these “soft” ion sources,CI is the most popular, and it is commercially available as an optionalion source from most GC-MS vendors.2 it is reviewed in refs  desired, cold EI can also provide classical EI mass spectravia a mouse click mode of operation changing without any hardwarechange.However, we assert thatEI of cold molecules in supersonic molecularbeams (SMB), which we named cold EI, is far superior to all the knownionization approaches in GC-MS. Cold EI was initially developed in1990, in refs  and 4, aInterpretation of Mass Spectra.6 We note that the term “standard EI”is not well-defined since, while these ion sources use 70 eV electronenergy, the resulting EI mass spectra can be somewhat different dueto differences in the EI ion source structures and ion optics. Asan example, the Agilent Inert and Extractor ion sources are differentin their design from the Agilent high efficiency ion source (HES),and EI ion sources of TOF MS are different from those of quadrupoleMS in both ion source structure and ion optics. However, they allprovide about similar EI mass spectra that are compatible with library-basedsample identification and therefore are defined as “standardEI”. Cold EI also provides library-searchable mass spectra,usually with enhanced molecular ions, which actually helps to increasethe probability of accurate chemical identifications even if the matchingfactor may be slightly lower.70 eV EI became the standard ionization energy for gasphase compoundsin the early days of mass spectrometry as described in Fred Mclafferty’sclassic book In this paper, we describe andexplain why cold EI is far superiorto standard EI.8 The standard EI andlow eV EI experiments described below were performed with an Agilent7890 GC and 5977 MS using 70 or 14 eV electron energies and otherconditions as described in ref .This instrument consists of an Agilent 5977 MSD  combined with the Aviv Analytical supersonicmolecular beam (SMB) interface and its fly-through EI ion source forthe electron ionization of internally cold molecules in the SMB (hencethe name cold EI). We also developed the Tal-Aviv Molecule Identifier(TAMI) software that inverts the molecular ion isotope abundance patternsinto elemental formulas.d in ref .30H62 squalane hydrocarbon as obtained by cold EI (upperMS), photoionization (second MS), 14 eV EI (third MS), and 70 eV standardEI (bottom MS). The NIST identification probabilities are also includedfor cold EI and 70 eV standard EI. As demonstrated, neither 70 eVstandard EI nor “soft EI” at 14 eV exhibits any molecularion as their molecular ions abundances are both weaker than 0.01%.Photoionization provides a weak molecular ion (∼2% relativeabundance), whereas the molecular ion is the base MS peak in coldEI, and it also exhibits a clear isotopic pattern for M+1 and M+2. Furthermore, cold EI also provides a higherNIST identification probability of 78.3% compared with 37.4% of 70eV standard EI, while photoionization failed to identify squalaneby the NIST library. One reason why the PI mass spectrum failed tobe identified by the NIST library is that it misses the low mass m/z = 57, 71, 85, 99 characteristic hydrocarbonfragmentation pattern. In addition, cold EI clearly (visibly) exhibitsboth the low mass fragment ions as well as highly amplified isomericstructural mass spectral information.In m/z = 407 fragment ion, which indicatesa side methyl lossand m/z = 337 that is formed viaa loss of C6H13 radical which hints to the positionof another side methyl group, and similarly all other high mass fragmentions are structurally informative. We note that the 14 eV low eV massspectrum is very similar to the 70 eV standard EI mass spectrum, andonly minor differences in the relative intensities of the low massfragment ions are exhibited. The reason for this observation is thatthe molecular ion fragmentation is dominated by the large internalthermal energy of C30H62. Thus, despite someclaims to the contrary, low eV EI  is not a “soft”ionization approach, especially in contrast to cold EI which yieldsa molecular ion abundance 10 000 times greater while stillproviding full mass spectral fragment ions information and compatibilitywith standard EI library-based identification. Accordingly, in cold EI, we can observe the Amongthe “soft” ion sources, CI is by far the mostcommon, largely because it is sold by all the major GC-MS providers.Unlike cold EI, the use of CI: (1) requires time-consuming ventingand physical ion source replacement; (2) adds to the overall instrumentcost, including the need for a CI gas such as methane and its relatedsafety issues; (3) yields a signal that is typically 100-fold lowerthan of standard EI; (4) it is ineffective with some compounds suchas hydrocarbons; (5) provides mass spectra that are incompatible withEI-MS library-based identification; (6) its range of compounds amenablefor analysis is reduced due to extended CI ion source peak tailingand degradation; (7) often provides distorted molecular ion isotopeabundances, which reduces the possibility of identification basedon obtaining an elemental formula with unit mass resolution quadrupoleMS instruments. Thus, despite the clear need for having trustworthymolecular ions, CI is not widely (rarely) used.