West Nile virus (WNV) is the leading cause of mosquito‐borne illness in the continental United States (CONUS). Spatial heterogeneity in historical incidence, environmental factors, and complex ecology make prediction of spatiotemporal variation in WNV transmission challenging. Machine learning provides promising tools for identification of important variables in such situations. To predict annual WNV neuroinvasive disease (WNND) cases in CONUS (2015–2021), we fitted 10 probabilistic models with variation in complexity from naïve to machine learning algorithm and an ensemble. We made predictions in each of nine climate regions on a hexagonal grid and evaluated each model's predictive accuracy. Using the machine learning models , we identified the relative importance and variation in ranking of predictors  across regions. We found that historical WNND cases and population density were among the most important factors while anomalies in temperature and precipitation often had relatively low importance. While the relative performance of each model varied across climatic regions, the magnitude of difference between models was small. All models except the naïve model had non‐significant differences in performance relative to the baseline model . No model, including the ensemble or more complex machine learning models, outperformed models based on historical case counts on the hexagon or region level; these models are good forecasting benchmarks. Further work is needed to assess if predictive capacity can be improved beyond that of these historical baselines. Historical case counts and population density were the most important variables for predicting West Nile neuroinvasive disease casesNo model, including machine learning models, significantly improved prediction beyond that of historical patterns in casesWe found variability in the relative importance of climatic anomalies across models and regions, with generally low importance Across the southeast and in urban areas of the southwest, Cx. quinquefasciatus acts as the dominant vector. In the more rural areas of the western United States, the primary vector is Cx. tarsalis. Passerine birds generally serve as enzootic reservoirs and amplification hosts  enables rapid changes in mosquito dynamics and thus transmission rates in response to current climatic conditions. In contrast, passerine birds have longer lifespans . A wide range of models have been used to predict the geographic or temporal distribution of WNV in the United States Barker, , includiWe performed a multi‐model analysis and evaluation of probabilistic predictions of WNND cases across the contiguous 48 states plus Washington DC . We made these annual predictions to assess predictive performance under an early warning framework. We used the machine learning models included in the multi‐model assemblage to identify region‐specific factors that were consistently important for prediction across years.22.1st_make_grid function in sf package (version 1.0–8)  for individual hexes ) to align with our goal of a large‐scale, early warning framework for the predictive modeling.We predicted annual totals of WNND cases across CONUS using an equal‐area hexagonal grid (200 km diameter). We created the grid using an equal‐area projection of the CONUS counties (NAD83) and the Pebesma, . We usedPebesma, . We diviPebesma,   on the county scale .We aggregated human demographic data to each hexagon using a similar procedure as with the WNND case data. Using county‐level demographic data  from the 2010 U.S. Census  data set   which incorporated historical case counts on different spatial scales, but the same temporal scale. The NB‐hex model fitted a NB model to historical cases from each individual hexagon independently and used this fitted distribution to generate probabilistic predictions of cases counts for the prediction year. We used Stan ) in each hexagon. As with the NB‐hex model, we fitted an AR(1) model (We developed a single AR(1)‐Climate model per region which consisted of an AR(1) model (fitted with ln(cases + 1)) with an exogenous seasonal climate covariate , population density, and land use . To capture long‐term trends in historical WNND case series, we included yearly cases lagged 1–5 years prior to the year of prediction. We also included the median annual reported WNND cases for all previous years to capture a not explicitly temporal metric of historical counts  were log transformed ) for fitting both RF and NN models to improve fitting. To investigate the impact of different temporal resolutions of climate data on predictions, we fitted two versions of each type of machine learning algorithm, one with monthly climatic data and the other with seasonal climatic data . As in other models, season were defined as 3‐month aggregations of climatic data .We fitted RF models using the forecastML package in R Redell, , a packantree) and number of variables randomly selected to try at each node (mtry)) and selected the best value for each by comparing pseudo‐R2 and out‐of‐bag RMSE and MAE across all prediction years. We assessed ntree as 10‐, 15‐, or 20‐times the number of input parameters and mtry as one‐third or one‐half of the number of input parameters.We investigated a range of hyperparameters .calculate_intervals function the forecastML package, . Using To quantify the influential variables (“importance”) in each region, we calculated permutation importance Breiman, . Specifi2.2.2.26 iterations. We fitted 250 repetitions per model using randomly initialized connection weights, creating an ensemble of fitted models for each prediction. We calculated in‐ and out‐of‐sample RMSE and MAE for each model fit .We were unable to identify similar R packages developed for NN time series analysis and therefore, developed a workflow similar to that of the RF algorithms. Using the neuralnet package in R  based on comparisons of a subset of individual models using combinations of the above hyperparameters. For this, we fitted unique models to predict 2015–2018 in each climate region and compared in‐ and out‐of‐sample RMSE and MAE, selecting the combination of hyperparameters that minimized out‐of‐sample RMSE and MAE. We tried networks with the number of hidden nodes equal to two‐thirds, one‐half, or the square root of the number of input nodes. We considered sigmoid and softplus activation functions for the hidden layer and linear and leaky rectified linear unit  activation functions for the output layer. Thus, we used a cross‐validation scheme across unique models fit across different lengths of training data to select the final models. We used a subset (4 years) of the full period (7 years) for selecting the final model construction.The final model had the following hyperparameters: the number of nodes in the hidden layer was equal to the square root of the number of input nodes, a sigmoid activation function on the hidden layer nodes, and a linear activation function on the output node.Using the above selected combination of hyperparameters, we fitted neural networks for predictions for the full range of years (2015–2021). To generate probabilistic predictions per hex, we fitted NB distributions to 1,000 bootstrapped samples. To generate the bootstrap samples, we added the prediction from one randomly selected ensemble member and a randomly selected training residual from across all ensemble members for that hexagon. As before, we then fitted a NB model to the 23 quantiles of the bootstrapped samples. See Figures To identify variable importance per region, we used Olden's method of summing connection weights per input variable  where pi is the probability assigned to the observed outcome. For each model, we calculated the probability assigned to the observed number of cases using the fitted NB distribution for each hexagon and year. We compared mean logarithmic score per year and for all years per region to rank model performances. For calculating mean score, we truncated individual logarithmic scores to −10 to prevent scores of negative infinity which would occur with zero probabilities .We evaluated model skill and compared performance across models per climate region using logarithmic scores ). Thus, we transformed the outcome to a continuous positive value that could be approximated by a Gamma distribution and was on the same scale as the original logarithmic scores. For logarithmic scores of zero , we converted these to a small non‐zero value (1 × 10−10) between zero and the next smallest score (1.53 × 10−9) as zero is undefined in Gamma distributions. This truncation and the truncation of logarithmic scores to −10 introduced artificial densities at the corresponding surprisal values. However, the Bayesian model interprets these observations as uncertain and the Markov Chain Monte Carlo sampling distributed these observations across a wider range of values.To compare the performance of each model type across regions and given a specific set of variables potentially related to forecasts skill , we used Bayesian GLMs. For the analyses, we converted the logarithmic scores to surprisal values by changing the sign (−log(stan_glm function in the rstanarm package (version 2.21.3)  as ever reported a case  or never reporting a case . Similarly, we dichotomized the number of observed WNND cases in the predicted year into zero cases or ≥1 case (ObsCases). For the regression, we included the logged number of observed case count (ln(cases + 1)), model, histCases, and ObsCases as predictors. We included the main effect of logged cases in addition to the main effects, all double interactions, and the triple interaction of model, histCases, and ObsCases. We estimated the median and 95% Prediction Interval (PI) of the surprisal for each model.We used Gamma distributed (log link) Bayesian GLMs fitted with Stan, using the To investigate the impact of using normalized climate anomalies on estimated variable importance and predictive performance, we fitted an additional machine learning model. We selected the best machine learning model  and re‐fit it using raw values for all climate covariates; all other covariates and model selection procedures remained the same. We compared variable importance and mean logarithmic score between this model and the one fitted with the corresponding climate anomalies.33.1Mean logarithmic scores from each model generally had more variability across regions and years than between models Figure . The Alw3.2Across the regions, most models had similar skill to the baseline NB‐hex model Figure , except For all models, predicted surprisal was highly associated with the number of cases reported, reflecting sensitivity of the surprisal to that number  model used only historical WNV data for the specified locations, while other models were able to leverage climate data. Using single climate variables to assess AR(1)‐Climate models, we found high variability in predictive performance over years and regions  identified as important within regions, and overall, these variables did not improve predictive performance.In general, we did not find any significant difference in skill between benchmark models based on historical WNV data alone and machine learning models fitted with demographic, land use, and climate data. Two models were significantly worse than others, the NB‐nation baseline and the naïve Always Absent models. The Always Absent model does not use any prior information on reported WNND cases while the NB‐nation model considered all historical WNND case data but no geographical information. The poor performance of these models compared to models with hexagon‐specific information indicates the importance of local case data to improve predictions. For the other nine models, which used hexagon‐specific data, rankings based on mean logarithmic score varied between region, but the magnitude of the differences was small, leading to very similar predicted skill across regions. However, the resulting skill of these models indicates there may still be room for improvement in predictive capacity of WNND cases, but the upper limit on achievable skill  is currently unknown.Among the suite of better‐performing models were three baseline models relying only on historical case data: NB‐region, NB‐hex, and AR(1). In a previous, collaborative WNND forecasting project  as has been noted in other forecasting studies  had proportions of positive connection weights near 0.5, indicating disagreement on the