Contemporary analyses of insect population trends are based, for the most part, on a large body of heterogeneous and short‐term datasets of diurnal species that are representative of limited spatial domains. This makes monitoring changes in insect biomass and biodiversity difficult. What is needed is a method for monitoring that provides a consistent, high‐resolution picture of insect populations through time over large areas during day and night. Here, we explore the use of X‐band weather surveillance radar (WSR) for the study of local insect populations using a high‐quality, multi‐week time series of nocturnal moth light trapping data. Specifically, we test the hypotheses that (i) unsupervised data‐driven classification algorithms can differentiate meteorological and biological phenomena, (ii) the diversity of the classes of bioscatterers are quantitatively related to the diversity of insects as measured on the ground and (iii) insect abundance measured at ground level can be predicted quantitatively based on dual‐polarization Doppler WSR variables. Adapting the quasi‐vertical profile analysis method and data clustering techniques developed for the analysis of hydrometeors, we demonstrate that our bioscatterer classification algorithm successfully differentiates bioscatterers from hydrometeors over a large spatial scale and at high temporal resolutions. Furthermore, our results also show a clear relationship between biological and meteorological scatterers and a link between the abundance and diversity of radar‐based bioscatterer clusters and that of nocturnal aerial insects. Thus, we demonstrate the potential utility of this approach for landscape scale monitoring of biodiversity. Here, we explore the use of X‐band weather radar for the study of local insect populations using a high‐quality, multi‐week time series of nocturnal moth light trapping data. Specifically, we test the hypotheses that (i) classification algorithms can differentiate meteorological and biological phenomena, (ii) the diversity of the classes of bioscatterers are quantitatively related to the diversity of insects as measured on the ground, and (iii) insect abundance measured at ground level can be predicted quantitatively based on dual‐polarisation Doppler weather radar variables. Adapting the quasi‐vertical profile (QVP) analysis method and data clustering techniques developed for the analysis of hydrometeors, we demonstrate that our bioscatterer classification algorithm successfully differentiates bioscatterers from hydrometeors over a large spatial scale and at high temporal resolutions. Insect population trends have been the focus of large numbers of research publications over the past decade, with the majority indicating declines ] will fluctuate through time and may not be consistent with other radars within the WSR network. Furthermore, the ability to make assumptions about the scatterers within the observed volumes of data and expertly examine the data is limited due to the size and diversity of the data collected across a network. Dual‐polarization Doppler WSRs have yielded promising results that enhance our ability to extract information about the size and shape of organisms, and, thus, a more accurate assessment of biodiversity that might resolve these challenges , in the UK, and insect monitoring radars in Australia), but these small radars are few and limited in their spatial coverage. VLRs are typically well‐calibrated, operate on their own, produce relatively small volumes of data that can be expertly examined, and the analysis of their observations benefits from Meteorological monitoring has significantly improved through advances in polarimetric radar technology and an increasing understanding of the electromagnetic properties of hydrometeors , facilitating the development of classification algorithms for those objects  has been found to be useful for separating bioscatterers from hydrometeors  , which is sensitive to size and shape   as in Stepanian et al.  tools offer an opportunity to extract new, biologically meaningful information from the data but have only been applied to specific biological phenomena .Here, we demonstrate the use of a novel method  mobile X‐band dual‐polarization Doppler WSR  where the radar beam is either blocked at low elevation angles due to obstacles such as other observational instruments and buildings, or where the transmitter is switched off to avoid interference  of similar‐sized insects. These are limitations that we must bear in mind while we analyse the resulting clusters and their characteristics. In general, the clustering method may be applied to any geometry of WSR input dataset. This includes PPIs, RHIs, volumes of PPIs and vertical columns extracted from a specific location within a PPI.In general, utilization of QVPs has, in this context, both advantages and disadvantages. The advantages