Self-management and remaining physically active are first-line recommendations for the care of patients with low back pain (LBP). With a lifetime prevalence of up to 85%, novel approaches to support behavioural self-management are needed. Internet interventions may provide accessible support for self-management of LBP in primary care. The aim of this randomised controlled trial is to determine the clinical and cost-effectiveness of the ‘SupportBack’ internet intervention, with or without physiotherapist telephone support in reducing LBP-related disability in primary care patients.A three-parallel arm, multicentre randomised controlled trial will compare three arms: (1) usual primary care for LBP; (2) usual primary care for LBP and an internet intervention; (3) usual primary care for LBP and an internet intervention with additional physiotherapist telephone support. Patients with current LBP and no indicators of serious spinal pathology are identified and invited via general practice list searches and mailouts or opportunistic recruitment following LBP consultations. Participants undergo a secondary screen for possible serious spinal pathology and are then asked to complete baseline measures online after which they are randomised to an intervention arm. Follow-ups occur at 6 weeks, 3, 6 and 12 months. The primary outcome is physical function (using the Roland and Morris Disability Questionnaire) over 12 months (repeated measures design). Secondary outcomes include pain intensity, troublesome days in pain over the last month, pain self-efficacy, catastrophising, kinesophobia, health-related quality of life and cost-related measures for a full health economic analysis. A full mixed-methods process evaluation will be conducted.This trial has been approved by a National Health Service Research Ethics Committee (REC Ref: 18/SC/0388). Results will be disseminated through peer-reviewed journals, conferences, communication with practices and patient groups. Patient representatives will support the implementation of our full dissemination strategy.ISRCTN14736486. The SupportBack 2 trial is a large multicentre randomised trial that will determine the additional benefit, over usual primary care, of an internet-based approach that supports self-management of patients with low back pain (LBP) in UK primary care.The trial is designed to investigate the effectiveness of an internet intervention in addition to usual primary care, both with and without telephone physiotherapist support.A full mixed-methods process evaluation will be carried out to inform a logic model and ‘theory of change’ for the interventions.Inclusion is limited to those with LBP who have access to the internet and are able to communicate in English without assistance.Low back pain (LBP) has a lifetime prevalence of up to 85%Internet interventions are typically automated, interactive, tailored interventions that make use of multimedia formats to deliver behavioural change strategies online.et alSupportBack is an internet intervention designed to support patients to self-manage their LBP following consultation in primary care.The aim of the present full randomised controlled trial (RCT) is to determine the clinical and cost-effectiveness of the SupportBack internet intervention, delivered in addition to usual care with and without physiotherapist telephone support, in reducing LBP-related physical disability in UK primary care.A three-parallel arm (1:1:1), multicentre RCT is being conducted to determine the clinical and cost-effectiveness of an internet intervention for patients with LBP in primary care. Participants will be followed up at 6 weeks, 3, 6 and 12 months.The trial is being carried out with patients from 140 to 180 general practices across the UK. Patients access the intervention through their own devices with internet access  at a location that is convenient for them . If allocated to receive telephone physiotherapist support, this support is delivered wherever is convenient for the patient. A list of patient identification centres is available from the trial team on request.Aged 18 and above.Current LBP (have experienced pain in the last week) with or without sciatica.Access to the internet and an active email address.Ability to read/understand English without assistance.Ability to provide informed consent.Signs and symptoms in a patient with LBP that indicate potential serious spinal pathology such as infection, malignancy, fracture, inflammatory back pain, progressive neurology and/or cauda equina.Have had spinal surgery in the past 6 months.Pregnancy.Taken part in the prior SupportBack feasibility study.Two recruiting centres, Southampton and Keele (each with a team of telephone support physiotherapists) are working with National Institute for Health Research Clinical Research Networks to facilitate the recruitment of general practices. Potentially eligible participants will be identified in one of two ways:Patients who have consulted with LBP in the last 2 months will be identified by general practice staff from computerised records of consultations. Practices will be asked to repeat the searches approximately three times, or until the target number of patients per practice has been reached. Resulting lists of patients identified by the search will be screened by a practice GP who will rule out patients based on aspects of the eligibility criteria that can be determined from patient notes.During a patient consultation and on entering a relevant diagnostic or symptom Read code into the patient electronic medical record, GPs will be prompted about the trial and patient eligibility by an automated ‘pop-up’ screen activated by the Read code. GPs will then screen for eligibility (using the inclusion/exclusion criteria listed) and patients identified as suitable will have their medical record electronically tagged. A download of ‘tagged’ patients will occur regularly, anticipated to be every 2 weeks. This method will be used in practices where possible. Participating general practices not implementing the ‘pop up’ Read code method can identify potential patients during consultation. Having considered eligibility the GP or nurse practitioner will provide the patient with an invitation pack.Patients identified either by a medical records review or general practice consultation are mailed a study pack including an invitation letter from the GP, participant information sheet, reply slip, screening questions and prepaid envelope. Interested patients return the reply slip and screening questions using the prepaid envelope to the research team. Screening consists of two questions regarding current LBP and access to the internet, followed by three safety questions listing symptoms that may indicate serious spinal pathology. Patients who answer ‘yes’ to the first two questions, and ‘no’ to all safety questions, are considered eligible. For those who complete the screening questions and fail safety screening, a physiotherapist contacts the patient to make an appropriate clinical recommendation on hearing a further description of the symptoms. Those who fail the screening are documented on a screening log maintained by the research team. All patients considered eligible for the trial are assigned a unique participant identification number and sent a link to the study website, to complete consent, baseline questionnaires and be randomised. Recruitment opened in November 2018 and is expected to close in December 2020, with data collection completing approximately 12 months later in December 2021.The randomisation process for this trial is fully automated. The intervention and data collection software automatically generates the randomisation sequence, and a computer-generated algorithm block randomises participants to the trial groups. Participants are being stratified by trial recruiting centre and level of physical function: a score of less than four on the Roland Morris Disability Questionnaire RMDQ is beingParticipants randomised to this arm will continue to receive unrestricted usual primary care for LBP. Current National Institute for Health and Care Excellence recommendations for primary care management of LBP suggest assessment to rule out specific spinal pathology and use of risk stratification tools  has been extensively described elsewhere.16Practically, patients can access SupportBack from any device with an internet connection from wherever is most convenient for them. SupportBack consists of six sessions, and patients are encouraged to log in and use one session per week. Automated reminders adhere to this schedule. The first session highlights the centrality of PA in managing LBP, and supports patients to set goals to either walk more, or engage with a range of gentle back exercises of their choice. Goal options are tailored and are based the extent that patients report their LBP obstructs their day-to-day activities. The further sessions feature self-monitoring and feedback regarding their progress with walking or exercise goals, combined with encouragement from SupportBack to continue. After the first session, patients can unlock one further module per week on topics such as sleep, mood and work. These build into a personal repository, that alongside weekly goals, can be accessed at any time. If engaged with weekly, the tailored, interactive part of the intervention will last 6 weeks. Following completion of all the sessions, SupportBack converts into static resource where all activities/exercises and modules can be accessed for the duration of the trial.Participants randomised to this arm will also continue to receive unrestricted usual primary care, with access to the SupportBack internet intervention. In addition, these participants will also receive up to 1 hour of physiotherapist support over the telephone . At both centres (Southampton and Keele) support is provided by MSK physiotherapists working in the National Health Service (NHS).The objectives of the telephone contact are to encourage the use of the SupportBack intervention, provide reassurance regarding LBP and encourage adherence to PA goals. The physiotherapists are asked to closely adhere to a standardised content checklist for each phone call. The checklist follows the Congratulate, Ask, Reassure, Encourage approach,All measures and time points for collection are listed in The primary outcome in this trial is LBP-related physical function measured with the RMDQ.Demographic data are being collected at baseline including age, sex, educational attainment, marital and occupational status. A range of secondary measures are being collected including pain intensity,33To support the health economic analysis health-related quality of life is being measured with the 5-level EQ-5D (EQ-5D-5L)The internet intervention software automatically collects data on number of logins, page and module views and time spent in each login. This data will be used to explore adherence and user engagement to the digital component of the intervention.The reported minimally clinical important difference (MCID) between groups for the RMDQ varies. A between group MCID of 2 or 3 points is commonly reported.Data are primarily being collected online. The LifeGuide intervention and data system collects consent, baseline data including demographics and follow-up data across the four time points . When first sent a link to the system following screening, if patients do not log on within a week, they are emailed to check that they received the link and advised to look in their spam mail. If there is no response, one telephone call is attempted by the research team.With regard to follow up protocol, where there is no response to the online follow-up questionnaire emails, two reminder emails and text messages will be sent. Following continued non-response, a