direction of effect across individual model repetitions. While it is possible that these indicate trends in WNV transmission beyond simple annual cycles, the uncertainty in the direction of the impact could also reflect collinearity with other variables and lags. Regardless of that uncertainty, the machine learning models, like the NB and AR(1) models, all point to historical patterns as the most reliable known predictor of future WNND cases.Cx. pipiens and Cx. restuans, the dominant WNV vectors in the climate regions where it was most important  while in the west, Cx. tarsalis tend to use agricultural areas for larval breeding sites  where the same land use variable (urban) was identified in the top six variables by both a RF and a NN model. Variation in the estimated importance of these variables across modeling types could reflect differences in the underlying machine learning algorithm and that multiple, inter‐related factors are contributing to WNV transmission ecology. Bowden et al.  identifi Reisen, . HoweverWe did not identify any climatic factors with consistent high importance for WNND prediction within climate regions. We found a general agreement on the relative ranking of climate variables within a region and between season and month versions, but importance was not consistent between RF and NN models. Models with seasonally summarized climate variables often had better skill than versions with monthly variables, but the differences in prediction were not substantive. Additionally, the top climate covariates in the RF and NN models were not consistent with those selected for the AR(1)‐Climate models. While the exogenous covariates for the AR(1)‐Climate models were selected from a somewhat different, yet overlapping set of candidate variables, we would expect to see similar types  selected across models due to the attributed importance of climate to accurate WNV prediction ‐Climate models per region were very similar with little consistency in rankings across prediction years; no single seasonal temperature or precipitation variable was consistently predictive. Thus, while climate factors can modulate WNV transmission, climate variables are not the only driver and the relative influence of climate likely can change between years and locations due to the multi‐faceted ecology of WNV. Previous improvements in WNV prediction with climate have been achieved on smaller spatial and temporal scales , we would not be able to substantially improve WNND forecasts relative to forecasts made using climate from longer lags. In contrast, lagged historical cases up to 5 years often had consistently high importance (rank and relative magnitude). Previous studies using distributed lags (up to 36 months) of meteorological conditions  similarly found significant lagged effects on WNND cases at the county scale, but did not consider lags up to 5 years  ranked highly. In the Southwest and Upper Midwest regions, historical cases, population density, and a landcover (crops for the Southwest and urban for the Upper Midwest) were important. While these variable groups might inform further investigation and improved prediction of regional WNV transmission, we did not find clear patterns between highly important variables and best scoring modeling approaches. Rather, the majority of models had similar performance in each region with variation in their relative order. A machine learning model performed best in only a single climate region (the Upper Midwest). Overall, the NB‐hex and NB‐region consistently performed near the top and should therefore be good benchmark models for regional and sub‐regional  prediction.A strength of our approach was to use a multi‐model framework to evaluate a variety of model constructions with increasing complexity in structure and covariate inclusion using a single standardized performance metric. Additionally, we fitted each of the machine learning models using the same data set so we can attribute differences in performance to algorithms and not data availability. Variable importance identified by the RF and NN models can guide the development of future models including more sophisticated machine learning model architectures like long short‐term memory networks  to calculate climatic anomalies may not have fully captured the local conditions to which mosquitoes and birds were exposed. Also, our assessment of potential mosquito habitat  used a finer resolution (30‐m resolution) and there was no implicit connection with climate to capture the differential conditions in mosquito breeding habitats. Thus, the relatively large hexagons we chose might not reflect these small‐scale variations, making the model sensitive to hexagon size. However, at the relatively large spatial and temporal scale of models, these underlying associations may not be essential to describe the associations in our modeling framework and the large‐scale patterns of climate and land cover are consistent across hexagons. On the other hand, WNND case data were available at the county level, a much coarser scale than the environmental covariates, limiting the hexagon size. Reducing hexagon size could have led to hexagons without cases data, especially in the West due to large county sizes, removing potentially influential portions of the county from analysis. Any data set inherently introduces some bias, and the choice should be dependent on the intended application  could be extremely useful for within season decisions by public health and vector control agencies .5Human infection with WNV results from the complex interplay of mosquito vectors, avian hosts, human populations, and the environment. Using a multi‐model framework (including machine learning models) with a consistent set of covariates to assess prediction of annual cases of WNND on a hexagonal grid within climate regions of CONUS, we identified region‐specific factors that were consistently important for prediction. We found that historical numbers of cases were the most reliable indicator for forecasting future WNND cases regardless of the modeling approach. Models using local WNND case data performed as well or better than more complex machine learning models with more data as well as an ensemble of all included models. In addition to lagged case counts, machine learning models identified population density and land cover as influential in many climate regions. On the other hand, importance of lagged anomalies of climatic features varied across models and generally had low relative magnitudes of importance.Our use of baseline models of varying complexity allowed us to compare the relative performance of included models. While we did not identify a single “best” model for WNND prediction, NB models on the hexagonal or regional levels performed well overall and could be used as benchmark models. Predicted performance of model types across the United States was modulated by both the historical WNND patterns and variation in observed cases from these historical trends. Further work is needed to produce actionable predictions for early warning of WNND cases in the United States. Potential avenues include the use of more advanced machine learning architectures, improved prediction of emergence of cases, real‐time integration of vector surveillance data, utilization of additional surveillance systems, and more local models to guide public health and vector control activities.The authors declare no conflicts of interest relevant to this study.Supporting Information S1Click here for additional data file.


Sexual selection is a key evolutionary force but varies widely between populations. Two key factors that influence sexual selection are the extent to which females copulate with multiple males (polyandry) and variation in the social environment. Increasing research demonstrates populations are structured by complex socio‐sexual networks, and the structure of these networks can influence sexual selection by shaping the relationship between male precopulatory mating success and the intensity of postcopulatory competition. However, comparatively less attention has been dedicated to the influence of group structure on sexual selection and how differences in the size of groups may impact on the relative force of pre‐ and postcopulatory sexual selection in polyandrous populations. The presence of groups  and the size of groups varies widely in nature and forms an implicit part of much experimental sexual selection research under laboratory conditions. Here I use simulations of mating competition within populations that vary in the size of groups they contain, to show that variation in group size, and in particular small groups, can influence sexual selection. Specifically, I show that null expectations for the operation of pre‐ and postcopulatory sexual selection is governed by the size of groups within populations because smaller group sizes constrain the structure of sexual networks leading to reinforcing episodes of pre‐ and postcopulatory sexual selection. Given broad variation in group structure in nature and the tendency for experimental sexual selection research to study replicate small groups, these effects have implications for our understanding of the operation of sexual selection in polyandrous populations. A key goal in sexual selection research is to understand the role that polyandry plays in shaping the strength of sexual selection and the relationship between pre‐ and postcopulatory sexual selection. Here, I use simulations of mating competition within populations that vary in the size of groups they contain, to show that variation in group size, and in particular small groups, influences sexual selection. I show that the operation of pre‐ and postcopulatory sexual selection is governed by the size of groups within populations because smaller group sizes constrain the structure of sexual networks leading to reinforcing episodes of pre‐ and postcopulatory sexual selection. Given the tendency for experimental sexual selection research to study replicate small groups, these effects have implications for our understanding of the operation of sexual selection in polyandrous populations. To inveβM), where βMis calculated as T=βMM+ε, and ε is an error term. A male's SCI can be added to the above regression such that T=βM∙SCIM+βSCI∙MSCI+ε. Here βM∙SCI represents a partial sexual selection gradient on male mating success controlling for variation in SCI and βSCI∙M represents a partial sexual selection gradient on male SCI controlling for variation in male M. In general, sexual selection theory predicts that increasing sperm competition will decrease a male's reproductive success , whereas male mating success increases his reproductive success . As a result, the way in which the Bateman gradient, βM, is affected by variation in sperm competition intensity across males is determined by the slope of the regression of each male's sperm competition intensity on male mating success  such that βM=βM∙SCI+ and therefore counteracting pre‐ and postcopulatory episodes of sexual selection and a reduced βM , and therefore reinforcing pre‐ and postcopulatory episodes of sexual selection and stronger positive values of βM  and his SCI, termed the sperm competition intensity correlation  we now have populations of equal size where males and females mate and compete within group sizes ranging six individuals , 12 individuals , or 36 individuals , comparable to the range of the freely mating group sizes used in experimental studies of sexual selection, and each group size also has range of levels of SCIC. Such a simulation approach allows exploration of different network structures given differences in group size, as well differences in average and variance in mating rates within the network and is commonly applied in studies of assortative patterns of connections in networks  small groups; where all randomized swaps occur only within the original predefined groups of three males and three females, (ii) medium groups; where all randomized swaps occur within groups of six males and six females obtained by merging pairs of small groups, and (iii) large groups; where randomized swaps were made between pairs across the whole population . All randomizations hold constant the mean polyandry and the variation in male and female mating success of the original population. Randomizations were conducted in a stepwise way for each population, one pairwise swap at a time, and