(1)Molecularion enhancement. Cold EI provides enhanced molecular ions forthe broadest rangeof compounds.9 The degree of molecularion enhancement can be minimal for small molecules, while it can beunexpectedly large for large molecules, often exceeding enhancementfactors of 1000 for hydrocarbons with the carbon number above C36H74 or for branched isomers greater than C30H62 as shown in (2)Particular importance for largecompounds. The molecular ion abundances strongly decline withmass in standard EI, and for hydrocarbons they decline by 20% foreach added carbon atom, while in cold EI the high molecular ion abundanceis about size independent.5 The reasonfor this observation in standard EI is that the intramolecular vibrationalenergy heat capacity increases about linearly with the number of atomsin the sample compound. Thus, while the molecular ion enhancementin cold EI for small molecules may be minimal, it can exceed a factorof 1000 for C36H74 or bigger hydrocarbons. Incontrast, CI and/or APCI are ineffective for the analysis of hydrocarbons,and as already described9 low eV EI does not enhancethe molecular ions of large hydrocarbons.(3)The most trustworthy molecularions. Cold EI eliminates vacuum background and has no adductions, and it reduces column bleed and ghost peaks via enabling lowerelution temperatures at high column flow rates. Thus, cold EI makesit much easier to visually identify the molecular ions compared tostandard EI, low eV EI, PI, CI, and/or APCI. Accordingly, cold EIprovides the highest ratio of molecular ions to higher mass noiseions, and thus the cold EI molecular ions are the most trustworthy.5(4)Compatibilitywith NIST libraryidentification. Cold EI provides both enhanced molecular ionand library-searchable fragment ions, and thus it is the only“soft” ionization method that is compatible with NISTlibrary identification. While the molecular ion is enhanced,cold EI also results in the formation of fragment ions that are formedfrom doubly charged molecular ion dissociation into nonstatisticalfragment ions. These fragment ions are not present at low electronenergies (low eV EI).(5)Improved NIST library identification. The molecularion provides the most characteristic informationabout the analyzed sample compound, and by enhancing the intensityof the molecular ion, cold EI actually improves the NIST library identificationprobabilities. Knowledge of the sample compound mass also leads toa better ability to reject incorrect identifications. This aspect,including computer simulations of the improvement of library identificationprobabilities with enhanced molecular ions, has been described previouslyin detail.11(6)NIST library identificationconfirmation via availability of molecular ions. Trustworthymolecular ions provide an independent confirmation to the NIST libraryidentification regardless of its provided matching factors and identificationprobabilities. Without having trustworthy molecular ions, the libraryidentification often fails and/or cannot be trusted. Accordingly,with cold EI, we always look at the molecular ion to see if it isthe same as in the NIST library #1 compound in its list.(7)Provision of elemental formula. The cold EI enhancement of the molecular ions combined with exhibitingaccurate isotope abundances enables the effective use of the Tal-AvivMolecule Identifier (TAMI) software with unit resolution quadrupoleMS data for the conversion of isotope abundances into the elementalformula.8 We note that in CI and/or APCI, the isotopeabundances could be distorted due to incomplete proton transfer. Inaddition, cold EI enables the analysis of extended range of compoundsfrom which greater portions are not in the library and thus requirehaving the elemental formula as the best form of tentative identification.(8)Extended range ofcompoundsamenable for analysis. Cold EI enables a significantly greaterrange of compounds amenable for analysis, including low volatility,polar, and those that require derivatization.12 Thisfeature is achieved via the possible use of short columns with highercarrier gas flow rates (up to 100 mL/min) that result in lower elutiontemperatures from the injector to the column and from the column tothe SMB, while ion source-related degradation is fully eliminatedin cold EI via its fly-through ion source.12 CI for example has a closed ion source configuration(compared with standard EI) to increase the internal CI gas pressure,and thus it exhibits an early onset of ion source-related peak tailingand as a result it is prone to a smaller range of compounds amenablefor analysis. We view this extended range benefit of coldEI as its most important feature as it results in largelyextended range of cold EI applications that bridges the gap betweenGC-MS and LC-MS.(9)One ion source for all typesof analyses. Cold EI can serve for the full range of all GC-MSwith standard EI applications, including additional applications thatare unique to cold EI. Thus, with cold EI there is no need to haveany other ion source, while in contrast most other “soft”ion sources are supplementary and optional to standard EI.5(10)Fast ColdEI-Classical EI ionsources mode changing. In the rare case of a desire to haveclassical EI mass spectra, the cold EI ion source enables its replacementto “standard EI” like an ion source with a simple methodchange that results in the reduction of the cooling helium makeupgas flow rate (combined with column flow rate) from 60 mL/min to 5mL/min. This change can be achieved even in the middle of an analysisrun.4 CI, PI, or FI typically require ventingand manual ion source replacement which adds to the cost; plus, CIrequires additional CI gas and its safety requirements, while APCIcannot be replaced with standard EI. Low eV EI is ineffective forcompounds which do not exhibit molecular ions.