paper questionnaire pack with a prepaid envelope will be sent 1 week after the last email/text reminder. If the paper questionnaires are not returned within 2 weeks of being sent, a blinded research assistant will call the participant to complete the primary outcome measure (RMDQ), quality of life questionnaire (EQ-5D-5L) and pain severity. If the participant is happy to continue, further measures from the questionnaire battery at the respective follow-up point will be collected in this manner. The full follow-up protocol with the telephone calls will be implemented at 6 weeks and 12 months follow-up points. These two follow-up points are considered most important, capturing initial and long-term response. Calling at all time points may lead to increased dropout at later time points. Follow-up at 3 and 6 months will include all the above steps except for the phone calls. All participants will receive a £5 voucher when asked to complete questionnaires at the more distant time points of 6 and 12 months. Examples of data collection forms can be provided by the trial team on request.Quantitative analysis will begin following cleaning and inspection of the data. Descriptive analysis will be conducted to determine outliers and distributions of the data. Where necessary, if data are not normally distributed, transformations will be applied or another appropriate distribution used. The primary analysis for the RMDQ score will be performed using a multilevel mixed model framework with observations at 6 weeks, 3, 6 and 12 months (level 1) nested within participants (level 2). Results will be reported adjusting for baseline severity in function, stratification factors and any prespecified confounders. The model will use all the observed data and makes the assumption that missing RMDQ scores are missing at random given the observed data.As there may not be a constant treatment effect over time, a treatment/time interaction will be modelled and included if significant (at the 5% level), with time treated as a random effect. An unstructured covariance matrix will be used.Analysis of secondary outcomes will also be conducted using linear regression for continuous outcomes and logistic regression for dichotomous outcomes, again controlling for baseline symptom severity, stratification factors and any potential confounders. The structure and pattern of missing data will be examined, if appropriate, and a sensitivity analysis based on data imputed using a multiple imputation model presented. Data will be analysed on an intention-to-treat basis . We will also undertake a complier-average causal effect analysis,It is not anticipated that there will be significant practice level (cluster) effects but this assumption will be tested by comparing a fixed effect model to a random effects model. If there are significant practice level effects then, the model will include a random effect for practice (random intercept) and participant (random intercept and slope on time) to allow for between participant and practice differences at baseline and between participant differences in the rate of change over time (if significant at the 5% level), and fixed effects for baseline covariates.https://www.southampton.ac.uk/medicine/academic_units/projects/supportback2.page).No interim analyses are planned. Full details of the analyses to be undertaken will be set out in the statistical analysis plan and approved by the trial steering committee (TSC). Our full statistical analysis plan will be published on the trial website in due course , obtained from the EQ-5D-5L instrument using the published UK value set. In addition, a cost-effectiveness analysis will be carried out using the study primary outcome measure, that is, the cost per point change in back-related physical function measured using the RMDQ will be estimated. Both costs and effects will be estimated using multiple regression, to allow for potential confounders, such as baseline scores for EQ-5D-5L and RMDQ. Standard practice will be followed to calculate incremental cost-effectiveness ratios (ICERs), and present ICER(s) where any one option has both higher costs and increased effects compared with another. ICERs will show incremental cost per QALY or incremental cost per point improvement in RMDQ. Bootstrapping will be used to calculate cost-effectiveness acceptability curves. These will illustrate the effect of uncertainty on study results. Major assumptions made in the analysis will be tested by means of sensitivity analysis. In particular, assumptions made during the costing of the intervention such as the number of individuals who will be using the website will be explored. Similar methods to the main clinical analysis will be used to handle missing data, that is, analysis of patterns of missing data with multiple imputation methods employed if deemed appropriate. The proposed health economics analysis will be detailed in a health economics analysis plan (HEAP) which will be completed before analysis commences. The HEAP will be circulated for comment prior to the health economics analysis. Any digressions from the HEAP will be documented and justified in the final health economics report.A process evaluation will be carried out following Medical Research Council guidelines on process evaluations of complex interventions.Quantitative data describing trial implementation will be presented including number of practices recruited, patient eligibility  and recruitment rates. The number of withdrawals from the trial per arm will be presented, along with numbers/percentages of drop-outs from the intervention who do not respond to follow up. Use of the internet intervention will be described by presenting automated data collected on number of logins and modules accessed for both the internet intervention and the intervention plus telephone physiotherapist support arm. With regard to the internet intervention plus telephone physiotherapist support arm, the number of support calls successfully made (and attempts contact the patient), along with the mean number per participant in this arm will be described.Qualitative interviews will be conducted with up to 45 trial participants following the 3, 6 and 12 months follow-up points. Different participants will be interviewed at each time point, enabling us to explore how time since accessing the tailored weekly component of the intervention effects how suggestions are used and implemented in daily life. Interviews will also be conducted with the trial physiotherapists. Participants will be purposively sampled to ensure diversity in terms of age, sex and symptom severity . Participants will also be sampled based on high and low usage of the internet intervention and high and low engagement with the telephone physiotherapist support. For participants, questions will focus on their experience of using the intervention, including telephone physiotherapist support and usual care. Interviews with the trial support physiotherapists will be designed to explore their experience of delivering the intervention, with a particular focus on barriers and facilitators, and determinants of successful exchanges.A logic model of proposed mechanisms affecting LBP-related physical disability and pain outcomes for the SupportBack intervention has been developed see ref.. This mo40 41Questions will be included in the qualitative interviews focusing on participants’ perceptions of how use of the SupportBack intervention and/or telephone support affected their LBP. This will enable the inductive exploration of participants’ views and triangulation of qualitatively derived theory on mechanism with our quantitative analysis. Similar questions will also be explored in the usual care arm, focusing on how elements of their usual care may have led to improvements in their LBP.The relationship between elements of participants’ context (moderators) and the effect of the interventions across the 12 months follow-up period will be explored. This will include variables such as LBP severity and duration at baseline, age, educational level and occupation status. Following the analysis of mechanisms, correlations and multiple regression (linear and logistic) will be used to explore relationships between moderating variables and LBP-related physical function and pain intensity. Qualitatively, the above aspects of participants’ context, including their own descriptions of their LBP history, will feed into analysis when exploring themes regarding participants use of the intervention and their perceptions of benefit.42Interview data collected regarding implementation, mechanisms and context will be transcribed verbatim, coded and analysed using an inductive thematic analytic approach.The SupportBack 2 trial has a data monitoring and ethics committee (DMEC) composed of a statistician (chair) and two academic clinicians (Professor in Primary Care Research and Professor of Physiotherapy respectively). The DMEC reports to the TSC and is fully independent from the trial Sponsor with no competing interests. Interim descriptive analyses are prepared for the DMEC. The DMEC charter can be obtained from the research team on request.All serious adverse events (SAEs) are reported to the lead clinical trial unit. The assessment of seriousness will be made by the participants GP or delegate. Assessment of causality will be made by the GP or delegate, and related or unrelated status will be determined. As the SupportBack intervention provides reassurance and encourages gentle activity within the participants’ own limits, there are no ‘expected’ SAEs documented.All patient data are being kept in strict confidence and managed in accordance with the Data Protection Act 2018 and General Data Protection Regulation (2018) legislation. The University of Southampton policy on archiving will be followed; the data will be stored for 10 years following the end of the study, after which time it will be disposed of securely. Following completion of the trial, a cleaned anonymised data set will be shared on request.Patient representatives have been involved with the SupportBack trials from the outset. The idea for the trials and their subsequent design was informed by the local branch of the national charity BackCare. From this group, LL joined the research team and contributed to funding applications for both feasibility and main trials. SupportBack 2 has a panel of three patient and public involvement (PPI) representatives who are part of the trial management group, advising on patient facing materials and contributing to discussions of trial related issues as they arise. PPI will pay a key role in dissemination of trial findings and interpretation of qualitative data.The SupportBack 2 trial has received full ethical approval form a local review board (REC Ref: 18/SC/0388). All potentially eligible patients receive a patient information sheet. This information emphasises that participation in the trial is voluntary and that the participant may withdraw from the trial at any time for any reason. The participants are given the opportunity to ask any questions that may arise by speaking with the trial team and time to consider the information fully prior to agreeing to participate.The findings of this trial will be published in peer-reviewed journals and presented at international conferences. We will develop press releases in order to disseminate the findings to the general public, and work closely with our PPI collaborators to ensure dissemination to patient and other special interest groups. A summary of the findings will be sent to all included general practices and those patients that request this information. If the intervention is shown to be effective, we will work with developers to rapidly develop a version for widescale dissemination and implementation.