retained each newly assorted population and continued until randomizations could no longer increase or decrease the SCIC. This process means that for each mating distribution  following Webster et al. , and therefore a steeper positive Bateman gradient (βM). I therefore also calculated the mean standardized male βM for each population, where a mean standardized Bateman gradient of 1 indicates that a 100% increase in relative mating success results in a 100% increase in relative reproductive success and allows direct comparisons between populations  and the mean standardized Bateman gradient and has been more recently indicated as an accurate measure of the strength of sexual selection on phenotypic traits . Under this scenario sperm competition occurs under a fair raffle where each male has an equal sperm contribution, and therefore represents a simple null model that does not rely on assumptions of male competitive phenotypes  must share their paternity (P) with more rivals, reducing the covariance between M and P, and consequently reducing male Bateman gradients . Specifically, by limiting mating to among only a few individuals, smaller groups restrict SCIC to more negative values , even for populations with the same mean and variance in mating rates. This effect is similar to limitations in the diversity of social interactions in social networks , null expectations for patterns of SCIC and COVMP are impacted by group size alone. The impact of SCIC on COVMP then has downstream consequences both for Bateman gradients and therefore ultimately the maximum potential selection on precopulatory traits . These results therefore provide null expectations for the relationship between pre‐ and postcopulatory episodes of sexual selection and show that null expectations vary based on group size alone. Appreciating such null expectations is likely crucial to avoid misinterpretations when extrapolating patterns observed in small groups to larger groups and when comparing between study systems and experiments using different group sizes, particularly as positive values COVMP of may arise purely due to small group sizes.As a consequence of limiting patterns of mating assortment toward more strongly negative values, populations with small groups consistently demonstrated a higher, more positive COVMP in small replicate polyandrous groups, have typically identified either positive or weakly positive values of COVMP . Such effects may accentuate the reinforcement of traits such as male social dominance that have been shown to be favored by both pre‐ and postcopulatory sexual selection in several species such as cockroaches and in small groups of fowl  explored polyandrous groups of five individuals, in which variation in mating success was quickly eroded as every individual had soon copulated with every partner in both male and female roles in the early stages of the experiment , individual groups of a population subdivided into units of three males and three females in some cases reached saturation of the mating matrix with up to 88%–100% of possible mating pairs copulating in only 6 days  across all levels of polyandry. Moreover, these results also show that as polyandry increases, the range of both the Jones index and COVMP) can be non‐zero and secondly that these null expectations vary depending on the group structure of populations . This suggests that future research investigating the relationship between pre‐ and postcopulatory episodes of sexual selection and their contribution to sexual selection, should generate null expectations that may be non‐zero and assess observed patterns in the context of these null expectations. Ideally, careful experiment should complement the use of null models. For instance, while mating patterns may not differ in comparison with random networks, experiment may more effectively assess the extent to which observed patterns are a result of random processes versus counteracting non‐random behavioral mechanics. Exploring sexual networks in systems that allow manipulations that exclude individual competitive mechanisms, such as those that prevent males from delivering mating plugs, or block the impact of male seminal fluid proteins, may be particularly useful ; formal analysis (lead); funding acquisition (lead); investigation (lead); methodology (lead); visualization (lead); writing – original draft (lead); writing – review and editing (lead).The author declares no competing interests.


We report on a 2‐year‐old boy whose initially inconspicuous skin lesions later on evolved into a typical clinical presentation of benign cephalic histiocytosis (BCH). The diagnosis of BCH can often be made on clinical grounds without the need for an extensive diagnostic work‐up. Given the benign and self‐limited course of the disease treatment is not required and the clinical management can be limited to a watchful waiting approach. A case with typical clinical presentation of benign cephalic histiocytosis. Five months later the number of lesions had increased and the macules had evolved into monomorphic flat reddish brown papules. , the typical clinical morphology and the predominant distribution in the head and neck region. However, contrary to its original designation as BCH, several recent case reports have shown that extracephalic skin involvement, particularly on the upper trunk, is also commonly found.BCH is a benign disease without systemic involvement that runs a self‐limited course. The lesions usually resolve within 50 months with occasional mild atrophy and hyperpigmentation.Differential diagnosis primarily includes the two prevailing types of NLCH, JXG, and GEH. JXG is characterized by many small pink to red–brown, dome shaped papules scattered on the upper part of the body that rapidly become yellow (micronodular JXG) or by a few nodular lesions (large nodular JXG). JXG mostly occurs in infants or young adults and may be associated in approximately 5% of pediatric cases with extracutaneous involvement especially of the eyes, liver, and lung. An association with neurofibromatosis I, juvenile chronic myelomonocytic leukemia, and LHC has also been described. GEH typically affects adults and presents as recurrent crops of generalized numerous red to brown papules with a widespread distribution. GEH can be associated with hematological neoplasia. Clinical and histopathological distinction between an early phase of small nodular JXG or GEH and BCH may occasionally be difficult or even impossible. Thus it has been proposed that these diseases might be variants of a single clinical entity.In the present case the diagnosis of BCH was based on clinical criteria that included a disease onset in early childhood (on average 15 months), the typical clinical morphology of the lesions and their predominant distribution in the head and neck region. A skin biopsy is generally not required and should be reserved for doubtful cases to allow for differentiation from other types of cutaneous histiocytoses.In summary, our case brings to attention the clinical characteristics of BCH and points to the fact that BCH may not be confined to the cephalic region. We also discuss the distinction of BCH from other types of NLCH and underline the benign course of this disease, which does not require an extensive diagnostic work‐up but only a regular clinical monitoring.Sandra Osorio: Conceptualization; investigation; validation; writing – original draft; writing – review and editing. Lucie Harpain: Investigation; validation; visualization; writing – review and editing. Karin Jahn‐Bassler: Investigation; validation; writing – review and editing. Adrian Tanew: Funding acquisition; project administration; supervision; validation; writing – review and editing. Sonja Radakovic: Conceptualization; funding acquisition; investigation; project administration; supervision; validation; visualization; writing – original draft; writing – review and editing.None.All patients in this manuscript have given written informed consent for participation in the study and the use of their de‐identified, anonymized, aggregated data, and their case details (including photographs) for publication.


Procedural anxiety is a concern for a number of patients undergoing radiation therapy. While procedural anxiety is often treated pharmacologically, there is a clinical need for effective alternative strategies for patients who are contraindicated from medication use, and those who prefer not to take unnecessary medications.The primary objective was to assess the efficacy of nonpharmacological interventions delivered to adults with cancer, in the radiation oncology department, just prior to, or during radiation therapy, in reducing levels of self‐reported procedural anxiety. The secondary objectives were to assess the efficacy of these interventions in reducing physiological symptoms of procedural anxiety and anxiety‐related treatment disruptions.Systematic review.Electronic databases  were searched from inception up until February 2022.Population: Adult patients with cancer undergoing external beam radiation therapy. Intervention: Nonpharmacological interventions delivered within the radiation therapy department. Comparison: standard care controls, or standard care plus an alternative intervention. Outcomes: level of self‐reported procedural anxiety (primary), physiological symptoms of anxiety (secondary) and measures of anxiety‐related treatment disruptions (secondary).Two reviewers independently extracted data. A meta‐analysis was originally planned but deemed not feasible as the studies could not be confidently pooled for meta‐analysis, due to the variability in the interventions, study designs and the generally low number of studies. Therefore, a narrative synthesis is presented.Screening of 2363 records identified nine studies that met inclusion criteria: six studies of music interventions, two of video‐based patient education and one of aromatherapy. Overall, three studies received a global rating of strong methodological quality and low risk of bias. Three studies reported a significant effect of the intervention on reducing the primary outcome of self‐reported procedural anxiety: two music interventions , and one video‐based patient education . One of the studies (a music intervention) also reported a significant reduction in the secondary outcome of physiological symptoms of procedural anxiety (systolic blood pressure).The evidence for nonpharmacological interventions delivered to adults with cancer just prior to, or during radiation therapy, in reducing levels of self‐reported procedural anxiety is limited, with very few well‐designed studies. There is a need for interventions for procedural anxiety during radiation therapy to be evaluated through rigorous randomised controlled trials. The purpose of this review is to summarise the evidence of nonpharmacological interventions targeting procedural anxiety in patients undergoing RT. Specifically, this review will focus on interventions that could feasibly be incorporated into existing procedures within the RT department, offering maximum utility for healthcare providers who manage patient anxiety. There were nine studies that met criteria for inclusion: six studies of music interventions, two of video‐based information‐provision and one of aromatherapy. The evidence for nonpharmacological interventions delivered to adults with cancer, just prior to, or during radiation therapy, in reducing levels of self‐reported procedural anxiety is limited, with very few well‐designed studies. This is despite the high prevalence of procedural anxiety, its potential implications (e.g. treatment interruption and patient distress) and the increasing number of studies examining nonpharmacological interventions to address procedural anxiety. A summary of the evidence is needed in order to guide research and clinical practice. The purpose of this review is to summarise the evidence of nonpharmacological interventions targeting procedural anxiety in patients undergoing RT. To provide healthcare providers with maximum utility in managing patient anxiety, this review will focus on interventions that can be feasibly integrated into current procedures within the RT department . It endeavours to address the needs of all cancer care centres, including those lacking access to psycho‐oncology services as well as those that face substantial wait‐lists.1.1iAssess the efficacy of nonpharmacological interventions