of QVPs are related to the fact that averaging values reduces statistical errors in the WSR data  and a preliminary analysis of the PPI data. The analysis allowed us to check that no bird migrations were observed over the focal nights. The nights included were: 11–14 and 31 May; 1, 14–21 and 25–27 June; and 1–11, 17–18 and 24–26 July. QVP data were only utilized for the periods of time when light trap data were also available, from civil dusk to civil dawn. QVPs of horizontal reflectivity factor The method used in this study is based on the iterative hierarchical clustering algorithm developed by Lukach et al.  for clasThe optimality of the splitting in the BCA is determined with two criteria: one ‘inner loop’ and one ‘outer loop’. The first criterion is determined as a measure of compactness based on the distances between the points and the barycenters of all clusters. The algorithm terminates the process when the clusters of the ‘inner loop’ subset reach the most compact form. These clusters are passed to the ‘outer loop’ and the posterior probability of a total clustering being true is estimated for all input points. In this way, the BCA creates a hierarchical tree that maximizes the information of the identified clusters throughout its structure. As there is no predisposition of the algorithm to focus on any particular property of the data, it is agile and is just as suited to examining bioscatterers as hydrometeors.Advantages of this algorithm are that it is a purely data‐driven classification and that it allows for choice in the level of classification detail used in the subsequent analysis due to its hierarchical structure. The main idea behind the data‐driven clustering method is the splitting of multivariate data into an optimal number of classes according to underlying characteristics manifested in a principal components space while keeping track of the hierarchical sequence of the splitting process. Full details of the algorithm may be seen in Lukach et al. .The output of the BCA through time was compared to observations from the RIS light‐trap network. We focused on two light trap sites located at Bentley Wood (SU253324) and Porton Down (SU223384). The distance between the two light traps is c. 6.7 km, and both are within 20 km of the CAO, providing optimal resolution within the NXPol‐1 data across both sites. For the comparison, we chose sampling dates between May and July to maximize the diversity of macro‐moth species likely to be present. Specific sampling days were chosen to exclude both significant meteorological phenomena and any moth catches involving more than one night of sampling (i.e. where the catch would be representative of two or more nights instead of a single night). This process of data selection resulted in 33 days of sampling. The Bentley Woods light trap was active across all these dates while NXPol‐1 was in operation (see above), whereas the Porton Down trap was only operational across 16 nights: 31 May; 1, 14–15, 21, and 26 June; and 3–6, 10–11, 18 and 24–26 July.The two focal light traps captured 177 macro‐moth species  across our 33 chosen sampling dates. Individuals are identified by trained experts who monitor the traps. We collated a morphometric trait database  were retained. The variable loadings show PC1 as having a weak positive association with all morphometric traits, while PC2 has a strong positive association with the lateral and anterior aspect ratios (see Table 2) × abundance (hereafter termed ‘mass‐squared abundance’: ‘MSA’). Finally, we used these derived variables to test for relationships between the light trap samples and the BCA clusters.All entomological data analyses were carried out in R, version 4.0.2 , diversity function in the vegan package. To obtain a measure of macro‐moth morphometric diversity, we measured the functional dispersion  per night using the Finally, we tested for correlations between macro‐moth abundance per night and the nightly abundance of each individual BCA cluster, as well as the total abundance across all BCA clusters per night , using Pearson's correlation or Spearman's rank correlation depending on whether the data conformed to the assumptions of parametric tests.The hierarchical clustering tree resulting from the BCA is shown in Figure Note that the set of clusters output by the BCA depends on (i) the selection of the set of input variables utilized, and (ii) the set of points in the multivariate space of selected input variables. For the BCA to be stable in this context, the use of the same input data will always lead to the same output set of clusters. As in Lukach et al.  we testeHV mean value of all the clusters. Cluster 5 also has a narrow distribution of ZDR values that is centred close to 0 dB. This implies that the observed radar backscatter possibly originates from spherical targets. The mean values in the horizontal