First Person is a series of interviews with the first authors of a selection of papers published in Disease Models & Mechanisms, helping early-career researchers promote themselves alongside their papers. Paco López-Cuevas is first author on ‘ Paco López-CuevasHow would you explain the main findings of your paper to non-scientific family and friends?Chordoma is a rare type of bone cancer, affecting approximately one individual per million every year. This cancer can compromise different locations of the spine, but often appears in the base of the skull, where surgeries are challenging. Chordomas originate from internal cells of the spine known as notochord cells, which form part of the notochord. The notochord plays an important role in the formation of the spine, but when notochord cells get transformed into cancerous cells, they grow and divide uncontrollably, and this can result in chordomas. To study the impact of notochord cancer cells in spine formation and bone quality, we used a zebrafish line that induces cancer in the notochord cells. Interestingly, we saw that the notochord cells of these fish divide faster than normal, and they induce a local wound-like response, characterised by increased inflammation and alterations in the sheath layer of collagen that wraps the notochord. These changes have an impact on how the spine is formed in these fish, resulting in vertebral defects that we term butterfly vertebrae, because they look like they have ‘wings’. We also saw an imbalance between the cells that form bone (osteoblasts) and those that destroy bone (osteoclasts), resulting in poor-quality bones. The coolest observation was that when we get rid of inflammatory cells, the notochord cancer cells stop dividing uncontrollably, and fish have fewer butterfly vertebrae. Eureka! We might have found a solution for chordoma in our fish model by playing with the number of inflammatory cells, and this could be a potential way to treat chordomas in human.“[…] when we get rid of inflammatory cells, the notochord cancer cells stop dividing uncontrollably, and fish have fewer butterfly vertebrae.”What are the potential implications of these results for your field of research?Our results suggest that the zebrafish chordoma model used here could be an important tool to help us better understand chordoma biology and how transformed notochord cells and inflammation could be involved in the development of spine malformations and malignancy. Our findings on the role of inflammatory innate immune cells in the development of notochord lesions and subsequent vertebral defects suggest that inflammation could be a promising pharmacological target for chordoma. Overall, our study supports parallels between chordoma and wound-triggered inflammation.What are the main advantages and drawbacks of the model system you have used as it relates to the disease you are investigating?We used a zebrafish cancer model in our studies. Zebrafish are teleost fish that have emerged as advantageous animal models to study a variety of human diseases, including bone diseases and cancers, due to several reasons such as their fast development, translucency and feasible genetic manipulation. Their translucency allows time-lapse confocal imaging to study cell behaviour in real time, in a non-invasive way and without the need to sacrifice animals, something that would not be possible using other animal models. In addition, during the first weeks of development, zebrafish do not have a functional adaptive immune system, allowing the investigation of the innate immune response, on its own. All of these advantages, together with the availability of reporter lines labelling specific cell types, the feasibility of generating mutants for different genes and their skeletal similarities to humans, led us to choose zebrafish to study chordoma. More specifically, a major advantage of our zebrafish chordoma model is the fish survival to adulthood. While previously reported zebrafish chordoma models were lethal at larval stages, our model allowed us to analyse changes from developmental stages to bone homeostasis in adult zebrafish, using techniques such as micro-computed tomography (CT) and histological analysis. Bone changes are characteristic of human chordomas; therefore, our model would be suitable to study aspects of chordoma not possible before, adding the possibility of testing new drugs for therapeutic applications in chordoma development and bone maintenance.A limitation of our model is that transformation of notochord cells is achieved by expression of a mutated RAS gene, which is not commonly observed in chordoma patients. However, our RAS model mimics the downstream signalling driven by the activation of epidermal growth factor receptor (EGFR), which is well known to be highly expressed in human chordomas. Another drawback of our model is the fact that transformation is not only induced in notochord cells, but also in melanoblasts, resulting in melanoma development. Fortunately, this can be avoided by combination with available pigment-free fish lines.What has surprised you the most while conducting your research?During the development of this research, we read in the literature that the progression of several cancers could be mediated by an inflammatory response, and that inhibition of inflammatory cells might be a desirable treatment to prevent cancer growth. We were really surprised when we found out in our own hands that chordoma is a type of cancer that could be treated by depletion of inflammatory cells. This finding was a very striking result that really motivated us to carry on with our investigation. Another surprising moment was when we realized that our zebrafish chordoma model had extensively been used by others to study melanoma for many years , but no one had previously paid attention to their skeletal changes.Describe what you think is the most significant challenge impacting your research at this time and how will this be addressed over the next 10 years?Chordomas are highly resistant to current mainstream cancer treatments, including chemotherapy and radiotherapy, which is the reason why surgery is the main strategy to treat this type of cancer. However, due to the proximity of chordomas to vital structures (e.g. nerves or blood vessels), surgery remains challenging and often fails to achieve a complete removal of the tumour, increasing the probability of cancer recurrence. Therefore, alternative interventions are needed for chordoma patients. In our study, we report that immunotherapy, in particular the suppression of innate inflammatory cells, could be a suitable therapeutic approach for chordoma. Due to its success, this type of treatment modality has increased in popularity over the last years, especially among those cancers where inflammation clearly benefits cancer growth. While immunotherapy is very promising, further research will be required, and animal models like the zebrafish will undoubtedly be valuable to extrapolate results to the clinic.What changes do you think could improve the professional lives of early-career scientists?In my case, after I finished my undergraduate studies, I had the opportunity to conduct a research internship abroad. This helped me to network and establish new collaborations, allowing me to grow scientifically and ultimately facilitating the possibility to start my PhD studies. Being exposed to a completely different laboratory environment outside my country and make new connections was definitely key in the beginning of my scientific career. Therefore, I believe that early professionals would benefit from more funding schemes that are specifically dedicated to this matter. In addition, the election of the laboratory where you would like to carry out your PhD is a fundamental element that can strongly influence your future scientific career. In this line, although I am aware that several universities have begun to do this, I would encourage the implementation of PhD programmes with rotations or short internships in different laboratories at the beginning of these studies and prior to the final laboratory election. This would allow PhD students to get to know their potential PhD supervisor and work with laboratory members, helping them in their decision before this long journey starts.During my PhD, I have been very fortunate to have Prof. Paul Martin as my PhD supervisor. He has always encouraged me to establish collaborations with other scientists outside our laboratory. For instance, this study has been the result of a collaborative work with Dr Erika Kague, who has also been a great mentor for me and has taught me a lot about the bone field. Therefore, in my view, scientists should collaborate from very early in their careers, not only because they could learn from other mentors different from their principal investigators, but it could also increase their chances to publish in scientific journals.Some early-career scientists choose to leave academia due to the uncertainty of this field. Offering permanent postdoctoral positions to those who are not interested in becoming a principal investigator or lecturer, but they rather prefer to perform experiments in the laboratory, would increase the number of scientists remaining in their research jobs.“[…] the election of the laboratory where you would like to carry out your PhD is a fundamental element that can strongly influence your future scientific career.”What motivated you to pursue a scientific career and what are your next steps?Since I started my undergraduate studies, I have been interested in research, so I exposed myself to different laboratories where I enjoyed designing and executing experimental approaches to try and give an answer to biological questions, particularly those that were associated with human pathologies. However, it was not until my first research internship abroad mentioned above that I could contextualize the social, clinical and economic impact that my role as a biomedical researcher could have. My goal during this internship was to investigate new approaches for tuberculosis treatment using the zebrafish model. Seeing that results of my own experiments could have a clinical application was very motivating and made me realise the personal and professional satisfaction that research can create on me. That was the turning point when I decided that I want to pursue a biomedical scientific career, with the hope that I can contribute to our current knowledge of human diseases and help to develop new treatment strategies. Because I am particularly interested in the study of cancer and inflammation, after completion of my PhD I would like to combine all my previous expertise to continue my research in this field and start working as a postdoctoral fellow.