(11)Cold EI improves MS-MS. The molecular ions are the most selective ions against matrix interferencewhich is reduced with mass by about a factor of 10 per 100 u.13 Furthermore, the product ions that are producedfrom the molecular ion CID also typically have a higher mass, andthus MS-MS on the molecular ion can be 100 times more selective thanMS-MS on a fragment ion. Accordingly, GC-MS-MS with cold EI is muchmore sensitive in complex matrix analysis than with standard EI, inanalogy to MS-MS with electrospray LC-MS. While this improved selectivityis also true for other “soft” ion sources, cold EI providesgreater instrumental sensitivity with MS-MS than any other “soft”ion source.14 This feature and the abilityto extend the range of compounds amenable for GC-MS make cold EI idealfor GC-MS-MS.(12)Ion source-related peak tailingand degradation. Cold EI involves the ionization of cold moleculesduring their flight path through a contact-free fly-through ion source.Thus, any ion source-related degradation or peak tailing is fullyand inherently eliminated. On the other hand, peak tailing and ionsource degradation adversely affect standard EI, low eV EI, CI, andPI. Thus, compounds with OH, NH, or COOH often require derivatizationfor their analysis by GC-MS and otherwise may exhibit a nonlinearresponse and high limit of detection.15 In CI, the closed ion source causes both peak tailing and degradationto be further promoted and amplified.(13)Response uniformity.Cold EI is unique in exhibiting an approximately uniform compoundindependent response16 that enables semi-quantificationwithout external calibration. Standard EI exhibits a uniform responseto small molecules, but it loses this feature for large compoundsdue to ion source-related peak tailing. Analytes with OH, COOH, orNH groups also need external calibration due to ion source-relatedpeak tailing and degradation that also result in nonlinear response.Because of its closed ion source structure, CI is worse than standardEI in this respect, while low eV EI is worse than 70 eV standard EIdue to its electron energy being close to the compound-dependent ionizationpotentials.(14)Superior sensitivity. The cold EI molecular ions in largecompounds provide superiorsensitivity in reconstructed single ion monitoring (RSIM) than standardEI in view of both a higher signal and lower vacuum background andcolumn bleed noise.5 The lower column bleed is dueto lower elution temperatures in cold EI analysis at higher columnflow rates.12 The cold EI signal (TIC andmolecular ions) is far greater than that of CI, FI, PI, or low eVEI. CI for example provides a 10–1000 times weaker signal than70 eV standard EI, depending on the analyte proton affinity. For example,S/N specifications of the Agilent 5977 MSD call for EI to be 125-foldmore sensitive than CI. While cold EI S/N can be equivalent to thatof 70 eV standard EI with OFN (cold EI S/N for 1 pg OFN at m/z = 272 is >10+6 due tozeronoise and its LOD in SIM is 0.2–0.4 fg), it is superior withcold EI for large compounds, and the greater the difficulty in standardEI analysis, the greater is the gain in cold EI S/N. For example,cold EI can provide a factor of 1000 greater S/N in single ion monitoringof the molecular ions for cholesterol and n-C32H66 in comparison with the Agilent high efficiencyion source (HES).5 Furthermore, many compounds canbe effectively ionized by cold EI, while they are not amenable foranalysis by standard EI.12(15)Improved isomer identification. The combination of enhanced molecular ions and high mass fragmentions provides extended structural and isomer information, as demonstratedin (16)Isomer distributionanalysis. Enhanced molecular ions enable the unique methodof isomer distributionanalysis for fuels and oils characterization and optimization.17 This is a potentially very important applicationthat requires having abundant molecular ions for branched hydrocarbonisomers, which is not shared by standard EI, low eV EI, and PI, asdemonstrated in GC-MS ion sourcesare characterized by several features, most ofwhich are rarely discussed or compared. Sixteen important featuresare described in the list below, including important yet sometimesunexpected benefits of enhanced molecular ions in cold EI that makeit the best and most effective GC and MS interface and ion source:Consequently, GC-MS with cold EI issuperior in sample identificationand quantification versus GC-MS with any other type of ionizationmethod and ion source. Thus, cold EI does not serve to merely complementstandard EI, but rather, it should be used to replace standard EIas a far superior ion source and GC-MS interface.We view thecold EI feature of extended range of compounds andapplications amenable for analysis as its most important feature thatbridges the gap between GC-MS and LC-MS, while having a trustworthyenhanced molecular ion is also a highly important benefit of coldEI. Thus, cold EI can lead the way to the future of GC-MS, and withit there is no need for any additional ion source, while all the centralGC-MS performance features are improved.