and vertical reflectivities (ZH and ZV) are highest among the other clusters (18 and 19 dBZ) but should be considered as low for meteorological observations and in most cases would represent light rain in the weather radar observations. Combining all these components together, we consider Cluster 5 as representing light rain.The BCA makes a clear distinction in Cluster 5 from the remaining four clusters at the first iteration level, and the characteristics Fig.  of ClustZH and ZV values (between 0 and −12 dBZ), slightly positive mean ZDR values (between 1 and 2 dB) and low RhoHV mean values (<0.7).As the meteorological and the non‐meteorological cluster branches were separated from each other at the first level of the BCA's tree it is easy to generalize the characteristics of all the other clusters together. The characteristics of the non‐meteorological, or bioscatterer, branch formed by Cluster 1, 2, 3 and 4 can be described by low mean ZH and −12 dBZ for ZV) and a mean ZDR value near 1 dB. These characteristic values suggest that data points belonging to Cluster 1 should come from observations comprised of ensembles of bioscatterers with low number densities  that have relatively small bodies that are slightly elongated and horizontally oriented. Conversely, Cluster 2 has the highest mean reflectivity values among all the bioscatterer clusters (close to 0 dBZ in both ZH and ZV). Like Cluster 1, Cluster 2 has a mean ZDR value of ~1 dB and a mean RhoHV value of ~0.7, but Cluster 2 is also distinguished by having a negative mean KDP value. This suggests Cluster 2 is comprised of observations of relatively dense ensembles of larger, horizontally oriented bioscatterers with slightly elongated bodies. The inferred larger size of the bioscatterers comprising this cluster is also supported by the fact that Cluster 2 is found closer to the surface than Cluster 1  and the highest mean ZDR value (~2 dB). Combined with its other characteristics, these values suggest that Cluster 3 represents observations of bioscatterers with low number density that have elongated body shapes that are highly uncorrelated in the observed air volume. The uncorrelated signature of Cluster 3 may be partially due to variations in body geometry caused by wing beating which could influence the bioscatterers backscattering characteristics. Unlike the other three bioscatterer clusters, Cluster 4 has a near 0 dB mean ZDR value. Thus, combined with also having the most negative mean KDP value (~ −2.5°km−1), Cluster 4 likely comprised a collection of bioscatterers with low numbers of larger but more spherical bodies.The mean reflectivity values of Clusters 3 and 4 are intermediate to those of Clusters 1 and 2. Thus, Clusters 3 and 4 are mostly separated from the other clusters by the remaining polarimetric radar variables. In particular, Cluster 3 has the lowest mean RhoIf we assume that the clusters are primarily representative of insects, we can think of Cluster 1 as representing smaller individuals that are homogenous in terms of their morphology, with slightly longer, slimmer body shapes—potentially reminiscent of smaller Diptera. This cluster is ubiquitous within the air column, being present between ~100 and 800 m in altitude Fig. , but is The ecological interpretation of these clusters in the context of ‘biodiversity’ is not straightforward. An increase in cluster 3 at the expense of clusters 1 and 2 may involve an increase in overall diversity as one of the features of cluster 3 is that the constituent objects are diverse in size and shape. However, an increase in the diversity of clusters will still tend towards an increase in the broader diversity of insect morphotypes, as the clusters each represent different community structures drawn from the wider species pool. Further work will be needed to explore the extent to which species overlap in their memberships of different clusters and cluster‐specific spatio‐temporal distributions.Linking these clusters to specific (groups of) taxa will require a series of cross‐validation studies. These studies could involve VLR or other entomological radars that can identify species at different heights. However, those VLRs suffer from similar issues to the WSRs in that they can vary in calibration and power. Aerial netting can be used to capture animals in the air column, but this is a considerable logistical exercise that captures relatively few animals. Finally, it is possible to use natural experiments during mass emergences or migrations of known insect taxa to explore the link between radar measurements and those specific morphotypes. Examples of such natural experiments include aquatic insect emergence  = 2.339, P = 0.043]. Approximately 89% of the constrained inertia of this model was explained by the first axis, although this axis did not statistically predict BCA clusters alone . This axis was characterized