Ixodes ricinus tick reproduction host in this system, and (iii) a warmer climate, concurring with our current knowledge of how temperature affects tick activity and development rates. The implications for policy include adopting increased disease management and awareness in high risk habitats and in the presence of alternative LIV hosts  and tick hosts . These results can also inform deer management policy, especially where there may be conflict between contrasting upland management objectives, for example, revenue from deer hunting vs. sheep farmers.Identifying the risk factors for disease is crucial for developing policy and strategies for controlling exposure to pathogens. However, this is often challenging, especially in complex disease systems, such as vector-borne diseases with multiple hosts and other environmental drivers. Here we combine seroprevalence data with GIS-based environmental variables to identify the environmental risk factors associated with an endemic tick-borne pathogen—louping ill virus—in sheep in Scotland. Higher seroprevalences were associated with (i) upland/moorland habitats, in accordance with what we predicted from the habitat preferences of alternative LIV transmission hosts (such as red grouse), (ii) areas of higher deer density, which supports predictions from previous theoretical models, since deer are the key  Mycobacterium bovis the causative agent of bovine tuberculosis in domestic cattle, which can have wildlife reservoir hosts including red deer Cervus elaphus, wild boar Sus scrofa, and European badgers Meles meles (Fasciola hepatica infection (fasciolosis) in livestock via their effect on the vector, the mud snail Galba truncatula . In Europe these pathogens are vectored primarily by the most ubiquitous tick in Europe, Ixodes ricinus, which is a generalist, parasitizing almost all terrestrial vertebrates. It spends the vast majority of its lifecycle away from its hosts, so its survival and activity is influenced by a multitude of environmental factors [e.g., . In addition, this ensured we excluded pet sheep and small-holdings or “hobby-farms,” which tend to have different management.We conducted a national survey, using a stratified random sampling design based on Scottish Agricultural Census data to ensure random and representative sampling of sheep flocks for all regions over Scotland. Only flocks with at least 50 breeding ewes were included and, on farms that had multiple flocks, only one flock was used. Breeding ewes were chosen to get a representative sample for a location. Younger animals may not have yet seroconverted to endemic pathogens. Tups are likely to have been purchased from elsewhere, so that any seroconversion may have been due to infection picked up in a different location. The inclusion criterion of 50 sheep was chosen because holdings of <50 sheep often do not have the number of breeding ewes we required for sampling . This sample size allows a 95% confidence interval of <5% for estimating LIV sero-prevalence . FarmersHemagglutination-inhibiting antibody (HIA) tests were undertaken on sheep blood sera using chick red blood cells as described by Clarke and Casals . The LIVFrom GIS databases we extracted data on climate, habitat and tick hosts for the locations of each farm. Climate variables from a GIS database included variables relating to temperature and precipitation on a 1 km or 5 km grid . All theHabitat data were derived from the UK Land Cover Map (2000) in a 50 m grid. They were split into the following categories according to those most commonly occurring around the farms: bracken, blanket bog, heathland , improved grassland, rough grassland, montane, broadleaf woodland, coniferous woodland, mixed woodland and, in addition, we created a generic “woodland” category ; 2 grid resolution.Sheep and cattle density data were obtained from the national agricultural census data (AgCensus), available at the Parish level, at a 2 kmApproximate red deer densities were derived from Deer Commission for Scotland  count data, based on dedicated observer counts of individual deer from the ground or air, Krigged to a 2 km grid. These data are the best (indeed only) quantitative deer data available, but have several caveats. For example, red deer counts were conducted where the 44 Deer Management Groups areas are, but these cover only around 75–80% of Scotland. Furthermore, the counts for different areas were not always conducted at the same time but, instead, staggered between 2000 and 2006. Therefore, given the level of error in these data, we consider any positive results linking deer density to LIV seroprevalence in sheep to be highly conservative.Intersect Point tool was used in ArcMap v9.3  to extract the values of all environmental parameters at the locations of each of the 125 sheep farms from a set of raster and vector maps of environmental data.To test for environmental variables  associated with the sero-prevalence of louping ill virus among sheep farms we used general linear mixed models using the glimmix procedure in SAS Version 9.1. The response variable was seroprevalence for each farm expressed as the number of positive serum samples divided by the number of samples assayed for each farm. This is more powerful than using merely a single figure for the proportion of positives because it allows the model to take into account the number of samples taken, which varied from 26 to 29. A binomial distribution was specified. The data distribution was over-dispersed and zero-inflated , which is commonly found with disease prevalence data such as these. Therefore, each data point, i.e., individual farm, was entered as a random effect in the model  as a wayP-values for the variable and model AIC. We also then entered all climate variables within a category into the model simultaneously to identify which had the strongest overall effect in terms of F and P-values and change in AIC. This variable selection procedure selected annual growing degree days  from the temperature-related variables, and the number of dry days from the precipitation-related variables.Because of the large number of potential climate-related explanatory variables  and becaThus, we entered the following selected explanatory variables as fixed effects into the model: easting and northing , time of year that bloods were sampled , year of blood sampling, estimated deer density, estimated sheep density, growing degree days, dry days, and the proportion of land cover that was each habitat category listed in We had expected deer density to covary positively with the proportion of heathland. Also, because areas with more heathland have less improved grassland , we expep > 0.1, and then checked that removal did not adversely affect model fit (increase AIC). If removal had increased AIC, the variable would have been kept in the model, but this did not occur in our procedure. Because such terms were eliminated from the models, we present test statistics for only those fixed effects that remained in the final model.We conducted a backwards stepwise procedure, whereby we sequentially removed from the model all explanatory variables that did not improve the model: we first removed each variable that had very low significance, i.e., Of the 125 sheep farms sampled, 28 (22.4%) farms contained LIV seropositive sheep . The proportion of positive farms varied greatly over different areas of Scotland , with a The national average seroprevalence, including farms that did not have LIV, was 6.39% . If only sero-positive farms are considered, the average seroprevalence was 28.52% . The frequency distribution of within-farm LIV seroprevalence exhibited a negative binomial distribution typical of disease and count data, with most farms having no or low infection, and a small number having very high infection rates .Sheep farms had higher within-farm LIV seroprevalences (% of ewes within a farm that tested positive) if they were in areas with a warmer climate (more growing degree days per annum), a higher proportion of land that is heath-dominated moorland, a lower proportion of land under improved (smooth) grassland and areas with higher deer densities . There wWe aimed to test the hypothesis that LIV prevalence in sheep farms is influenced by environmental factors, especially those associated with tick abundance and LIV transmission hosts.B. burgdorferi prevalence: . Higher temperatures increase tick interstadial development rate, oviposition rate, egg development rates and tick activity –37, and valence: , 40. WhiAs predicted, there were higher LIV seroprevalences among sheep farms in heather moorland which is the characteristic habitat in upland UK and is the habitat most frequented by wildlife hosts that are competent LIV transmitters: red grouse and mountain hares. Upland areas with more heather moorland had less improved grassland . ImproveI. ricinus ticks in Scotland in both upland heather moorland and forest habitats at a range of spatial scales  between woodlands and adjacent open habitats which were often only 50–100 m apart, which suggests that any link between woodlands and tick or tick-borne risk incidence in adjacent open habitats probably operates at a much finer spatial scale than we had access to in this study.Against our predictions, we did not find a significant association between LIV seroprevalence and the proportion of land cover that was woodland. However, a previous more detailed study of ticks (not LIV) found that (i) distance to woodland and (ii) the proportion of sheep pasture that had tree cover were strong predictors of tick burdens on lambs and tick densities in sheep pastures in Norway . One reaI. ricinus tick densities in most areas of these islands (Gilbert unpublished data). This is likely due to a combination of the lack of deer and the colder climate which inhibits tick activity and development. The eastern regions of Scotland had intermediate seroprevalence. These areas, especially Grampian Region, Speyside and Perthshire, have a particularly heterogeneous landscape, from high quality improved grassland for cattle up to high altitude  montane habitats, with extensive forested areas and heather moorland in between. Some of these heathlands have the highest deer, red grouse and mountain hare densities in Scotland. Here, therefore, we would expect a wide spectrum of LIV seroprevalences, which is reflected by the overall intermediate values over the whole region. South of the Central Belt of Scotland there were even lower seroprevalence than the Northern Isles, even with a good sample size of 36 farms. The main habitats are upland rough grasslands and commercial coniferous forests, with some improved grassland for high density livestock grazing. There are deer, and some mountain hares and red grouse present, although not at the densities found in the East region of Scotland. We would therefore expect lower LIV infection rates than in the East region, but it is not clear why the seroprevalence is as extremely low as it is. This could be due to unconsidered factors such as historical movements of infected sheep and warrants further research.Geographically, the proportion of farms testing seropositive to LIV was much higher along the West and North coasts of Scotland than in other areas. This might be expected given the warm, humid climate which aids tick survival, activity and development. There was low LIV seroprevalence in the Northern Isles (Shetland and Orkney) which is most likely attributed to very low Although exposure of sheep to ticks can be mitigated by acaricide application to the animals  alternatThe datasets generated for this study are available on request to the corresponding author.The study design was reviewed and approved by Moredun Research Institute.CC and FB initiated and designed the blood collecting sampling strategy as part of a separate study on Ovine pulmonary adenocarcinoma . FB organized farm visits and collected blood samples and KW conducted the LIV serology tests. LG wrote the LIV proposal to Scottish Government, assimilated the separate data sets, analyzed the data and wrote the paper. CC, FB, and KW commented on the manuscript drafts. 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.


Saccharomyces cerevisiae, expressing different toxins, to independently control the rate at which they released their toxins. We developed mathematical models that predict the experimental dynamics of competition between toxin-producing strains in both well-mixed and spatially structured populations. In both situations, we experimentally verified theory’s prediction that a stronger antagonist can invade a weaker one only if the initial invading population exceeds a critical frequency or size. Finally, we found that toxin-resistant cells and weaker killers arose in spatially structured competitions between toxin-producing strains, suggesting that adaptive evolution can affect the outcome of microbial antagonism in spatial settings.Antagonistic interactions are widespread in the microbial world and affect microbial evolutionary dynamics. Natural microbial communities often display spatial structure, which affects biological interactions, but much of what we know about microbial antagonism comes from laboratory studies of well-mixed communities. To overcome this limitation, we manipulated two killer strains of the budding yeast  Microbes affect nearly every aspect of life on Earth, from carbon fixation  to humanVibrio cholerae strains to kill each other, using the type VI secretion system, coarsens the single-strain domains of populations that were initially well mixed . A formally equivalent model that included spatial diffusion and noise due to number fluctuations was studied theoretically in where concentration rather than the actual physical size discussed later in this paper for spatially structured communities on surfaces. Nevertheless, when number fluctuations are included in the dynamics, there is an interesting analogy with escape over a barrier problems in statistical mechanics  highlighted with a gray arrow and gray lines in o showed that their average fluorescent intensity was reduced with respect to the ancestral K1 population, and also compared to K1 cells sampled from a non-outlier population (K1s) that