This study explores methodologies for the data integration of antimicrobial use (AMU) and antimicrobial resistance (AMR) results within and across three food animal species, surveyed at the farm-level by the Canadian Integrated Program for Antimicrobial Resistance Surveillance (CIPARS). The approach builds upon existing CIPARS methodology and principles from other AMU and AMR surveillance systems. Species level data integration involved: (1) standard CIPARS descriptive and temporal analysis of AMU/AMR, (2) synthesis of results, (3) selection of AMU and AMR outcomes for integration, (4) selection of candidate AMU indicators to enable comparisons of AMU levels between species and simultaneous assessment of AMU and AMR trends, (5) exploration of analytic options for studying associations between AMU and AMR, and (6) interpretation and visualization. The multi-species integration was also completed using the above approach. In addition, summarized reporting of internationally-recognized indicators of AMR  and AMU  is explored. It is envisaged that this approach for species and multi-species AMU–AMR data integration will be applied to the annual CIPARS farm-level data and progressively developed over time to inform AMU–AMR integrated surveillance best practices for further enhancement of AMU stewardship actions. Disease mitigation strategies, such as infection prevention and control (IPC), good management practices (GMP) and enhanced disease detection, are utilized in Canadian food animal species, including broiler chickens, grower-finisher (GF) pigs and turkeys. Pertinent species-specific diseases are monitored through provincial and regional networks of private practitioners, animal disease diagnostic laboratories and government partners ,2,3,4. F100 and TI1000], DDDvet per PCU and other derivatives of these indicators  are also utilized by regional and national AMU monitoring systems and in AMU research , nDDDvetCA/PCU and nDDDvetCA/kg animal biomass (pcc = 0.83 to 0.99). These results indicated alignment of the exploratory AMU indicators with those routinely used by CIPARS for reporting and research.Two of the candidate AMU indicators were exploratory to CIPARS, the weight-based mg/kg animal biomass and the dose-based nDDDvetCA/kg animal biomass, which are derivatives of the mg/PCU and nDDDvetCA/PCU, respectively. To assess their relevance and ensure that these aligned with AMU indicators currently used by CIPARS, the pairwise correlation matrix was expanded from the previous constructed matrix  to incluComparability of the AMU unit of measurement (metrics) between populations is a requirement if the intent of the study is to compare AMU levels between species . Althoug1000), or the alternate indicator, TI100, are unavailable. Ultimately, for our study, the nDDDvetCA/kg animal biomass was selected for other study objectives  and was preferred over nDDDvetCA/PCU to reduce the analytic burden of obtaining denominator data . Furthermore, slaughter live weights  could also be conveniently obtained from processing plant records. In terms of communication to stakeholders, the kg animal biomass better aligns with other farm level production or economic parameters . Additionally, this approach could be useful for estimation of dose-based AMU indicators in other food animal species where input parameters, such as weight at treatment, may not be easily accessed  or may vary a great deal over the feeding period. In the Netherlands, a derivative of this indicator using annualized data (DDDA/Year) is used to compare the trends and levels of AMU across all the animal species in the country [All of the AMU indicators examined established that GF pigs had the highest mean value, except in the nDDDvetCA/1000 animal-days at risk C, where  country ,59 and t country . 100 [Multiple AMU indicators have been utilized for investigating the relationship between AMU and AMR, including the weight-based indicators, mg/kg animal biomass ,60, mg/P100 ,63,64. Cp values, referred to as effect estimates from this point forward). The model used is described in For this exercise, two homologous AMU–AMR pairs were used as examples A,B. A hop values (from 0.002 to 0.005), although the effect estimates substantially differed depending on the AMU indicator. For example, the nDDDvetCA TET/kg animal biomass and nDDDvetCA TET/PCU yielded higher OR with wider 95% CI’s, whereas the remaining indicators, nDDDvetCA TET/1000 animal-days at risk, mg TET/kg animal biomass and mg TET/PCU had comparable OR’s (1.02 to 1.05). Similarly  between weight based, and high pcc between dose-based AMUindicators.(1)and trends) between species:For simultaneous evaluation of AMU levels (Recommended: unit of measurement/scomparable between species (11) *; dose-based (11) **.Assessment: Descriptive statistics by species .Candidate AMU indicators:1)nDDDvetCA/kg animal biomass2)nDDDvetCA/PCU(2)For evaluating relationships between AMU and AMRRecommended: unit of measurement reflective of the level and duration of exposure; dose-based.Assessment: mixed effect logistic regression model, evaluation of effect estimates  by AMU indicator across species and forest plots visualization.Candidate AMU indicators:1)nDDDvetCA/kg animal biomass2)DDDvetCA/PCU(3)nDDDvetCA/1000 animal-days at riskOption selected for 1 and 2: nDDDvetCA/kg animal biomass(a)Reduced analytic burden in cases where inputparameters such as the days at risk and average treatment weight (Option 3)are unavailable.(b)The denominator, average pre-slaughter weight, can beaccessed from farm records or production summary reports. Alternatedenominator such as processing weight and final birds/pigs slaughtered can beobtained from processing plant records.