by moderate‐to‐high scores for summed abundance  and summed biomass (0.71) per night. BCA Cluster 3 displayed a weak positive score for this axis (0.35)  = 6.511, P = 0.015] when all terms were tested sequentially, but that no terms were significant when each was tested independently. Overall, this indicates that, while BCA cluster 3 can be typified by an increased abundance in comparison to the other clusters, we can make no assumptions regarding the morphology of the bioscatterers characterized by the BCA clusters.Of the four CCA models, model 3 Table  explaine35) Fig. . PermutaRs(31) = 0.60, P < 0.001]  and functional dispersion . Results showed a strong positive correlation between the taxonomic diversity of both the aerial macro‐moth community and the BCA clusters per night [01] Fig. .Rs(31) = 0.5, P = 0.003), 3 (Rs(31) = 0.73, P < 0.001) and 4 (Rs(31) = 0.53, P = 0.002), as well as total abundance across clusters 1–4 (Rs(31) = 0.57, P < 0.001), per night . As such, our findings suggest that the radar‐derived measure of abundance and diversity of nocturnal insects can be used to predict—if not measure—ground‐level biodiversity.While the potential for the use of dual‐polarization Doppler WSRs has been recognized for the study of aerial insects  than that estimated to be sampled by each light trap, providing a landscape scale measure of aerial bioscatterer activity. This difference in the scale of spatial sampling, together with the focus on macro‐moth species in the light trap data, may be responsible for our inability to relate moth morphometric diversity to the different BCA clusters since, depending on the level of landscape heterogeneity surrounding our two focal light traps, aerial insect (and macro‐moth) diversity may differ significantly within the area represented by the QVP. However, the RIS light‐trap network remains the only source of systematic nocturnal insect monitoring data in the United Kingdom. Bird and bat populations will also have contributed to patterns of biological activity on WSR observations  scans . Finally, and most interestingly, we show that the diversity of radar‐observed bioscatterer clusters correlates significantly and positively with the diversity of moths that dominate the nocturnal air column. Taken together, these results demonstrate a proof of concept for the application of dual‐polarization WSR networks to monitor UK insect communities at a national scale, with the potential for dissemination wherever there are comparable operational WSR networks. These novel data sources may represent a step‐change in the monitoring of insect populations by providing a much‐needed international, standardized source of information to identify and diagnose changing population trends.Polarimetric Doppler weather radars have the potential to resolve long‐standing issues in ecological monitoring. We provide an attempt to classify aerial nocturnal insects (bioscatterers) using X‐band dual‐polarization weather radar observations with ground truthing using high‐resolution ecological community data. We demonstrate that our BCA, based on a novel iterative hierarchical clustering technique, easily distinguishes meteorological from non‐meteorological phenomena. Additionally, the clusters corresponding to bioscatterers were primarily separated along axes of biomass and aspect ratio, although strong support only exists for only a subset of the clusters. This indicates a relatively weak ability  to provide higher taxonomic resolution for these bioscatterers. This result is most likely due to the strong influence of Mie scatterers and the lack of Data S1. Supplementary methods, including definitions of radar variables and details of moth morphometric measurements.Click here for additional data file.Table S2. The forewing length (mm), thorax width (mm) and thorax depth (mm) of 37 macro‐moth species .Click here for additional data file.


In the published article, there was an error in part of the title. Instead of “Implication for its dural effects in carcinogenicity” it should read “Implication for its dual effects in carcinogenicity.” The corrected full title appears below:“Arsenic in drinking water and incidences of leukemia and lymphoma: Implication for its dual effects in carcinogenicity”Abstract. The sentence previously stated:A correction has been made to the “In terms of time trends, the SIRs in both lymphoma and leukemia showed decreasing trends in both sexes, while exposure to arsenic in drinking water also decreased over time.”The corrected sentence appears below:“In terms of time trends, the SIRs in both lymphoma and leukemia showed increasing trends in both sexes, while exposure to arsenic in drinking water decreased over time.”The authors apologize for the errors and state that this does not change the scientific conclusions of the article in any way. The original article has been updated.