successfully expanded in the spatially structured experiments  rule out this hypothesis, given that they followed the same dynamics of competition assays between the original K2b stock and K1  re-invaded the K1 population (magenta) after the halo had formed. By competing K2b cells from the sub-population highlighted with the green arrow (K2bg) in We tested the hypothesis that the outlier population K1stor K2b , panel Ak and K1 . Given t the top , howeverKm, for the local nutrient concentration, and a death term proportional to the local concentration of the toxins. Cells diffuse locally on the surface of the agar via a growth-dependent diffusion term reflecting the fact that cells push each other around as they interact mechanically with other cells during their growth and division , using as backbone a pFA6a-prACT1-ymCherry-KanMX6 plasmid (pAG3) linearized with the restriction enzymes EcoRI and EcoRV. The segments containing CUP1P, K2, and CYC1T were assembled via Gibson assembly (plasmid pAG14), using as backbone a pFA6a-prACT1-ymCitrine-KanMX6 plasmid (pAG5) linearized with the restriction enzymes EcoRI and EcoRV. These plasmids were linearized at the CYC1T locus using the restriction enzyme PpuMI, and their integration at the CYC1T locus was verified using colony PCR using primers oAG5/oAG46 and oAG44/oAG45 (strain K1), and oAG8/oAG46 and oAG44/oAG45 (strains K2 and K2b).The killer toxin and fluorescent protein genes used in this study were cloned into integrative plasmids. The K1 killer toxin gene was PCR-amplified from the plasmid YES2.1/V5-HIS-TOPO-K1 pptox , which cK2 toxin , and for plasmid . The proS. cerevisiae strain yJHK234 derived from the W303 genetic background. This strain was constructed as described in GAL1P occurs in a titratable, unimodal way in response to changes in the extracellular concentration of galactose, because galactose has been turned into a gratuitous, non-metabolizable inducer by deleting the genes GAL1 and GAL10 from its genome, and the bistability in galactose induction has been removed by placing the GAL3 gene under the constitutive promoter PACT1  methylene blue (an indicator for cell death), incubated at 25°C for 24 hr and isolated blue colonies (clones cured of the virus being killed by surrounding non-cured colonies). We tested that the isolated clone yAG74 was sensitive to toxins secreted by both the K1 and K2 reference strains. To prevent catabolite repression, which would prevent expression of the K1 toxin from GAL1P in the presence of glucose, we deleted the hexokinase two gene, HXK2  was obtained by transforming the linearized plasmid pAG11 into strain yAG75. The K2 (yAG83) and K2b (yAG82) killer strains were obtained by transforming the linearized plasmid pAG14 into strain yAG75. The transformant clones yAG94 and yAG83 were chosen for the experiments because of the bright fluorescence signal of the transformed cells observed at the flow cytometer and at the stereomicroscope compared to other transformants, which suggests multiple integrations of the plasmid into the genome. Conversely, yAG82 was selected because of the weaker fluorescent signal of the transformed cells compared to yAG83, suggesting a single integration of the plasmid. The sensitive, nonkiller strains S1 (yAG96) and S2 (yAG99) were obtained by transforming strain yAG75 with the linearized plasmids pAG3 and pAG5 digested with the restriction enzyme AgeI (which cleaves DNA in ACT1P), respectively.The strains used here were derivatives of the er PACT1 . By comp Ramírez , we founne, HXK2  from yAGN,N,N′,N′-tetraacetic acid (EGTA), a chelating agent that we used to reduce the baseline expression of CUP1P. The medium was prepared by mixing 990 mL of Millipore-purified water, 20 g of BD Bacto Peptone, 10 g of BD Bacto Yeast Extract, 10 mL of a 1% (w/v) solution of adenine and tryptophan, 1.04 g of NaOH and 9.51 g of EGTA. Then, 2 g of NaOH were added to bring the EGTA into solution. Then, we added 11.2 g of succinic acid and brought the pH to 4.5 by adding approximately further 2 g of NaOH. The solution was then filter-sterilized. Agarose medium was prepared following the same procedure but using 590 mL of water instead of 990 mL. Separately, 400 mL of Millipore water were mixed with 20 g of BD Bacto Agar and microwaved for 2 min. The two solutions were then combined and used to fill Petri dishes. Solutions of copper(II) sulfate and of galactose at different concentrations were added to the media in different volumes according to the desired final concentration of the two inducers All experiments were performed using YPD buffered at pH 4.5 and supplemented with adenine, tryptophan, and ethylene glycol-bis(β-aminoethyl ether)-b were mixed with strains S2, S1, and S1, respectively (so that the two strains expressed different fluorescent proteins). Cell suspensions containing different strains were then mixed at the desired relative frequencies, and 40 µL of these were then diluted in 10 mL YPD buffered at pH 4.5 with EGTA. Each replica in the competition assays consisted of 500 µL of this solution placed in deep-welled (capacity 2 mL/well), 96-well, round-bottomed plates, taped to a roller drum rotating at a frequency of 1 rotation per second and placed in a room kept at 25°C. Technical replicates were assigned to random positions on the 96-well plate, irrespective of the treatment they belonged to. At regular time intervals, small samples (≤10 µL) were taken from each well, diluted in 50 mM Tris-HCl, pH 7.8, and the relative frequencies of the two strains were measured by flow cytometry. Flow cytometry data was performed using the Python package FlowCytometryTools and custom Python and Mathematica scripts. Occasionally, during measurement with the flow cytometer, some wells were not measured due to the aspiration of bubbles by the robotic liquid handler that automatically measured the 96-well plates. Due to the temporal sensitivity of the assay, relative frequency data from those replicates could not be recovered, and thus we excluded those technical replicates from the analysis. Competition assays with the unusual cells sampled from the experiments of Competition assays in liquid media were performed as follows. Strains were plated from the glycerol stock 4 days prior to the start of the experiment and grown for 2 days at 30°C in YPD plates. One day before the start of the competition assays, overnight cultures were started by transferring cells from the plates to a tube containing 2 mL YPD buffered at pH 4.5, which was placed in a rotating roller drum at 30°C. At the start of the competition assays, 200 µL from the overnight cultures were centrifuged, the supernatant was removed, and cells were then resuspended in 2 mL autoclaved water. The centrifugation and resuspension were repeated twice to dilute away any toxins produced overnight. For killer-versus-nonkiller competition assays, the strains K1, K2, and K2Media and growth conditions) were added to 100 mm diameter Petri dishes 2 days before the start of the experiment, along with appropriate amounts of a 5 mM solution of copper(II) sulfate or a 50 mM solution of galactose to reach the desired target concentration of inducer on the plates. The day before the experiment we inoculated overnight cultures of strains K1, K2, S1, and S2 in 2 mL YPD culture tubes with pH 4.5 and 25 mM EGTA and grew them at 30°C on a rotating roller drum. At the start of the experiment, we centrifuged and resuspended 200 µL of the overnight cultures in 2 mL autoclaved Millipore-purified water, repeating the centrifugation and resuspension twice to remove toxins from the overnight cultures. We mixed strains K1 and K2 with K1 frequencies b, S1, and S2 in 200 µL autoclaved Millipore water, repeating the centrifugation and resuspension twice to remove toxins from the overnight cultures. For the experiments of b culture were spread on the surface of the agar using an inoculating loop. Then, using a micropipette, we deposited droplets of the resuspended overnight K1 culture on top of the K2b lawn, at random locations on a regular lattice, well separated from each other. We deposited droplets of six different volumes (0.5–3 µL with 0.5 µL increments), with seven replicates per volume, across multiple plates. Similarly for the experiments shown in The experiments with spatially well-mixed populations on surfaces shown in n effect . In thisThe starting point for the derivation of the frequency model where which is in fact with –1, –1 and –1(cell/mL)–1 (see next section for its estimate), and thus the condition is satisfied for –1, it takes about 17 hr for the condition which show that the toxin concentrations evolve with the characteristic time scale the data  and 3A. Our starting point for the investigation of antagonistic population dynamics in spatially structured populations was the following spatial generalization of 2), which is related to the well-mixed carrying capacity via the relationship Parameter fitting). We found that the non-spatial version of this model could indeed fit the antagonistic dynamics between toxin-producing and nonkiller strains, and that it could predict the dynamics of antagonistic competition between the two toxin-producing strains K1 and K2 (and K1 versus K2b) in well-mixed media . These S. cerevisiae on glucose, where where b) in well-mixed media.Here, lgorithm  and the The parameters of the frequency model were obtained by least-squares fitting of the equation:where The parameters of where The parameters of The toxin concentration and toxin production rate were rescaled as described previously. The initial concentration of glucose was set to the experimental value terature . The valConfidence intervals for the model predictions in 2/hr , the critical inoculum size is about 20 mm2. In The diffusion coefficient of glucose in s factor . Additio 1/a1-a2 . In Figu2 to account for the increased killer strength of copper-induced K2 killer strains on agar plates, compared to liquid cultures. This value was calculated as follows. Upon interpolating between the last two data points in the lower-right panel of 2 for strain K2 grown on plates with 0 µM copper and 350 µM galactose. Given that K2b was a weaker killer than K2 by a factor 2. The initial condition for the radial density profiles of the two strains was set to reproduce the experiments as closely as possible. To this end, we first reproduced experimentally the initial conditions of the experiments of b using the same protocol as for the experiments of b occupied the region b was set to zero for b. The dynamics of this simpler, but less accurate, simulation .Thank you for submitting your article "Antagonism between killer yeast strains as an experimental model for biological nucleation dynamics" for consideration by The reviewers have discussed the reviews with one another and the Reviewing Editor has drafted this decision to help you prepare a revised submission.As you will see below, all reviewers found the paper interesting and important. A series of concerns me questions raised by all reviewers concerns the nutrient depletion explanation of the halos. The specific assumption made in the model (and not confirmed by the experiment) may turn out to be inevitable. Nevertheless, please address the concerns regarding the assumptions on which the model based, as well as all other comments, as specified by the detailed reviews below.Reviewer #1:The authors present a beautifully clean model system to study mutual antagonism in spatial and well-mixed populations which they use to test theoretical predictions from models developed previously (and adapted here). In particular, the authors verify the prediction that in a spatial setting a stronger antagonist can invade a population of a weaker antagonist only if the former is present at a large enough initial population size. The paper is well written and very accessible, and I have only two substantive concerns:1. Given the focus on minimum population size for invasion in the introduction I expected the authors to present the same verification also for well-mixed populations. While Figure 3B seems to indicate that in well-mixed populations the relative abundance of the stronger antagonist can indeed decrease if its initial abundance is below a threshold value I would have liked to see this shown (or at least stated in the text) more clearly.2. In the later portions of the manuscript, the authors infer from a variety of models they tested that the halo separating the two genotypes stems from nutrient