(c)Preferred for ease of communicationwith stakeholders .Other considerations for using this AMU indicator:*adjusts for number of animals treated and weight ofanimals.**adjusts for average daily dose per kg per day.E. coli and % resistance in Campylobacter, depending on the AMU–AMR pairs indicated in Overview: This section describes the integration of AMR and the quantitative AMU indicator selected from the exercises in In recent years, various research studies have explored the association between AMU and AMR. While the binary AMR measurements, % phenotypic resistance, were relatively common for the AMR component, various AMU data structures were used across different studies, for example, the use of binary AMU data ,67,68,69Option 1. Generalized linear model. The outcome of interest was the flock or herd level AMR for the antimicrobial and organism of interest. The strength of the association was evaluated using a generalized linear model , with the flock- or herd-level AMR as the outcome variable , and the flock- or herd-level nDDDvetCA/kg animal biomass as the continuous predictor variable. The models were fitted for each animal species and adjusted for year .Option 2. Mixed effect logistic regression analysis. The second approach used the isolate-level AMR data matched with the corresponding flock or herd level AMU indicator, nDDDvetCA/kg animal biomass, as above. The models were fit for each animal species. For these data combinations, the strength of association was determined using mixed effect logistic regression  adjusted for year, with a random intercept to account for the correlation of resistance within the same flock or herd.p values) were noted for each model and a p ≤ 0.05 was considered significant. An OR > 1 and an OR < 1 meant that AMR was positively or negatively associated with AMU, respectively. As an example, in the model using susceptible isolates as the outcome variable, an OR < 1 indicated that the probability of detecting isolates exhibiting susceptibility to all antimicrobials tested increased with lower AMU. For the broiler chicken data, all eight AMU–AMR pairs were modelled, but model convergence and unreliably wide 95% CI ranges were not achieved for some AMU–AMR pairs using the two modelling options above . In succeeding species models, AMU–AMR pairs that had AMU values ≤0.1 nDDDvetCA/kg animal biomass or that had AMR levels categorized as rare or very low were not determined. Effect estimates , forest plots were constructed . It is iBoth models have utility for further investigation of aspects of AMU exposures and farm-level factors that potentially have an impact on AMR, since the models could be expanded to include other risk factors for AMR. Mixed effects logistic regression was used for further analyses. In the future, inclusion of potentially relevant variables in the mixed effect logistic regression model, such as veterinary practices  and geographical locations . As in other integrated surveillance systems , the metThe strength of the CIPARS farm-level data is that both AMU and AMR data components were collected annually from the same epidemiological unit. Therefore, the isolates are more reflective of the actual farm-level AMU exposures, which is an advantage compared to other national surveillance programs. The data collection design  is appropriate for measuring both the selection and spread of AMR . AdditioBox 2.(1)Simple logistic regression model: AMR percentages, count-based AMU and animal health indicatorsNonparametric, Wilcoxon rank sum test: quantitative AMU indicators.Visual inspection, descriptive analysis of the percentages and directionality of the shift in AMU and AMR, followed by temporal analysis(2)public health importanceanimal health significancerelevance to broader AMU stewardship and monitoring of AMU reduction efforts or other interventionsIdentify the AMU-AMR pairs:(3)mixed effect logistic regression approachDetermine the strength of AMU-AMR association (4)Visualize the trends in AMU and AMR.(5)are trends inreported disease syndromes reflective of the trends in AMU?).Examine animal health data for context ), enteric (Clostridium perfringens and Eimeria spp.), respiratory (APEC) and miscellaneous bacterial diseases  regarding species-specific disease syndromes and etiologic agents, where responses that were ‘likely positive’ or ‘confirmed positive’ were considered flock or herd positive for that disease syndrome or etiologic agent. Temporal changes in the percentages of flocks or herds that experienced any of these syndromes, and utilized preventive animal health programs  that may play a role in these syndromes, were determined to be similar to the AMU count-based indicator and AMR temporal analysis described in previous sections. The rationale for the selection of certain disease syndrome/etiologic agents was based on conditions that are commonly occurring in Canadian flocks and herds and that have economic significance due to the potential for high morbidity and mortality or reduction in production performance parameters, such as weight gain, feed conversion or cost of therapeutic AMU. The approach to disease syndrome/etiologic agents to be included has also been noted elsewhere, for example, a study in Europe that noted the impact of enteric diseases on flock performance . For