delivered to adult patients with cancer, in the radiation oncology department, just prior to, or during RT, in reducing levels of self‐reported procedural anxiety.The primary objective of this review was to:iiPhysiological symptoms of procedural anxietyiiiAnxiety‐related treatment completions and durationThe secondary objectives of this review were to assess the efficacy of nonpharmacological interventions delivered to adult patients with cancer just prior to, or during RT, in reducing additional measures of procedural anxiety:2This review is registered within the PROSPERO database (registration number CRD42019112941) and is being reported in accordance with guidance provided in the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) statement.2.1Due to the lack of clarity and use of the specific term ‘procedural anxiety’ in the literature, for the purposes of this review, studies were deemed to be addressing procedural anxiety if they aimed to: address anxiety; the intervention occurred during, or just prior to RT, and within the RT department; and a standardised and valid measure of anxiety was used (e.g. the State Trait Anxiety Inventory (STAI) State subscale (STAI‐S), which is commonly used to measure procedural anxiety).2.1.1Included studies were those involving adults with cancer currently undergoing, or about to undergo external beam RT, with or without concurrent chemotherapy. Studies involving only paediatric patients (under 18 years) or carers only were excluded. Participants undergoing brachytherapy were also excluded, as the procedure for brachytherapy was considered too different to external beam RT to generalise any findings.2.1.2The primary criterion for the inclusion of nonpharmacological interventions was feasibility for implementation in real‐world clinical settings. The criteria were based on the clinical expertise of the authorship team (particularly the senior radiation therapist (SO)) and included interventions which could be readily implemented with little notice (as anxiety is often not identified prior to treatment)Eligible interventions were those that could be delivered within the department of radiation oncology, at an existing appointment and able to be delivered by any healthcare provider within the RT department . We excluded interventions that: were delivered by licenced mental health providers; were delivered outside of the RT department; required an additional appointment (which included group interventions); and interventions that required extensive additional training for the healthcare provider to deliver . Interventions that exceeded 5 min were excluded, unless they were minimally resource intensive. For example, music listening for 15 min in the waiting room, was included, as participants were able to do this alone or alongside treatment (if music was delivered during treatment sessions). However, an education session delivered by a clinician of the same duration was deemed ‘not able to be delivered in usual appointments’, as this type of intervention requires additional resources from staff.2.1.3Studies of usual care with no intervention controls, or usual care plus an alternate intervention comparison group(s) were included. Studies without a comparison group were excluded.2.1.4Studies using any of the following outcome measures were included in this review. Studies that included any of the secondary outcomes did not need to include any of the primary outcomes to be considered eligible for inclusion.Any validated self‐report measure of anxiety, for example, STAI total score,Physiological measures of anxiety symptoms, including heart rate, respiratory rate, heart rate variability, electroencephalogram, skin conductance and stress hormonesValidated measures of psychological distress, such as the distress thermometerMeasures of anxiety‐related treatment disruptions, for example, treatment completion (or treatment termination due to anxiety) and duration of treatment appointments2.1.5Study designs with a comparison group were all eligible for inclusion in the review. Observational studies and qualitative studies were excluded.2.22.2.1The following electronic databases were searched for potentially eligible articles published from inception until February 2022: CINAHL, MEDLINE, EMBASE, PsycINFO and Cochrane Central Register of Controlled trials (CENTRAL). The Medline search strategy Appendix  was adap2.2.2Reference lists of included articles.Complementary Therapies in Medicine, Supportive Care in Cancer, PsychoOncology and Clinical Journal of Oncology Nursing.A hand search of articles published between 2018 and 2022 in the following relevant journals A hand search of conference abstracts from the 2019 and 2021 International PsychoOncology Society conference (IPOS). It should be noted that 2020 and 2021 are considered the ‘two previous years’ consistent with the published protocol.A grey literature search using Google Scholar (including first 200 records).Other sources that were searched included:2.3There was no restriction on the year of publication or language. Studies with no full text available were excluded. Articles that did not report original data  were also excluded.2.4Cochrane Handbook for Systematic Reviews of Interventions.Two authors  independently extracted the data using a prepiloted data extraction form, which was based on recommendations by the 2.4.1Authors, year and journalParticipant inclusion criteria, study design and healthcare settingPatient demographic characteristicsIntervention characteristicsComparison groupSelf‐reported measures of anxietyPhysiological measures of anxiety symptomsValidated measures of psychological distress Treatment completion/durationThe following information was extracted:2.5Two reviewers  independently reviewed the included studies and assessed the methodological quality and risk of bias using the Effective Public Health Practice Project Quality Assessment Tool (EPHPP).The two reviewers also reviewed the included studies to assess reporting according to the TiDieR checklist.2.6Due to the heterogeneity in the intervention types, we did not pool all included studies for a meta‐analysis. We also did not pool studies with the same intervention type because of the small number of available studies and heterogeneity in study designs and outcome measurements. Therefore, findings are reported narratively.In line with recommendations by the Synthesis Without Meta‐analysis (SWiM) in systematic reviews: reporting guidelineResults for eligible studies are also reported in a Forest plot  approach,33.1After duplicates were removed, 2363 citations were screened for eligibility. Nine papers reporting results of nine studies met inclusion criteria and are included in this review, with a total sample of 995 participants. The full details of papers identified, screened and included and presented in the Preferred Reporting Items for Systematic Reviews and Meta‐Analyses (PRISMA) flow diagramn = 38) or unable to be delivered by any healthcare provider (n = 10); studies reported in conference abstracts that were not published in a subsequent full report (n = 27); ineligible study design (n = 10); ineligible patient population (n = 8); and not delivered in the RT department (n = 8).The primary reasons for exclusion were as follows: interventions that were unable to be delivered in usual appointments n = 2)n = 1).Three types of intervention were identified in the included studies: music (n = 4)n = 2).n = 2)n = 4)n = 3)n = 3)n = 4)n = 2).The music interventions could be broadly divided into those using self‐selected (participant chosen) music (n = 200). Participants randomised to the music group were given an opportunity to choose from a selection of old songs , and then instructed to sit in the waiting room prior to treatment and listen to the selected songs through headphones, for 15 min. Participants in the control group were instructed to sit comfortably on the radiology waiting room couch (a separate room from the music therapy group but similar facility) and to rest for 15 min. The study found a significantly greater reduction (p < 0.001) in self‐reported anxiety (mean decrease of 7.19 on the STAI‐S) in the music group participants when compared to the control group (mean score decreased by 1.04) after the intervention with a medium effect size of 0.52. The findings from this study also support a clinically meaningful reduction in procedural anxiety (−7.19 decrease on the STAI‐S) for those in the music group, with the mean anxiety score (35.44) in the normal range , participants randomised to the intervention group selected their preferred genre of music from a directory of prerecorded music, while intervention participants in the study conducted by O'Callaghan and colleaguesN = 100) were asked to bring their own music. The control groups in both trials received no intervention. O'Steen and colleaguesN = 42) trialled a music listening intervention delivered during the RT simulation appointment, as well as daily treatment sessions. Participants in the experimental group listened to music of their choice from a selection of music provided by the researchers. Participants in the control group did not listen to music and received the standard care of the department. In contrast to the trials conducted by O'SteenA further three studies trialled music interventions that involved some level of music self‐selection. However, in contrast to Chen and colleagues,N = 60) and Raglio and colleaguesN = 60). In the study conducted by Karadag and colleagues,p = <0.001) in the music group  compared with the control group (mean score increased by 1.0) after the intervention (at 5 weeks) with a moderate effect size of 0.58. The findings also support a clinically meaningful reduction in anxiety of 2.6 on the primary outcome measure (HADS‐A), a reduction that exceeds the minimum clinically important difference of 1.7.The two trials of predetermined music selection were conducted by Karadag and colleagues,Raglio and colleaguesN = 40) trialled a 2–3 min video showing patients what to expect in treatment. The video described the simulation process with explanations about the reason for immobilisers, how the patient may feel and the movements of the treatment machine. Similarly, Koth and colleaguesn = 78) trialled a 5 min educational video with similar content. The video included a demonstration of making the thermoplastic mask, CT simulation, radiation planning and the delivery of radiation treatments, including undergoing a mock treatment delivery and explaining the experience. The control groups in both trials received standard education. Esen and colleaguesp < 0.001) in anxiety after viewing the video‐based patient education, and after the first treatment session when compared to the control group (mean difference of 10 points on the STAI‐S). Koth and colleaguesTwo randomised trials examined the effect of video‐based patient education on patients' procedural anxiety. Esen and colleaguesN = 313) to determine whether aromatherapy reduced anxiety during radiation therapy. Participants were randomised to receive a fragrant placebo, a nonfragrant placebo or pure essential oils . Three drops of oil were applied to a paper bib that participants wore for the duration of their treatment each day. The study found a significant reduction in anxiety (p = 0.04) in the nonfragrant placebo group compared to the essential oils and the fragrant placebo (odds ratios 2.8) and concluded that aromatherapy did not reduce anxiety.Graham et al3.1.4n = 2)n = 7).Two interventions were delivered by a member of the research team 3.3n = 4) received a rating of weak.Individual ratings and the global rating of methodological quality and assessment of risk of bias are reported in Table 3.4Using the GRADE approach4This review aimed to assess the efficacy of nonpharmacological interventions delivered to adult patients with cancer, just prior to or during RT, in reducing procedural anxiety. As far as we are aware, this is the first systematic review to do so. The review identified nine studies that met the inclusion criteria, of which six reported the results of trials of music,This review identified some promise of music interventions in reducing procedural anxiety in patients undergoing RT. Of the two studies that reported significant findings,The remaining studies of music reported no significant effect on procedural anxiety. Like the two aforementioned studies, the delivery