depletion. To make their conclusions about nutrient depletion more comprehensible it would be helpful to show the results of models of intermediate complexity and their lack of a halo effect. The authors then go on to suggest (l. 495) that resistant mutants can re-invade the invading strains by presumably crossing the halo into the invader's territory; but how do the mutants cross the nutrient-depleted halo?Reviewer #2:In this manuscript the authors report interesting results on the population dynamics of a yeast population containing two antagonistic strains. The authors designed the yeast strains to produce toxins in variable amounts, which are regulated by inducible promoters. This enables them to systematically study the role of antagonistic interactions under controlled experimental conditions in different environments, including well mixed liquid cultures and agar surfaces. Specifically, they are investigating under which conditions the "stronger" of the two strains is able to invade a population of the "weaker" strain. Their main result is that the invasion requires a threshold population size (nucleation threshold) of the stronger strain. While the existence of such a threshold is less surprising (due to the state-dependence of the competition), the quantification of the effect in well-mixed and spatially extended systems is interesting. In particular, the authors give a fairly quantitative mathematical description of population dynamics using a rate equation approach for the well-mixed system and a reaction-diffusion model for the spatially extended system. This mathematical analysis provided further insights into the nature of competition. This includes that it is possible to describe the well mixed system in the form of a frequency model using interaction parameters determined from the interaction of the toxin-producing strains with sensitive strains. Furthermore, the reaction-diffusion equations, which explicitly consider both the toxin and the nutrients, seem to capture the formation of a depletion zone  between the two antagonistic strains at the start of their interaction.The paper is well written and interesting to read. In principle I am in favour of recommending the manuscript for publication. However, I have a major point of criticism that the authors should consider before I can make final recommendations.When investigating the nature and role of the different mutants found in their studies, the authors do not discuss the possibility of changes in growth rates. While the engineered original strains show the same growth rates, this might not be the case for the mutant strains. This could have a major impact on the dynamics, since, for example, the validity of a pure frequency model depends on identical growth rates. I would ask the authors to measure these growth rates in their mutant strains. Furthermore, I think it should be straightforward to test possible effects of a change in growth rates with their reaction-diffusion model.Reviewer #3:In addition to the technical questions raised above I have a few quibbles about the presentation.– The authors start using the term "stronger antagonist" already in the abstract without giving it even any definition, so the reader has to guess the meaning the best she/he can. Definition comes on line 118, page 8…– It would be useful to have a more explicit layout for the paper early on. This reader was wondering about effects of toxin stability and diffusion all through the well-mixed section, without knowing that it is coming later.eLife after suitable revisions/clarifications.Overall, I think the manuscript is suitable for  As you will see below, all reviewers found the paper interesting and important. A series of concerns me questions raised by all reviewers concerns the nutrient depletion explanation of the halos. The specific assumption made in the model (and not confirmed by the experiment) may turn out to be inevitable. Nevertheless, please address the concerns regarding the assumptions on which the model based, as well as all other comments, as specified by the detailed reviews below.and of the depletion of nutrients. However, we cannot come up with a simple way of testing whether the toxin activity alone is sufficient to cause the formation of the halo. One could imagine a setup in which nutrients are continuously provided to the agar, but this would cause an indefinite growth of the two populations, which would lead to a very thick lawn on top of the agar, and the physiological state and physical environment of cells within such lawn would be very different from that of our experiment. Most likely, such indefinite growth would lead to the halo being filled with cells, as it is hard to imagine that a ‘canyon’ would persist with no ‘landslides’ in such configuration. Alternatively, one could envision doing an experiment in a sophisticated setup using microfluidic chambers that would only allow a monolayer of cell to grow within them, but the pressure buildup within the chamber would also most likely lead to the halo being filled with cells. In both cases, the physical environment would be very different from the one experienced by the cells in our experiments. For these reasons, we rely on modeling to explain the halo formation dynamics, using a combination of experimentally measured and physically realistic parameters. Incidentally, the increased density of cells at the two sides of the halo oC to 32oC for further 48h , a temperature at which both the K1 and K2 toxins are unstable and fail to inhibit the growth of susceptible strains .”“If the halo was caused by the presence of the toxins alone, and not by the combined effect of the toxins and the diffusion of nutrients away from the agar underneath the halo, one would expect that inhibition of the toxin would allow cells to re-invade the halo region. To test this, we experimentally verified that no further growth in the halo region is observed after transferring populations that competed for 48h at 25Although we failed to comment on this in the previous version of the manuscript, there is a simple explanation for the failure of the model with logistic growth term to produce the halo, and we have now added a discussion on this point in the revised version of the manuscript (lines 837-849). We thank you and the reviewers for pointing out the missing explanation. The rationale is as follows.2 on the agar can support a given number of cells. In such a model, nutrients located in a given region of space cannot diffuse to nearby regions and thus can only support cells locally. With toxin production rates representative of our experiments, the toxin produced by an antagonist strain is not sufficient to completely halt the growth of the other antagonist. In the model with logistic growth, therefore, the two populations are able to grow at the interface between the two antagonist strains, even if at a slower pace compared to other regions of space, eventually filling the halo region with cells in the limit of large times. When nutrients can diffuse, however, nutrients move to other regions of space before cells at the interface between the two antagonists are able to grow, leading to the depletion region that we referred to as the ‘halo’.The logistic growth term assumes that every cmReviewer #1:The authors present a beautifully clean model system to study mutual antagonism in spatial and well-mixed populations which they use to test theoretical predictions from models developed previously (and adapted here). In particular, the authors verify the prediction that in a spatial setting a stronger antagonist can invade a population of a weaker antagonist only if the former is present at a large enough initial population size. The paper is well written and very accessible, and I have only two substantive concerns:1. Given the focus on minimum population size for invasion in the introduction I expected the authors to present the same verification also for well-mixed populations. While Figure 3B seems to indicate that in well-mixed populations the relative abundance of the stronger antagonist can indeed decrease if its initial abundance is below a threshold value I would have liked to see this shown (or at least stated in the text) more clearly.Thank you for this comment, we have now highlighted this point more clearly in the main text at lines 192-198:eq thus represents a critical inoculum size below which the invasion of a stronger antagonist is predicted to fail in well-mixed settings. Note that this particular “size” relates to an inoculum concentration rather than the actual physical size discussed later in this paper for spatially structured communities on surfaces. Nevertheless, when number fluctuations are included in the dynamics, there is an interesting analogy with escape over a barrier problems in statistical mechanics .”The equilibrium frequencyf and at lines 248-251:feq, is required for a stronger antagonist to invade a resident, antagonist population.”“Overall, the experimental results from well-mixed experiments  confirm the theoretical prediction that a critical starting frequency, the equilibrium frequency 2. In the later portions of the manuscript, the authors infer from a variety of models they tested that the halo separating the two genotypes stems from nutrient depletion. To make their conclusions about nutrient depletion more comprehensible it would be helpful to show the results of models of intermediate complexity and their lack of a halo effect. The authors then go on to suggest (l. 495) that resistant mutants can re-invade the invading strains by presumably crossing the halo into the invader's territory; but how do the mutants cross the nutrient-depleted halo?Thank you for this comment, which helped us realize that we hadn’t given sufficient explanation for the failure of the logistic growth model to reproduce the formation of the halo. We have now included the following discussion at lines 836-849 explaining why the models with logistic growth fail to reproduce the formation of the halo:2 on the agar can support Kspatial cells. In such a model, nutrients located in a given region of space cannot diffuse to nearby regions and thus can only support the growth of cells locally. With toxin production rates representative of our experiments, the toxin produced by an antagonist strain is not sufficient to completely halt the growth of the other antagonist, as shown by the fact that the absolute number of cells of both antagonists grew in all our well-mixed competition experiments, even if the relative frequency of one of the strains declined with time. In the model with logistic growth, the two populations are thus able to grow at the interface between the two antagonist strains, even if at a slower pace compared to other regions of space, eventually almost completely filling the halo region with cells . When nutrients can diffuse, however, nutrients move to other regions of space before cells at the interface between the two antagonists are able to grow, leading to the depletion region that we referred to as the ‘halo’.”