simdiseases . In GF pncerning . A more AMU indicator/s to use: there is no single AMU indicator that can encompass every analysis objective, in particular for comparison of AMU between species  and for Animal health context: the syndromic data collected from CIPARS farms also helps provide insights on the potential implications of changing AMU practices on the health of flocks/herds. More detailed analysis could be conducted in the future to assess the impact of the shifts in AMU on mortality and flock or herd production performance parameters, as well as the susceptibility profiles of the clinical pathogens.AMU–AMR associations: the analytic options presented here  were appropriate for use in routine annual analysis and reporting (adjustments to various levels of clustering such as within flocks/herds). It is important to note that there are many analytic methodologies described elsewhere, ranging from simple logistic regression analysis to more advanced methodologies where other relevant variables could also be included in the model . Emerging methodologies could be continuously explored to refine the approach used here.For this paper, summarized reporting pertains to the use of aggregated AMU and AMR data from the three species included in this study. The intent of the exercises conducted in this section was to provide an overview of the current AMR and AMU situation in the animal sectors combined. Relevant analytic procedures determined from E. coli and Campylobacter as summarized in Overview: The AMR levels across the three species by bacterial organism  was used to detect temporal changes in overall % resistance.% Resistance, multispecies: Overall % resistance in Animal biomass adjusted % resistance: The summarized AMR indicator developed in the EU/EEA, known as the AMR Indicator Index or Key Outcome Indicator ,46,70, wBrY-% resistance (or susceptible) in broiler chickens (GFPigsY-% resistance (or susceptible) in grower-finisher pigs ; calculated per year from 2015 to 2019. kg GF pigsY-kg grower-finisher pigs’ biomass ; calculated per year from 2015 to 2019. kg TurkeysY-kg turkey biomass ; calculated per year from 2015 to 2019. kg animal biomassTotalY-combined animal biomass from the three species.Equation (2). AMR weighted by species-specific percentage of resistant isolates and biomasschickens ; calculaher pigs ; calcula turkeys ; calculaThese exercises, exploring the utility of the two measures of AMR indicators and visual inspection, indicated that the directionality of the trends was similar regardless of the method used ; calculated per year from 2015 to 2019.Equation (3). Summarized  mg antimicrobials weighted by overall PCU.GFpigsY-total milligrams active ingredients in GF pigs; calculated per year from 2015 to 2019. mgTkY-total milligrams active ingredients in turkeys; calculated per year from 2016 to 2019. kg animal biomassTotalY–the annual kg animal biomass, the population at risk multiplied by the actual average live slaughter weights; calculated per year from 2015 to 2019.Equation (4) Summarized  mg antimicrobials weighted by overall kg animal biomassThe above measurements (Equations (3) and (4)) are logical choices for comparison with national sales and distribution data and other surveillance systems  that are using these indicators for annual reporting. It is important to note that these estimates pertain to the three species studied only, and other relevant food animal species, including beef, dairy, layers and minor species, were not yet included. The multispecies analysis to study AMU–AMR linkages followed the approach used in AMU: in terms of comprehensiveness, the data included three food animal species, but other important commodities in Canada, such as beef and dairy cattle and chicken egg layers, were not included, as these data were not yet available at the time of the study.AMR: data were limited to Gram-negative indicator organisms for many of the analyses. Also, AMR of clinical pathogen data were not available.AMU–AMR analysis: other models  evaluating the full range of potential risk factors for AMR by species were also not completed as they were beyond the scope of this study; however, they could be explored in future research.Animal health: the addition of other production performance parameters that could substantiate stewardship efforts, such as feed conversion rates and final kg of meat produced (slaughter data), are not collected by CIPARS, but will be useful to include to provide a full understanding of the impact of AMU reduction efforts in the health, welfare and sustainability of the food animal sectors in Canada.This paper explored two levels of AMU–AMR data evaluation, the species level and multispecies level, discussed the approach for analysis for each of the surveillance data components and highlighted the utility of syndromic data for providing context to the AMU trends; therefore, this study demonstrated how surveillance data could be optimized for informing surveillance best practices and AMU stewardship. It also emphasized that high resolution farm-level AMU data collection enables a surveillance program to adapt to rapidly evolving AMU surveillance methodology development, to study associations between AMU and AMR and to provide useful context for interpreting trends in AMU and AMR, thereby supporting antimicrobial stewardship activities and informing further research. Antimicrobial resistance surveillance in clinical pathogens, which is a component of other surveillance systems remains a gap; inclusion of this component in the CIPARS farm surveillance program will complement the animal health data collected.