of these interventions varied, with no clear differences from those that reported significant findings. However, three≥ 40). Therefore, it is entirely possible that the intervention may have been more effective for those experiencing anxiety in the nonclinical range, resulting in a greater reduction in the outcome measure for the intervention group.This review identified equivocal findings in the studies trialling video‐based patient education. While no effect was reported in the study conducted by Koth and colleagues,This review identified only one trial of aromatherapy,It is worth noting some limitations of the interventions included in this review.Overall, none of the interventions screened participants for clinically significant anxiety as part of eligibility for study participation. It is possible that samples with preintervention levels of anxiety in the normal range could prevent a measurable reduction in anxiety. Additionally, the study by Karadag and colleagues excluded women with left‐sided breast cancer in this study. This criterion was due to the RT treatment protocol for left‐sided cancers, which involves a breath‐holding technique, precluding the use of a music listening during the treatment delivery. As this intervention was only trialled on women with breast cancer, the exclusion of women with left‐sided cancers is quite a significant limitation. While the delivery of the intervention inside the treatment bunker produced a statistically significant and clinically meaningful reduction in anxiety, the suitability of this intervention is dependent on the RT treatment protocol (e.g. precluding those having treatment for left‐sided breast cancer) and therefore limits the clinical utility of this intervention.There is considerable scope for further innovative research in this area, given the prevalence and impact of procedure related anxiety in the RT setting. Further examination of music interventions and video‐based patient education is warranted using large, rigorous trials. It is worth noting the clinical heterogeneity among the trials included in this review, between those that reported an effect of their intervention and those that did not. It is possible that some interventions may be more suited to certain populations. Future research should endeavour to identify the key elements of these interventions for different clinical populations. Additionally, future trials should be designed to only provide interventions to patients who report raised anxiety levels. If patients do not report anxiety at baseline there is no scope for an intervention to reduce anxiety scores (floor effects) and this may obscure any beneficial effects of an intervention.Another useful avenue for further research would be a review of psychological interventions to complement the current review. A systematic review of the literature of psychological interventions for people with head and neck cancer identified 21 intervention studies between 1980 and 2017.We note that patients experiencing clinically severe anxiety, for example a specific phobia which is inhibiting their ability to undergo treatment, are likely to require additional specialised assistance from a trained psychologist. Case studies of cognitive behavioural therapy including graded exposure therapy/systematic desensitisation offer a promising avenue in this regard.4.1There was a high degree of heterogeneity between studies and variability in validated tools used, limiting our ability to confidently pool any data. The inconsistency in findings, and the overall very low rating of the evidence precludes any firm conclusions. A key limitation of this review is that it is possible some of the interventions included in the review may be treating generalised anxiety. The term ‘procedural anxiety’ is not used consistently, so we chose to focus on studies that target anxiety with interventions just prior to or during RT, using standardised measures typically used to measure procedural anxiety.4.2Overall, there is no clear evidence base on which to guide clinical practice. There are very few well‐designed studies of promising interventions, precluding any specific recommendations regarding interventions to reduce procedural anxiety in the RT setting.5These findings highlight the paucity of good quality evidence regarding nonpharmacological interventions targeting procedural anxiety in the RT setting. While there is limited evidence of promise in music and education‐based interventions, it is unclear which elements of these interventions contributed to a reduction in procedural anxiety. There is significant scope for improvement in this area, especially improved methodological quality and reduced heterogeneity. Future research should not only seek to determine which key elements of interventions are important for a therapeutic effect in defined clinical populations, but also explore novel interventions that have shown promise in reducing anxiety in other medical settings.Erin Forbes: Conceptualization (lead); formal analysis (lead); methodology (lead); project administration (lead); writing – original draft (lead); writing – review and editing . Amanda L Baker: Conceptualization ; methodology ; supervision ; visualization ; writing – review and editing . Ben Britton: Conceptualization ; methodology ; supervision ; visualization ; writing – review and editing (supporting). Kerrie Clover: Conceptualization ; methodology ; supervision ; visualization ; writing – review and editing . Eliza Skelton: Data curation ; formal analysis ; writing – review and editing . Lyndell Moore: Data curation ; methodology ; writing – review and editing . Tonelle E. Handley: Data curation ; methodology ; writing – review and editing . Sharon Oultram: Investigation ; methodology ; writing – review and editing . Christopher Oldmeadow: Formal analysis ; writing – review and editing . Alison Gibberd: Formal analysis ; investigation ; writing – review and editing . Kristen McCarter: Conceptualization ; methodology ; supervision (lead); visualization ; writing – review and editing .The authors have no conflicts of interest to declare.Appendix S1Click here for additional data file.


The growing world population and global increases in the standard of living both result in an increasing demand for food, feed and other plant‐derived products. In the coming years, plant‐based research will be among the major drivers ensuring food security and the expansion of the bio‐based economy. Crop productivity is determined by several factors, including the available physical and agricultural resources, crop management, and the resource use efficiency, quality and intrinsic yield potential of the chosen crop. This review focuses on intrinsic yield potential, since understanding its determinants and their biological basis will allow to maximize the plant's potential in food and energy production. Yield potential is determined by a variety of complex traits that integrate strictly regulated processes and their underlying gene regulatory networks. Due to this inherent complexity, numerous potential targets have been identified that could be exploited to increase crop yield. These encompass diverse metabolic and physical processes at the cellular, organ and canopy level. We present an overview of some of the distinct biological processes considered to be crucial for yield determination that could further be exploited to improve future crop productivity. https://www.cropbooster‐p.eu/). In this review, we present an overview of some of the distinct biological processes considered to be crucial for yield determination and recent updates in the respective fields. In the future, these could further be exploited to improve crop productivity.The manuscript is part of a review collection from the CropBooster‐P project ( Economic growth will enrich this population, which will likely lead to increased overall food consumption. Besides the increased demand for food and animal feed, there will also be an increasing pressure arising from competing uses for agricultural products, for example to allow a transition from a fossil‐based towards a bio‐based economy and to limit global climate change through sustainable energy supplies  and the partitioning of acquired resources can be closely related to yield potential in these conditions. Hence our selection of sub‐traits is based on this principle. Future perspectives are presented for each of these areas.In this review, we present an overview of the key biological processes underlying crop yield potential that could contribute to the futureproofing of our current crops and that could be further exploited to improve crop productivity and safeguard future food security Figure . More sp22) and water (H2O) into oxygen (O2) and carbohydrates. The carbohydrates produced are used by the plant for growth and development. In addition, carbohydrates provide precursors for a range of diverse molecules including hormones, lipids and amino acids, and thus actually underpin all aspects of plant metabolism. Despite its importance, in agriculture photosynthesis has a ‘real world’ efficiency well below its theoretical maximum  experiments indicate that raising photosynthetic performance may increase crop yields , and the enzyme PsbS, resulted in increased leaf CO2 uptake and plant dry matter productivity and yield of 14‐25% in the field  irradiance to light‐limiting irradiance, due to, for instance, clouds and self‐shading  is particularly slow  increases following a change from low‐ to high‐light conditions in wheat , thereby driving electron transport. At the top of the canopy, more light energy is often absorbed than can be used in the photosynthesis process  complex connects electron transport from PSII to PSI and is under non‐light‐limited conditions the rate‐limiting step in the electron transport chain. When photosynthesis is not light‐ or rubisco‐limited or limited by the regeneration phase of the Calvin cycle, electron transport (and thus the cyt b6f complex) will limit photosynthesis and be the cause of the loss of photosynthetic light use efficiency. Increasing the activity of the cyt b6f complex may therefore also increase the photosynthetic rate  and cyt b6 (PetB) core proteins of the cyt b6f complex , such as C4 photosynthesis  into C4 acids, which are then transported to deeper, gas‐tight, bundle sheath cells where decarboxylation occurs, releasing CO2 for subsequent recapture by Rubisco . Alternative approaches that do not require anatomical changes are to add cyanobacterial, algal or anthocerote CCMs to crop chloroplasts. Unlike the CCM of C4 plants, which relies on a biochemical pump, these CCMs work by means of biophysical CO2 or bicarbonate pumps. In the cyanobacterial CCM, Rubisco is packed within a protein‐bound structure called the carboxysome, while in the algal or anthocerote CCM, Rubisco aggregates to form a structure called the pyrenoid  and trehalose phosphate phosphatase (TPP) genes, was identified as a promising strategy to enhance sink strength and source‐sink interactions  associated with grain size ‐RELATED KINASE 1 (SnRK1), which is a master gene of sucrose sensing. SnRK1 is activated upon C starvation or stress. Its antagonist, the TARGET OF RAPAMYCIN (TOR) kinase is activated upon nutrient supply  from Arabidopsis, SENESCENCE‐ENHANCED 1 (SEE1) from maize, SENESCENCE‐ASSOCIATED RECEPTOR‐LIKE KINASE (SARK) from bean, CYSTEINE PROTEASE (Ghcysp) from cotton and SENESCENCE‐ASSOCIATED GENE 39 (SAG39) from rice have been used to delay leaf senescence and increase plant performances  or by mutating the isochorismate synthase gene SALICYLIC ACID INDUCTION DEFICIENT 2  accelerates senescence and increases nutrient  remobilisation from leaves to developing grains  and ONAC2 genes of rice  the Arabidopsis WRINKLED1 (WRI1) gene that encodes a transcription factor that enhances the expression of genes involved in lipid synthesis, (ii) the ACYL‐COA:DIACYLGLYCEROL ACYLTRANSFERASE1 (DGAT1) gene that promotes the formation of oil bodies, and (iii) the OLEOSIN gene that codes for a coat protein that defines and protects oil bodies results in the production of the ‘high oil’ tobacco lines that contained 15% more TAGs (dry weight) in their leaves  gene encoding a lipase that degrades oil bodies to interrupt the first step of TAG turnover and overexpressed the Arabidopsis thaliana transcription factor LEAFY COTYLEDON 2 (LEC2) in the ‘high oil’ tobacco previously engineered. The LEC2 master regulator of seed maturation and oil accumulation in seeds was expressed under the control of the senescence‐associated promoter SAG12, to minimise negative pleiotropic effects of constitutive LEC2 expression  is down‐regulated, seed size is increased but accompanied with the production of less seeds  the photoperiod, that is the sensitivity to the inductive effect of long days on the transition between the vegetative and the reproductive phase, is mainly governed by the Ppd‐1 genes family located on homologous chromosomes 2, including Ppd‐D1, Ppd‐B1 and Ppd‐A1; and (iii) earliness per se, referred to as the remaining variability independent of vernalisation requirement and photoperiod sensitivity, is the less known component with only one locus mapped as a Mendelian factor located on chromosome 1D (Eps‐D1).Given the more frequent occurrence of extreme weather conditions, altering developmental stages is also a key factor to adapt the crop life cycle to abiotic stress. Although a longer growing season means more photosynthesis, earlier flowering might be an option to avoid heat stress during the grain‐filling period  can induce root aerenchyma formation  and freezing, will need to be met with strategies for resistance, resilience and/or acclimation and better resource  uptake and use efficiency (Gojon et al., No funding was received to support this research.The authors declare that they have no conflict of interest.