“The failure of such model to reproduce the halo can be explained as follows. The logistic growth term in Equations 6 assumes that every cmWe have also now included in Figure 7 – supplement 1 plots of numerical simulations of the intermediate models, showing that they fail to reproduce the formation of the halo.Concerning the ability of the mutants to cross the nutrient-depleted halo, it is important to note that, right after a dilution, nutrients are abundant everywhere on the plate, including in the area corresponding to the halo. In the absence of toxin-resistant mutations, the growth rate of cells is reduced in that area due to the presence of the toxin, so nutrients that are below the area corresponding to the halo can diffuse away from that region of space and can be taken up by nearby cells at the edge of the resident and invader populations. In the presence of a resistant mutant, however, the growth rate of resistant cells at the edge of the halo is not reduced by the presence of the toxin, and thus these cells are able to take up nutrients before they diffuse away from the halo. This early access to nutrients allows these cells to cross the halo. We have now added a sentence to clarify this point at lines 550-554:;We believe that resistant cells were able to cross the nutrient-depleted region of the halo because, right after the populations were diluted by replica plating, resistant cells could grow and divide despite the presence of the invader’s strain toxin, and they could thus take up nutrients located in that region of space before those nutrients diffused away.”Reviewer #2:In this manuscript the authors report interesting results on the population dynamics of a yeast population containing two antagonistic strains. The authors designed the yeast strains to produce toxins in variable amounts, which are regulated by inducible promoters. This enables them to systematically study the role of antagonistic interactions under controlled experimental conditions in different environments, including well mixed liquid cultures and agar surfaces. Specifically, they are investigating under which conditions the "stronger" of the two strains is able to invade a population of the "weaker" strain. Their main result is that the invasion requires a threshold population size (nucleation threshold) of the stronger strain. While the existence of such a threshold is less surprising (due to the state-dependence of the competition), the quantification of the effect in well-mixed and spatially extended systems is interesting. In particular, the authors give a fairly quantitative mathematical description of population dynamics using a rate equation approach for the well-mixed system and a reaction-diffusion model for the spatially extended system. This mathematical analysis provided further insights into the nature of competition. This includes that it is possible to describe the well mixed system in the form of a frequency model using interaction parameters determined from the interaction of the toxin-producing strains with sensitive strains. Furthermore, the reaction-diffusion equations, which explicitly consider both the toxin and the nutrients, seem to capture the formation of a depletion zone  between the two antagonistic strains at the start of their interaction.The paper is well written and interesting to read. In principle I am in favour of recommending the manuscript for publication. However, I have a major point of criticism that the authors should consider before I can make final recommendations.When investigating the nature and role of the different mutants found in their studies, the authors do not discuss the possibility of changes in growth rates. While the engineered original strains show the same growth rates, this might not be the case for the mutant strains. This could have a major impact on the dynamics, since, for example, the validity of a pure frequency model depends on identical growth rates. I would ask the authors to measure these growth rates in their mutant strains. Furthermore, I think it should be straightforward to test possible effects of a change in growth rates with their reaction-diffusion model.Thank you for this interesting observation. We have now measured the growth rates of mutants in two ways:oC using a stage-top incubator. The toxin diffusion coefficient is estimated at 0.003 cm2/h, and thus the production of the K2 toxin by strain K2b and its mutants does not affect cells of the of the K1 strain and its mutants at a distance of 1 cm on the same plate (the average distance traveled by a toxin molecule over such time is ). During the 6 h, cells formed micro-colonies of up to 32 cells, with the exception of a few non-dividing or very slow dividing cells that we excluded from the analysis, but whose growth rates are shown in the Figure 6 – figure supplement 2. T-tests performed between all pairs of strains/mutants gave no statistically significant differences between their growth rates .1. By spreading single cells of each mutant and ancestor cells on a YPD agar plate identical to the ones used in the spatial experiments , with no inducers added. Droplets of each strain/mutant were deposited on the plate on a grid at distances of 1 cm from each other and the location of each strain/mutant within the grid was randomized. Cells on the plate were imaged using an inverted microscope with 50X objective every 20 min for 6 h, during which the plate was kept at 25GAL1P  versus strain S2 can be used to directly detect differences in reproductive fitness, because no toxin is produced in the absence of inducer  and thus the relative frequency of the two strains varies due to differences in reproductive fitness alone. Mixed competition assays between strains carrying the K2 killer toxin gene induced by the promoter CUP1P  versus strain S1, instead, can only detect the joint effect of differences in reproductive fitness and killer activity, given that the promoter CUP1P has non-zero expression even in the absence of its inducer (copper). T-tests performed between all pairs of competitions between killer strains/mutants of the same type  and the corresponding sensitive strains  give no statistically significant differences between the rates at which the relative frequency of killer strains in competition assays vary with time . Although the curves on the left plot are not linear, we note that the curves for strains K2b, K2bo and K2bs overlap. The curve for the resistant strain K2br does not overlap with the others, which may be attributable to a difference in the initial relative frequency of K2br and the sensitive strain S1.2. By growing liquid cultures of each mutant and ancestor strain mixed with a strain sensitive to the toxins produced by the former and expressing a fluorescent protein of the opposite color, measuring the relative density of each strain in pairwise competitions, and diluting daily. These competition assays performed over multiple days and generations can detect smaller fitness differences than the experiments reported above, by measuring the relative frequency of the two strains at different times over multiple generations. Mixed competition assays performed with strains carrying the K1 killer toxin gene induced by the promoter Taken together, these results show that no changes in growth rates are detectable between the strains and mutants isolated at the end of the experiment, and between such strains and their ancestors. We have now included these plots in the revised version of the manuscript as Figure 6 —figure supplements 2 and 3. The fact that no differences in growth rate could be detected between any pair of strains is now mentioned in the main text at lines 439-442:“Figure 6 —figure supplements 2 and 3 reveal that we could not detect any differences in growth rate between any pairs of strains, ruling out the possibility that the altered outcomes of competition observed in Figure 6 could be due to changes in cell division times during the experiment.”Reviewer #3:In addition to the technical questions raised above I have a few quibbles about the presentation.– The authors start using the term "stronger antagonist" already in the abstract without giving it even any definition, so the reader has to guess the meaning the best she/he can. Definition comes on line 118, page 8…The definition has now been anticipated to lines 61-63, as there was no space to define it in the abstract:“We refer to the strain that survives in a 1:1, well-mixed culture as the stronger antagonist and the one that goes extinct as the weaker antagonist. Models based on generalizations of the Lotka-Volterra equations  predict that being a stronger antagonist  is a necessary, but not a sufficient condition for an invading strain to replace a resident, antagonist population”It is of course difficult to give a precise definition before having introduced the details of the experimental system, hopefully the adjectives ‘strong’ and ‘weak’ in the abstract make at least intuitive sense.– It would be useful to have a more explicit layout for the paper early on. This reader was wondering about effects of toxin stability and diffusion all through the well-mixed section, without knowing that it is coming later.We have now added an explicit layout at lines 98-105. Together with the paragraph at lines 8597 and Figure 1, the layout of the paper should now be clearer.


Betsch and Sprengholz  address In this study , severalhttps://pollenscience.eu/, using “allergy-relevant” pollen thresholds (30 and 100 m−3), whose own creator has highlighted that such thresholds may be disputable  (https://pollenscience.eu/).Third, based on their data and analysis code (/j5g7n/) , the var/j5g7n/) , as wellhttps://www.augsburg.de/umwelt-soziales/gesundheit/coronavirus/fallzahlen; last access: June 17, 2021), as well as pollen measurements . We found that while the number of tests per week remained relatively stable, the percentage of positive tests peaked between weeks 13 and 19 , with a minor decrease in R2 = 0.16.We do acknowledge with Betsch and Sprengholz  that it 3 and 19 . The peaOverall, we agree that the testing is a significant parameter to be taken into account when modeling the spreading of COVID-19, but when integrated into other more significant variables, like airborne pollen concentrations, the magnitude of difference in the explained variability is minimal, only 1%.


Dear Editor,We read with great interest the recent article by Moleski et al. , discussn = 205) and with a new presentation of IBS ‘type’ symptoms (n = 74). Patients were divided into two groups, group one consisted of individuals presenting with self-reported NCGS. In contrast, group two consisted of individuals with a new presentation of IBS ‘type’ symptoms. Individuals were characterised according to demographics, in addition to investigative outcomes.It is important to highlight to readers that patients who present with either self-reported NCGS or irritable bowel syndrome (IBS) ‘type’ symptoms should be investigated for other causes for their gastrointestinal symptoms. In view of this, we reviewed the case notes of patients with self-reported NCGS (n = 172) and 62% (n = 46) respectively. There was no significant difference in the presenting age between patients . Following investigation, 11.7% (n = 24) of NCGS and 17.6% (n = 13) of patients with IBS ‘type’ symptoms were found to have other gastrointestinal diagnosis (Tables n = 18) and by comparison for patients with IBS ‘type’ symptoms it was bile acid diarrhoea (BAD) .We found that patients presenting with NCGS and IBS ‘type’ symptoms were predominantly female, at 84% n = 72 and 62We conclude that the demographics of patients with NCGS and IBS are similar, with a young female presentation. We found a significant minority of these patients have other GI diagnoses. The data supports the recommendation that patients presenting with self-reported NCGS should only be confirmed as having NCGS after excluding other gastrointestinal disorders. We have also demonstrated from our data that a similar approach is required in patients with IBS ‘type’ symptoms. One strategy that may help would be the utilisation of the Salerno experts’ criteria in reaching a firm and positive diagnosis of NCGS which would in turn improve the validity of the results published . Whilst


This presentation will describe the creation and findings from an interprofessional curriculum in behavioral health developed by social work faculty for medical students. Training in behavioral health is needed more than ever during a time of increased isolation and fear during the COVID pandemic. Older adults with untreated behavioral health concerns are a vulnerable population, which can result in negative effects, including emotional distress, reduced physical health, increased mortality, and suicide . Healthcare is increasingly complex with a need to focus on the physical, social, and behavioral aspects of daily living, and providers are realizing the importance of interprofessional collaboration. Towards that aim, I created a module for 4th year medical students in mental health and older adults, which is now part of their medical education curriculum. I will present outcomes in: (1) satisfaction; (2) acquired knowledge and skills (post-test); (3) application of knowledge and skills (pre-post competency assessment and comfort around asking about depression); and (4) patient outcomes . Feedback from the 143 medical students is positive with 95% strongly agreeing or agreeing that this expanded their knowledge and understanding in mental health issues among older adults. At baseline, 17% of medical students were moderately to very comfortable in asking questions on the GDS compared to 42% at post-assessment. After completing the course, almost 25% of medical students made a referral to social work during their rotation. This collaboration resulted in curriculum that is both rigorous and impactful.