A diverse biomedical workforce is essential to achieve excellence in patient care, clinical translational, and basic research. Diversity, equity, and inclusion challenges in cancer molecular represent a combination of the challenges facing the science, technology, engineering, and mathematics (STEM) field, and challenges in Radiology and Nuclear Medicine. Although there is a growing awareness of conscious and unconscious bias that negatively affect the cancer imaging world, many challenges remain such as overcoming barriers to entry into the pipeline, avoiding program dropout, and providing long-term career prospect. The COVID-19 pandemic has resulted in a significant setback and further highlighted problems faced by women and underrepresented minorities. In this perspective, we have identified some of the challenges faced and highlighted ongoing and future initiatives to address these challenges. As a consequence, building a diverse biomedical workforce is essential to excellence in patient care, clinical translational, and basic research  field, and challenges in Radiology and Nuclear Medicine.https://doi.org/10.1148/radiol.13130101). Thus, there is an urgent need for Radiology to address its dire underrepresentation of female and racial/ethnic representation in both preclinical molecular imaging research and clinical-translational molecular imaging. Overcoming barriers to entry into the pipeline, avoiding program dropout and providing long-term career prospects are all important challenges towards the goal of successful careers of those who are underrepresented in the field. The COVID-19 pandemic has presented additional barriers and fractures in our work-force, and further highlighted problems faced by women and URM. While the switch to virtual academic interactions, remote teaching and training, and virtual radiology, has allowed more flexibility, overall the disruption to childcare and school routines has significantly negatively impacted women and URM. As discussed in a recent study  representation  . The proUnderrepresented minorities in Radiology, especially black and hispanic people, are used to being the “only” in their cohort, program, or department. Black and hispanic people remain starkly underrepresented in almost all STEM graduate programs, and this is only exacerbated as we consider postdoctoral fellows, faculty, and leadership roles. In addition to the general challenges that face all trainees in the fast-paced and often competitive environment of academia, this lack of representation and visibility can often leave URM students and other marginalized groups feeling like this space is not meant for them. Unilateral hierarchies represent the root cause for many acts of microaggression and disparities reported by racial/ethnic URM in STEM fields. Therefore, increasing the quota/proportion of underrepresented minorities alone is not enough. We need to increase the representation of qualified underrepresented minorities in leadership roles in order to ensure that every team member has an advocate at the leadership table, when decisions are being made. Introducing powerful advocates for everyone will reduce the risk of discrimination and harassment at the workplace and provide our students and junior faculty with a diverse set of role models.The infrastructure to meet these challenges should combine top-down and grass roots approaches to increasing diversity and inclusion in the STEM and cancer molecular imaging fields. Programs that identify talented candidates early on, and continue to support them throughout their careers, would increase the success of women and URM, and build a cadre of leaders that would serve as role models for future generations. Financial support for such programs through philanthropy or other funding mechanisms is critically important.Department leaders, scientific societies, and programs are recognizing the importance considering and including qualified women and underrepresented minorities in leadership training programs, committees, and consideration for awards and honors. The American College of Radiology (ACR) has established a Commission for Women and Diversity with the intent to celebrate diversity and actively promote inclusion at all levels of training, practice and leadership. The Society of Nuclear Medicine and Molecular Imaging (SNMMI) has established a Diversity, Equity and Inclusion Task Force with the goal to develop strategies to make all people feel that they belong to and can bring their true authentic self to their work place. These efforts are important, but should be expanded. Opportunities should be aggressively advertised to women and underrepresented minorities through appropriate venues, as in some instances, there may be an absence of awareness of these opportunities. There should be expanded training to raise awareness of conscious and unconscious bias. Biases, when these are identified, should be directly confronted and addressed . At the Training in clinical molecular imaging provides a broad foundation for a wide range of career opportunities, ranging from Radiology faculty positions, research and management positions, radiation physicist positions and careers in computer