Chronic lower limb wounds can be described as having the inability to progress through stages of wound healing. Although 80% of lower limb wounds develop as a result of venous insufficiency, other causes include arterial disease and diabetes. In addition to the sustained impact on quality of life, the chronicity of lower limb wounds presents a significant financial burden to healthcare systems. Self‐management is a fundamental aspect of the long‐term management of chronic illness and its relevance has intensified since the start of the global pandemic. The objective of this systematic scoping review was to define what the self‐management of a lower limb wound entails and explore the interventions available to support patients to self‐manage. A total of seven articles were evaluated. There was limited consensus regarding the definition and components of self‐management in this area. Interventions involved patients participating in additional exercise, wound care, and lifestyle behaviours such as limb elevation and skin care. Only two studies applied theory and only one evaluated participant acceptability of interventions, making it difficult to assess the feasibility of implementation. Although the evidence reviewed provides some insight into the self‐management of a lower limb wound, theoretically‐guided research is needed in this area. Chronic lower limb wounds include, but are not limited to, those caused by venous, arterial, and diabetic ulceration, with venous ulceration accounting for 80% of all ulcersSelf‐management is fundamental to the long‐term management of chronic illness and has been shown to improve clinical outcomes, quality of life and result in fewer hospitalizations.Self‐management of a chronic condition can be simply defined as a person's active participation in their treatment.To address the urgent need for adaptations to healthcare delivery in response to the COVID‐19 pandemic, the UK's National Wound Care Strategy Programme published guidance regarding how healthcare providers can best support patients with lower limb wounds to adopt self‐management behaviours. This included providing patients with the appropriate support via telemedicine, ensuring they are aware of concerns and warning signs in addition to offering compression hosiery or wraps where appropriate.Although the benefits of the continued use of compression hosiery, regular exercise, and limb elevation have been acknowledged, in practice patients often face significant barriers.How is self‐management in lower limb wounds defined?What interventions exist to aid self‐management in lower limb wounds?How are self‐management interventions evaluated and what outcome measures are used?What is the impact of self‐management interventions on outcomes measured?What is the feasibility and acceptability of developing and delivering these interventions?This scoping review aimed to explore what interventions currently exist and to answer the following questions:2This review was informed by Arksey and O'Malley's framework,2.1Studies evaluating a supportive self‐management intervention for adults aged over 18 with a diagnosed lower limb wound/ulcer. Studies of mixed populations were included if the majority of patients (over 50%) presented with a lower limb wound not in the exclusion criteria.Studied including patients irrespective of previous surgical intervention to treat the underlying cause of ulceration.Quantitative and mixed methods studies including randomised controlled trials, cohort studies, case‐control studies, qualitative studies.2.2Sickle cell diagnosis.Diabetic ulceration.Ulcers caused by solely trauma with no other underlying causality.Ulcers caused by vasculitis.Pressure ulcers.Studies related to self‐management interventions in people with any of the following ulcer‐causing conditions (due to the difference in treatment guidelines):2.2.1The search strategy included papers published between the years 2000 and 2021. Previously published Cochrane reviews whose aim was the evaluation of healthcare interventions, self‐management, and lower limb wounds were used to inform the development of the search strategy.An electronic search was carried out using the following databases; CINAHL, EMBASE, Medline, and Cochrane. Additionally, the bibliographies of all studies eligible for inclusion identified by the search strategy were searched for further relevant studies.2.2.2Three authors were involved in reviewing publications for inclusion, LBS, SR & SL. Authors LBS and SR carried out the initial abstract review. Authors to LBS, SR & SL carried out the full‐text reviews and all papers at this stage were reviewed by a minimum of two authors. Where disagreement occurred the third named author was consulted. A breakdown of the study selection process is detailed in Figure 3Of 1038 articles a total of seven met the inclusion criteria for this review  for the intervention group and no change for the control group (mean score = 21 at baseline and 21 at 12 weeks). (When scoring the YPAS participants can score a minimum of 0 and a maximum of 143). Findings were similar when looking at Range of Ankle Motion (ROAM), where the intervention group also showed improvement over time with the overall scores being 29 at baseline and 32 at 12 weeks (Wilks 𝜆 = 0·92 [p = 0.05]) vs a mean score of 23 at baseline and 24 at 12 weeks in the control group. . Although these results are encouraging, the study required 110 participants to be adequately powered to detect change and only enrolled 62. Similarly, although differences in physical activity and movement were found between the control and intervention group in the studies published by Kelechi et alThe impact of interventions on physical activity varied across studies. O'Brien (2017) found limited difference between the intervention and control group in the majority of outcome measures. However, when evaluating the Yale Physical Activity Survey (YPAS), they reported a significant improvement over time in the intervention group, with overall scores being 30 at baseline and 37 at 12 weeks . This benefit was maintained at 18 months, although the difference was smaller and not found to be statistically significant, with 56% of patients in the control arm experiencing a new ulcer compared with 46% in the intervention arm. Similarly, O'Brien et alp = 0.09). As previously mentioned, due to the small number of participants, the study was under powered to detect an effect.Of the articles evaluating interventions aimed at self‐management exercise programmes, the two including wound healing or recurrence as an outcome measure suggest a positive relationship between exercise and healing.When evaluating patient self‐application of compression therapy, Suehiro et alThe evaluation carried out by Mościcka et al3.4.3As outlined in table two, the intervention evaluated by Kelechi et al varied over time, performance was higher for each behaviour at 26 weeks than before receiving the educational programme. Although activity levels and the use of a soap substitute remained fairly stable across all follow‐up time points (pre‐education/baseline/13 weeks/26 weeks), there was a significant decline in the performance of leg elevation, heel raises and squats over time.When exploring the adoption of healthy lifestyle behaviours associated with delaying ulcer recurrence, the intervention trialled by Miller et al included an educational programme which aimed to improve patients' knowledge of venous disease, and recommended management strategies. It also included dedicated time and activities allowing patients to practice recommended care. While the percentage of patients performing all five behaviours Kelechi et al3.4.4Of the two studies reporting QOL outcomes, neither reported differences between the intervention and control group.4This scoping review aimed to explore the self‐management of lower limb wounds and the interventions available to aid patients. Unfortunately, no studies actually defined the concept of self‐management in this context. While the studies provide some insight as to what activities might be classed as self‐management, there is limited consensus on what a self‐management intervention for lower limb wounds should include. Almost all interventions described as self‐management interventions included an exercise component, and education programme included learning on nutrition and hydration. However, the degree to which self‐management activities go beyond what would be considered a normal healthy lifestyle behaviour is unclear.. Furthermore, multi‐component interventions require a comprehensive evaluation of user acceptance.The majority of interventions in this review include more than one component and therefore fulfil the Medical Research Council definition of a complex healthcare intervention, which includes several interacting components.The qualitative literature describing patients' experiences provides additional insight into factors that may influence self‐management. Several studies describe caregivers as providing significant support, either by carrying out wound dressings or providing motivation for healthy lifestyle behaviours,The presented studies demonstrate that there is scope for patients to become more involved in the care of a lower limb wound, but the theoretical and empirical basis for self‐management interventions is weak. There is insufficient evidence regarding the optimal components of self‐management interventions, and for the degree to which patients engage with these. Few studies have evaluated patient or staff acceptability. In an era of healthcare in which we must promote greater self‐management, there is a need for more detailed work in this area, informed by theory, behavioural science, and intervention development frameworks.4.1Although this review was carried out systematically and followed a pre‐determined search strategy a formal protocol was not published and therefore it was not peer reviewed. In addition, although several steps were taken to ensure a robust search strategy there is always the possibility that some relevant studied were not identified. When conducting the analysis the main limitation of this review was the small number of studies identified, making it difficult to fully answer the research questions. Due to the heterogeneity of included studies, it also made it too difficult to carry out a quality appraisal. Lastly, due to the covid‐19 pandemic, the pathway of care for this patient group has changed significantly. This presents a stronger argument for further primary research in this area.L Bolton Saghdaoui, S Lampridou, and S Racaru are funded by pre‐doctoral fellowship awarded from Imperial Health Charity/National Institute for Health Research (NIHR) Imperial BRC and NIHR/HEE. M Wells is supported by the NIHR Imperial Biomedical Research Centre (BRC). The views expressed are those of the authors and not those of IHC, NIHR or the Department of Health and Social Care.No conflict of interest to declare.