High volatility and inertness were predicted for Fl due to the strong relativistic stabilization of the closed 7p1/2 sub-shell, which originates from a large spin-orbit splitting between the 7p1/2 and 7p3/2 orbitals. One unpaired electron in the outermost 7p1/2 sub-shell in Nh is expected to give rise to a higher chemical reactivity. Theoretical predictions of Nh reactivity are discussed, along with results of the first experimental attempts to study Nh chemistry in the gas phase. The experimental observations verify a higher chemical reactivity of Nh atoms compared to its neighbor Fl and call for the development of advanced setups. First tests of a newly developed detection device miniCOMPACT with highly reactive Fr isotopes assure that effective chemical studies of Nh are within reach.Nihonium  and flerovium  are the first superheavy elements in which the  Z ≥ 104  with atomic number Z ≥ 104 . GeneralZ ≥ 104 . As SHE  or less . Chemica or less . These ement 118 .7s2 and 7p1/22, respectively  and Fl (element 114) due to a large relativistic stabilization of the outermost spherical orbitals ectively . Locatedgroup 13 . The lar -0.7 eV . Adsorpt -0.7 eV .2 and Au. Relying on the expected high volatility and weak chemical reactivity, the gas-solid chromatography method was used for Cn and Fl adsorption studies, mainly on Au surface shells in Cn and Fl  and Ts (element 117) isotopes, respectively 288Mc has a higher cross section of about 10 pb  at the GSI Helmholtzzentrum für Schwerionenforschung (GSI) in Darmstadt, Germany. The results of all previous studies on Nh chemistry call for further optimization of the existing techniques to facilitate future gas-chromatography experiments on Nh and, possibly, Mc.Nh isotopes for chemistry studies can be produced as decay products following nuclear fusion reactions between ectively . The nucut 10 pb . The sec studies . Followi243Am or 249Bk targets were irradiated with 48Ca beams. Because of the limited availability of the 249Bk target material, most experiments were performed by irradiation of 243Am targets. The main product of those irradiations was 288Mc, produced in the 3n-evaporation channel following the complete fusion reaction. The SHEs of interest were thermalized in a He/Ar gas mixture directly behind the target and transported by the gas flow to a detection setup. The transport line from the chamber, where the ions recoiling from the target were thermalized, to a detection setup was 4 m or longer in all experiments performed at FLNR. Since the half-life of 288Mc (T1/2 ≈ 170 ms) is too short for the applied technique, the chemical experiments were focused on adsorption studies of its longer-lived α-decay product, 284Nh (T1/2 ≈ 1 s), on a Au surface. The Au-covered PIN photodiodes formed a detection channel, consisting of one or two detector arrays kept either at constant ambient or lower temperatures, or with a negative longitudinal temperature gradient. In all these experiments without pre-separation, an insert made of quartz was installed in the recoil chamber to prevent collisions of thermalized products with the metallic walls. In addition, a hot quartz-wool filter was installed to avoid the transport of non-volatile species by aerosol particles, which can be formed by the intense beam interacting with the recoil chamber back wall.In the past, a series of chemical experiments with Nh were conducted by a collaboration led by FLNR . In tota48Ca ion beam and 243Am (249Bk) recoiling from the target, i.e., primarily 288Mc (294Ts), were thermalized inside a recoil chamber (1) placed directly behind the target or in a Recoil Transfer Chamber (RTC) at the focal plane of the Dubna Gas-Filled Recoil Separator (DGFRS) or of TASCA. Quartz was inserted inside the recoil chamber in the experiments without pre-separation (A), where the beam traversed through the gas before it was stopped in a beam stop (2). To prevent the break-through of aerosol particles, a quartz wool plug heated to 600°C (3) was installed at the exit of the recoil chamber. In experiments behind a preseparator, 288Mc ions penetrated a window separating the RTC volume from the separator volume. Two detector arrays, (4) and (5), placed in a row were used in the experiments . The thickness of the SiO2 and Au layers on the detector surface was 30–50 nm. The recoil chamber (or RTC) was connected to the detection setup by PTFE capillaries of different lengths kept at different temperatures, as is indicated in The products from the fusion-evaporation reaction between the ents see : A) a Auents see ; B) a Auents see ; C) two 284Nh were found in the first three runs at Dubna using 243Am targets although observation of 10–20 decay chains from 284Nh had been expected based on the known efficiency and a total beam integral of 1.35.1019 particles   .α-decay spectra from volatile byproducts of multi-nucleon transfer reactions, the data quality in the experiments without pre-separation hampered the safe identification of Nh and its daughters  > 45 kJ/mol was concluded based on the non-observation  simulations using the mobile adsorption mechanism . The time elapsing between the start of each beam pulse and the observed decay-in-flight of Hg isotopes over the first 16 SiO2-covered detector pairs was registered. This measurement allowed determination of the flush-out time distribution. The measured flush-out time distribution and the integrated fraction of flushed-out 182,183Hg atoms as a function of time are depicted in 284Nh (T1/2 ≈ 1 s) due to decay during their transfer from the RTC volume into COMPACT were estimated not to exceed 50%.Besides losses due to irreversible adsorption before reaching the detectors, the rate of observed atoms may be reduced due to nuclear decay during transport. These losses depend critically on the time required for transporting the atoms from the production site to the detector; in our case, the COMPACT arrays. Transit times through TASCA are on the order of 1 μs and are negligible compared to the time needed for flushing out the atoms from the RTC to COMPACT. The transport time to the detection setup was measured with short-lived 2 chromatography channel. The travelling time of the weakly-interacting Hg along the 16 cm-long SiO2-covered detector array was estimated to be 50 ms at ambient temperature.The measurement of decay-in-flight of Hg isotopes in the first detector array with time and position resolution enabled us to determine the retention time of Hg in the SiO2 and Au surfaces was next targeted. The first attempt at adsorption studies of Nh at TASCA was performed in 2016. A pulsed 48Ca+10 beam  from the UNILAC accelerator with a beam energy of 5.47 MeV/amu (before the Ti target substrate) bombarded a 243Am target wheel, which rotated synchronously with the pulsed beam (243Am target segments were deposited on 2.2(1) μm Ti foils by molecular plating (243Am segments was 0.80(1) mg/cm2 of 243Am (isotopic enrichment: 99.7%). A beam integral of 4.4(1)·1018 was collected during the 20-days long irradiation. TASCA was filled with He at a pressure of pHe = 0.8 mbar and set to a magnetic rigidity of B·ρ = 2.21 Tm to center 288Mc recoils in the TASCA focal plane , made of 3.6 μm Mylar™ film on a stainless-steel supporting grid with 80% transparency. Recoiling EVRs were then thermalized in a He/Ar (1/1) gas mixture at 1 bar inside the 48-cm3 large RTC volume and were flushed out to COMPACT detector arrays at a gas flow rate of 2 L/min. The inner RTC wall was covered with a Teflon™ layer. A 6-cm long PTFE tube (i.d. 4 mm) connected the RTC volume to the detection setup, so that atoms thermalized inside the RTC encountered only non-metallic surfaces before they entered COMPACT.Building upon successful Fl chemistry experiments at the gas-filled separator TASCA , the gassed beam . Four 24 plating . The aveal plane . The nom2 followed by 16 Au-covered detector pairs. The second COMPACT array consisted of 32 Au-covered detector pairs  terminated by a spontaneous fission (SF) event, were registered. This indicates a reactivity of Nh that is higher than that of Fl on a confidence level of >95% for small numbers. Only one SF event consisting of two coincident fission fragments but without α-decay precursors was detected in the fifth detector pair of the first COMPACT array, on the SiO2 surface. The probability for the detection of a single α decay is about 80%. Thus, the probability that the SF event originated from the long decay chain beginning in 288Mc or 284Nh is almost negligible. However, it could originate from shorter decay chains, which end by the spontaneous fission of Nh or Rg isotopes; according to the nuclear decay properties given in  was determined (2 and H2O) were kept at a level of a few ppm, yet gas-phase reactions between Nh atoms and gas impurities cannot be completely excluded. In that case, the formation of NhOH could be expected, which is predicted to be non-volatile  of Nh (7.306 eV) (IP(Mc) = 5.574 eV   is large5.574 eV .2-covered detector channel. PTFE is known as a very inert hydrophobic material, and the adhesion of different chemical species on the PTFE surface is very low  μm Ti foils and had thicknesses of about 0.5 mg/cm2. The inner volume of the RTC was separated from the TASCA volume by a 6-μm thick Mylar® window. The reaction products recoiling from the target were physically separated using TASCA, and thermalized in a He/Ar (1/1) gas mixture inside the RTC. The measured flush-out efficiency of the reactive alkali metal Fr was compared to that of the volatile and relatively inert metal Hg. While the efficiency for Hg isotopes was expectedly higher (∼58%), a substantial efficiency for Fr isotopes (∼30%) was determined. The distributions along the detector channel were similar for both elements following the diffusion-controlled deposition on the Au surface. The lower transport efficiency of Fr can be explained by its higher chemical reactivity, resulting in larger adsorption losses on the inner walls of the RTC and in the connecting slit.The transport efficiency from the RTC into the miniCOMPACT was measured with short-lived 284Nh. The non-observation of decay chains from 284Nh in the experiment behind the DGFRS (284Nh or its progenies. These results called for the development of an advanced setup. We therefore developed the new miniCOMPACT detector array, which does not require any transport line between the RTC and the detection setup. Initial tests of this approach with highly reactive Fr isotopes gave an encouraging value of 30% for the absolute transport yield suggesting that a chemical study of Nh and Mc is now within experimental reach.Elucidating the adsorption behaviour of Nh on different surfaces is currently one of the hottest topics in the field of superheavy element chemistry. Nh has one unpaired electron in the valence shell and is, therefore, expected to be chemically reactive. To date, several attempts to study the interaction of Nh with Au were reported from FLNR, Dubna. The chemical results of the first studies without physical pre-separation  sufferedhe DGFRS , and in