sciences at Universities and the industry. Thus, our field is uniquely poised to impact diversity in STEM through pipeline programs. While graduate students and postdoctoral fellowship candidates can apply to a variety of training programs, none of these programs specifically address challenges and disparities experienced by trainees from URM backgrounds. Many R1 Universities receive more than 500 applications every year for less than 10 training spots provided by existing training programs. While the selection committees for these programs carefully review every application and consider trainees from all backgrounds, it is impossible to move the needle in terms of URM representation through these programs alone. The marked underrepresentation of Black and Hispanic students specifically would require influx of these students into the academic system at a higher rate. Adding training spots to existing training programs, perhaps through philanthropic initiatives, could impact the current underrepresentation of URM students in the field of molecular imaging more effectively by enabling an increased influx of URM trainees into our discipline. This increased number of URM trainees would lead to an increased availability of qualified candidates for faculty positions, and ultimately, increased representation of URM among Radiology faculty.The imposter syndrome is experienced by high-achieving individuals who doubt their achievements. In a recent study conducted by our team at Stanford , we founDespite substantial monetary investments in gender and racial/ethnic equality programs, large proportions of minority faculty members drop out at the early and mid-career stage . Daily mvia reduced paylines or dedicated funding opportunities that are restricted to faculty at junior ranks. While specific support for junior faculty is certainly appreciated, many junior faculty find themselves stranded at the mid-career level, when these funding sources are not available to them anymore. This often leads to a dropout of highly qualified personnel after 6- or 7-figure investments by their institutions and/or tax payers. It is an unspoken reality that many academic faculty nationwide spend the vast majority of their time writing grants. This cannot be in the interest of medical innovation and discovery. As a community, we have to address this problem: We have to expose research-interested academic radiologists to a range of career options at an early career stage so that the field can leverage their skills rather than seed-funding blind ending careers. For clinical molecular imaging researchers with an interest in clinical-translational research, more long-term funding opportunities should be provided such that researchers can truly focus on research and discovery, without constant distractions by grant-writing duties and fears about their short or long-term job security. While this applies to all researchers, long-term career security is especially important for researchers from financially underserved communities. Members of financially affluent backgrounds may be more open to taking risks with regards to long-term financial insecurity. On the other hand, members from financially disadvantaged communities may put a higher emphasis on financial security, thereby excluding academic research jobs as viable career options.Many initiatives provide dedicated funding to support research efforts of junior faculty vice versa. In other words: Does promotion to a leadership role lead to increased productivity as a result of increased access to resources, which in turn is rewarded with further promotions. The composite of individual psychological experience of bias and lack of clearly defined promotion metrics, combined with lack of individualized support and guidance feeds this overall issue. Initiatives at a nationwide level should explore the efficacy of interventions that help to mitigate this problem. Likely individualized sponsorship would help greatly.Providing adequate resources and time for both clinical and research work is essential to maximize the value and excellence of clinical cancer molecular imaging. Our data at Stanford showed, in accordance with others, that female and racial/ethnic minority faculty reported less access to resources compared to male faculty . For exaAn inclusive world with scientists and clinicians that represent all spectrums of our society is one of the most worthwhile and important goals of our century. The COVID-19 pandemic has clearly posed additional challenges in attaining this goal. These challenges have served to highlight the importance of global co-operation and inclusion, and our resilience as we work towards a better world. Academic and research institutions, medical and academic societies, medical practices have all had to adjust to the disruptive forces of the pandemic to bridge distances using virtual interactions for outreach, teaching and training. These virtual capabilities and tools can be exploited in molecular imaging to break down barriers to inclusion, and to access high level specialized education, so that opportunities can become equal for everyone, and for the field of clinical and research molecular imaging to benefit from a diverse, vibrant, global community.The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.All authors have equally contributed to this perspective. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.