Implementation science based health care research is urgently needed for genomic and precision medicine in AustraliaPrecision medicine is a tailored approach to health, incorporating an individual's genetic make‐up, environment and lifestyle, and is a new frontier offering much promise for disease prevention and cure.Box 1It is an exciting time for genomic and precision medicine in Australia. An ever‐increasing proportion of families are receiving accurate genetic diagnoses, access to screening and counselling, and clinical management from publicly funded genomic technologies Box , with otThe translation gap between medical evidence‐based practices and actual clinical adoption is well recognisedBox 2CALD = culturally and linguistically diverse. Figure adapted with permission from O'Connell et al.In a 2021 reportFirst, an effective, adaptable and sustainable model of clinical care for genomic and precision medicine is needed to address the limitations of the current genetics workforce. Second, the paucity of evidence about how best to address barriers to accessing genomic testing in Aboriginal and Torres Strait Islander people,The challenges to implementing genomic and precision medicine in Australia provide an opportunity for translational research informing policy and practice. The relatively new discipline of implementation science is defined as “the scientific study of methods to promote the systematic uptake of research findings and other evidence‐based practices into routine practice, and, hence, to improve the quality and effectiveness of health services”.Implementation science uses theories (explaining mechanisms), models (descriptive processes) and frameworks An implementation science approach can address many of the already identified barriers and gaps in precision medicine Box . Yet stuin vivo gene therapy in Australia for retinal dystrophyOther Australian research groups have demonstrated the outstanding success of genomic care in new genetic diagnoses and management pathways.Another Australian example is the national implementation science evaluation of a mainstreaming initiative to integrate routine genetic testing for breast and ovarian cancer.A systematic reviewTo ensure effective models of genomic care are created, there is an urgent need for local hospital and health service and state‐based genomic medicine implementation research Box . Such reOpen access publishing facilitated by The University of Sydney, as part of the Wiley – The University of Sydney agreement via the Council of Australian University Librarians.No relevant disclosures.Not commissioned; externally peer reviewed.


Various biodiversity indicators, such as species richness, total abundance, and species diversity indices, have been developed to capture the state of ecological communities over space and time. As biodiversity is a multifaceted concept, it is important to understand the dimension of biodiversity reflected by each indicator for successful conservation and management. Here we utilized the responsiveness of biodiversity indicators' dynamics to environmental changes  as a signature of the dimension of biodiversity. We present a method for characterizing and classifying biodiversity indicators according to environmental responsiveness and apply the methodology to monitoring data for a marine fish community under intermittent anthropogenic warm water discharge. Our analysis showed that 10 biodiversity indicators can be classified into three super‐groups based on the dimension of biodiversity that is reflected. Group I ) showed the greatest robustness to temperature changes; Group II  showed an abrupt change in the middle of the monitoring period, presumably due to a change in temperature; Group III (species evenness) exhibited the highest sensitivity to environmental changes, including temperature. These results had several ecological implications. First, the responsiveness of species diversity and species evenness to temperature changes might be related to changes in the species abundance distribution. Second, the similar environmental responsiveness of species richness and cCOD implies that fish migration from lower latitudes is a major driver of species compositional changes. The study methodology may be useful in selecting appropriate indicators for efficient biodiversity monitoring. Understanding the dimensions biodiversity indicators for indicator selection is an important issue in ecology. This study focused on the environmental responsiveness of biodiversity indicators' dynamics as a signature of the dimension of biodiversity. We evaluated the environmental responsiveness of 10 biodiversity indicators time series derived from actual fish community data and found that the 10 biodiversity indicators were classified into three super‐groups. Consider a biodiversity indicator X related to a biodiversity dimension Y. Environmental changes can alter not only the state but also the dynamics (rules of state transitions) of ecosystems  was used. The monitoring area was located 2 km from the discharge outlet of the Takahama nuclear power plant (NPP). The Takahama NPP started operation in 1974 and was shut down for two periods: 4 years from February 2012 to January 2016, and 14 months from March 2016 to May 2017 . These surveys were conducted once a month from January 18, 2012 to April 26, 2019. The fish identification procedure followed that described by Nakabo , we assumed that sRMSEij≥1 is a criterion for deterministic prediction failure. Finally, we obtained a 41 × 41 matrix from the results of mutual predictions .The procedure for nonlinear mutual prediction in our study was as follows. First, 41 sliding windows  consisting of 48 consecutive time points were created from the full time‐series of a focal biodiversity indicator. The size of sliding windows was selected by considering the trade‐off between prediction skill and sensitivity for detecting the timing of changes in dynamics  in over 90% of the windows for eight biodiversity indicators . For Group II, simplex projection had good predictive performance for temporally close windows but not for temporally distant windows , and species dominance pmax). For Group III, simplex projection often failed in mutual predictions, even for windows that were temporally close .Mutual prediction matrices suggested that the 10 indicators could be classified into three groups (Groups I–III) with similar patterns of mutual prediction Figure . For GroS and cCOD; cluster 2, total abundance N only; cluster 3, Shannon's diversity H′; cluster 4, taxonomic diversity δ and Simpson's diversity D; cluster 5, species dominance pmax and Simpson's evenness ED; and cluster 6, Shannon's evenness EH′ and Smith–Wilson's evenness Evar.A hierarchical clustering analysis identified six significant clusters based on similarities in the mutual prediction matrices Figure . Cluster4The study results showed that most biodiversity indicators had deterministic dynamics Figure  and we iThe environmental responsiveness of indicators in Group II indicated that the temporal fluctuations in species diversity indicators cannot be understood by a simple combination of patterns of species richness and evenness but rather reflect different dimensions of biodiversity. While species diversity dynamics showed a clear response to rapid temperature changes, species richness remained unchanged and species evenness showed complex responses. These findings are in contrast to earlier correlation‐based studies showing that species richness or species evenness share the same dimension of biodiversity with species diversity  were altered during the warming period, suggesting that warming alters the dynamics of relative species abundance. The mutual prediction skills of total abundance, species diversity, and taxonomic diversity decreased for window pairs spanning the first and second halves of the study period Figure , suggestThe classification of species richness and cCOD in the same group (Group I) may be explained by seasonal immigration and emigration/local extinction of fish species with lower cCOD. Seasonal immigration and the local extinction of tropical fish occur every year along the Sea of Japan ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Reiji Masuda: Data curation ; resources (lead); writing – review and editing . Yutaka Osada: Formal analysis (supporting); investigation (supporting); methodology (supporting); writing – review and editing . Kazutaka Kawatsu: Formal analysis (supporting); investigation (supporting); methodology (supporting); software (lead); writing – review and editing . Michio Kondoh: Conceptualization (supporting); investigation ; methodology ; supervision (lead); writing – original draft (supporting); writing – review and editing .YOt was supported by JST SPRING (grant number JPMJSP2114). MK and RM were supported by a JSPS KAKENHI Grant (grant number 19H05641), and MK was supported by a JSPS KAKENHI Grant (grant number 21H05315) and the Environment Research and Technology Development Fund (grant number JPMEERF20214103) of the Environmental Restoration and Conservation Agency of Japan.We have no conflicts of interest to declare.Appendix S1–S4Click here for additional data file.


Macaca mulatta) that has experienced multiple Category 3 hurricanes since the colony's inception on Cayo Santiago, including 275 individuals from ages 9 months to 31 years . Using geometric morphometrics to quantify FA and a linear mixed‐effect model for analysis, we found that sex, age, and decade of birth did not influence the amount of FA in the individuals included in the study, but the developmental stage at which individuals experienced these catastrophic events greatly impacted the amount of FA exhibited (p = .001). Individuals that experienced these hurricanes during fetal life exhibited greater FA than any other post‐natal developmental period. These results indicate that natural disasters can be associated with developmental disruption that results in long‐term effects if occurring during the prenatal period, possibly due to increases in maternal stress‐related hormones.As natural disasters become more frequent due to climate change, understanding the biological impact of these ecological catastrophes on wild populations becomes increasingly pertinent. Fluctuating asymmetry (FA), or random deviations from bilateral symmetry, is reflective of developmental instability and has long been positively associated with increases in environmental stress. This study investigates craniofacial FA in a population of free‐ranging rhesus macaques ( Macaca mulatta) to better understand the impact major ecological catastrophes have on skeletal development. Individuals that experienced a hurricane event in utero exhibited higher levels of FA than juvenile or adult individuals that experienced that same event, which could be due to increased maternal stress hormones such as glucocorticoids that can disrupt fetal development.Fluctuating asymmetry (FA), or random deviations from symmetry, can be used as a proxy for developmental instability, and anthropogenic and natural disruptions have been associated with increased levels of FA. In this study, we investigate the effect of demographic variables  and hurricane experience on FA in a model primate taxon: rhesus macaques ( The macaques in this sample span multiple generations and include individuals that experienced one or more Category 3 hurricanes during their lifetime on Cayo Santiago. We examine how FA changes across ontogeny, with demographic factors such as age and decade of birth and examine the impact of experiencing such a major natural disaster on FA levels.Fluctuating asymmetry (FA)—defined as random deviations from symmetry in traits that are otherwise bilaterally symmetrical—has been repeatedly demonstrated to reflect a morphological proxy for the frequency and/or magnitude of stress events experienced by an individual  are significantly greater in populations subjected to environmental disturbance via industrial logging activity  from Cayo Santiago, Puerto Rico, to assess three distinct aims: (1) clarify the relationship between FA and age within a model primate taxon; (2) quantify the potential role of other demographic variables—specifically sex and decade of birth—in driving FA; and (3) assess the impact of a catastrophic natural event  upon FA levels in a free‐ranging primate population.In this study, we use a free‐ranging sample of rhesus macaques  oversees the health and maintenance of the colony, which is otherwise free ranging. Once commercial primate diets were produced in the United States, the CPRC began provisioning the macaques with fresh water and monkey chow and have increased supplementation plans in recent years due to hurricane‐related environmental instability  and animals that experienced at least one named hurricane in their lifetime . Of the latter group, 78 animals experienced just one hurricane and 23 animals experienced two. To better understand the effect of adversity on the ontogeny of fluctuating asymmetry, the individuals who experienced a hurricane were further divided into groups on the basis of skeletal age at which the hurricane was experienced: fetal (n = 10) individuals who experienced the hurricane prenatally; juvenile (n = 50) individuals who experienced the hurricane prior to skeletal maturity ; adult (n = 41) individuals who were skeletally mature during the hurricane event .We analyzed crania from 275 individuals of both sexes . After scanning, the 3D surface models were processed in Geomagic Studio (3D Systems) using the “fill holes” and “mesh doctor” functions. After processing, the 3D models were imported into 3D Slicer  was then performed in MorphoJ to determine the levels of FA present in each individual's cranium. This analysis includes individuals (specimens) and sides (right/left) as main effects, as well as an interaction term between individual and side . The average difference between the right and left sides represents directional asymmetry and the individual‐by‐side term represents FA  and then a second subset of 174 individuals that had never experienced a hurricane to mitigate any potential influence of environmentally driven FA upon these results. Post hoc Tukey tests utilizing Bonferroni‐Holm correction were subsequently applied using the R package “comptest”  and included age , sex, decade of birth, and hurricane yes/no  as fixed effects, and matriline as a random effect. The second model sought to investigate whether experiencing multiple hurricanes had an additive effect, and included age, sex, decade of birth, and number of hurricanes experienced in an animal's lifetime  as fixed effects, and matriline as a random effect. Finally, we explored whether experiencing a hurricane at different periods of ontogeny influenced the development of FA. This model was run only on animals that had experienced a hurricane (n = 101) and included age, sex, decade of birth, and age at hurricane  as fixed effects, and matriline as a random effect. Thus, all models contained 4 fixed effects  and 1 random effect . A simulated power analysis . The F ratio of FA to measurement error Procrustes mean squares is 1.54, which illustrates that the FA signal is 1.54 times greater than measurement error. This is enough to be confident that our results are valid with an F ratio comparable to other published studies  and FA are present in the sample . Sex  and decade of birth  are similarly non‐significant throughout.No significant effect of age at death on FA is observed in either our full dataset .Modeling hurricane experience as a binary effect, where an animal either did or did not experience an event, has no significant effect on FA . A post hoc, Bonferroni‐Holm adjusted Tukey's test demonstrated that fetal individuals during a hurricane event exhibit significantly greater FA later in life than those that were either juveniles  or adults  during the hurricane; however, no differences are observed between individuals that experienced a hurricane as juveniles versus adults . To further confirm this observation, we then ran a final model post hoc comparing levels of FA in individuals that had experienced a hurricane at a fetal age to all individuals that had not experienced a hurricane. FA levels were again significantly higher within individuals that had experienced a hurricane at a fetal age  while no other fixed effects  attained significance.However, among individuals that had experienced hurricanes, age at the time of the hurricane yields a significant effect on FA (4In an assessment of the influence of age (Aim 1), sex and decade of birth (Aim 2), and natural disaster experience on FA (Aim 3), our results indicate that age, sex, and decade of birth have no statistical influence on FA in the population of rhesus macaques living on Cayo Santiago. While a binary hurricane experience factor did not appear to influence levels of FA, the developmental period in which an individual experienced a hurricane had a significant impact on FA levels later in life. Specifically, individuals that experienced hurricanes during fetal development exhibit significantly higher levels of FA than those that experienced a hurricane during either the juvenile or adult postnatal periods Figure .4.1These results support earlier findings that sex has little influence on FA in the Cayo Santiago macaque population ; however, no significant differences were observed between age classes older than 2 years , individuals who experienced the hurricane both at earlier and later periods of prenatal development exhibit similar levels of FA Table . Such daHurricane disturbances are complex, dynamic events that can change in both size and intensity while traveling thousands of miles. As hurricane formation is linked—among other external factors—to ocean surface temperatures, both the frequency and magnitude of hurricanes have been tied to global climate change, particularly within tropical oceans between latitudes of 40° S and 40° N Lugo, , a regio5This study provides evidence that stress from natural disasters during the prenatal period exhibits lasting effects on the primate skeleton, possibly due to increases in maternal stress‐related hormones such as cortisol and glucocorticoids that cause disruptions to typical fetal development. The macaques living on Cayo Santiago are an ideal sample for investigating the effect of hurricane disturbances as most major hurricanes arriving in Puerto Rico have a drastic effect on this island and its inhabitants. Further research in this population is warranted and can provide a clearer picture of the impact of natural disasters on skeletal development, including insights into the effect of social connectedness and nutrition. As climate change continues to create more instability in climatic events, natural disasters are becoming more frequent and severe. These macaques provide a window into the effect such catastrophes can have on both human and nonhuman populations around the world.Ashly N. Romero: Conceptualization (lead); data curation (lead); formal analysis (supporting); funding acquisition (lead); investigation (lead); methodology ; project administration (lead); visualization (lead); writing – original draft (lead); writing – review and editing . Edwin Dickinson: Formal analysis ; investigation (supporting); methodology ; writing – review and editing . Cassandra M. Turcotte: Formal analysis ; investigation (supporting); methodology ; writing – review and editing . Claire E. Terhune: Conceptualization ; funding acquisition ; investigation ; resources (lead); supervision (lead); validation ; writing – review and editing .Funding for this research was provided by P.E.O. International, the University of Arkansas Fulbright College of Arts and Sciences, and the University of Arkansas Department of Anthropology.All authors declare no competing interests.https://www.morphosource.org/projects/000373632?locale=en.This article has earned Open Data and Open Materials badges. Data and materials are available at Table S1.Click here for additional data file.