A robust quality management system for automated hematology analyzers is crucial for generating high‐quality results and instilling confidence in analyzer function. A comprehensive quality management system comprises multiple aspects. Two major aspects are quality assurance (QA) and quality control (QC). QA is a framework and strategy for ensuring quality across the entire process, from blood draw through reported results. QA thus encompasses preanalytical to postanalytical variables related to result generation. QC, on the other hand, is focused exclusively on the analytical portion of quality management to ensure the function and performance of the analyzer itself. QC facilitates identification of analyzer problems so they can be addressed and fixed in a timely fashion. Recommendations for QC, including QC materials (QCMs), frequency of QC, and interpretation of QC results, are specific for the analyzer.The ideal QCM would 1) be able to assess the function and performance of all aspects of the hematology analyzer, (2) have a long shelf life, (3) be easy for the user to run and interpret, and (4) instill confidence in results for all species tested. To understand these requirements, it is imperative to understand the workflow of an automated hematology analyzer. Although the specifics vary among analyzers, the workflow follows the same general pattern  closely mirrors patient samples and contains red blood cells (RBCs), white blood cells (WBCs), and platelets. The use of fresh cells in commercial QCM is unfortunately impractical because their shelf life is only days. Therefore, traditional FC‐QCM uses cells that are mixed with a fixative  to improve stability and shelf life of the FC‐QCM.a Cells in the FC‐QCM can come from mammals , reptiles, birds, or a combination thereof. In some cases, FC‐QCM use nucleated erythrocytes from reptiles or birds as WBC surrogates to assess WBC parameters since avian and reptile erythrocytes are easy to obtain in large numbers. Some commercial FC‐QCM use small erythrocytes to assess analyzer platelet identification. When mammalian cells are used, they are usually human and may not accurately represent performance on veterinary species. Due to the lack of transparency about formulation, the true contents of a commercial FC‐QCMs are not known, but they are not necessarily a close mimic of veterinary patient samples.The formulation and source of cells in commercial FC‐QCM are proprietary, and there is limited information available to practitioners or clinical pathologists. Publicly available information about FC‐QCM is often vague and includes statements like “partially derived from human sources.”Even with fixation, the shelf life of FC‐QCM is short. FC‐QCMs are often exquisitely sensitive to temperature, and improper storage further reduces their shelf life. Required FC‐QCM for the Abaxis Vetscan HM5 hematology analyzer, for example, indicates that it is usable after opening for “up to 14 days if it is properly stored.”* Even when properly stored, changes in FC‐QCM can occur over time due to aging. For some parameters, the expected aging changes in the QCM lead to alterations in QC targets over the lifespan of the QCM  function differently and have different required features for QCM. Commercial FC‐QCM may be marketed specifically for one analyzer or for use with multiple analyzers. For instance, Para 12 Extend (Streck) is marketed for analyzers made by eight different companies, but analyzer‐specific QC targets and reference intervals are provided to meet the needs of each analyzer. However, the applicability of one FC‐QCM for different analyzers depends on if the formulation provides information that is relevant to that analyzer's technology and methodology. Analyzer algorithms and targets must be developed for the specific recommended FC‐QCM since there are differences between analyzer requirements and FC‐QCM components.Analyzer algorithms for identifying cells  are species‐specific and result in different scatterplots. Similarly, FC‐QCM scatterplots do not necessarily mimic the plots for any veterinary species. Scatterplots for both patient samples and FC‐QCM vary among analyzers because analyzers identify and characterize cells using different characteristics. If the recommended FC‐QCM for the ADVIA 2100/120 is run through the analyzer as a sample, it looks reasonably like canine patient samples. When the same thing is done with the recommended FC‐QCM on the IDEXX ProCyte Dx analyzer, plots look less like their corresponding canine patient scatterplots  from the mean or target.1S rule, for instance, would reject a 1 SD if it was the fourth such deviation in a row. A 41S rule would be more stringent and detect smaller deviations from the target but would be more likely to falsely reject QC results. Multiple rules  can be used to incorporate a variety of different magnitudes and durations of risk where any included rule can be used to reject a result. Similar to the frequency of QC, the choice of rule needs to reflect the particular risk management strategy appropriate for the reference laboratory or clinic, the volume of patient samples, and frequency of QC analyses. As such, complex rules are appropriate for reference laboratories with high sample volume and trained personnel but are unnecessarily complex for use in clinics. The frequent false rejection of QC results in clinics leads to unnecessary and frustrating analyzer downtime and troubleshooting that could erode the clinic's commitment to following QC recommendations.Choice of statistical methods to interpret QC results is part of the risk analysis. A variety of statistical methods can be used for the interpretation of QC results with different degrees of rigor for identifying analyzer issues. Reference laboratories and clinics will have different ideal balances of maximizing the potential to identify analyzer issues and minimizing unnecessary troubleshooting and false rejection of results. Levey‐Jennings charts are commonly used to track performance of QC over time.These common QC myths hide some of the true opportunities for improvement in QC, particularly QC in the in‐clinic setting, where improvements in ease and compliance could lead to improved confidence in results from hematology analyzers. Although FC‐QCM provides many of the answers about analyzer function and performance, FC‐QCM alone does not allow us to assess all the components. Technological advances and research have resulted in new QC methodologies and materials being used in pockets across the industry without broad adoption. Many of these advances offer opportunities for continued improvement in hematology QC by combining different strategies and new QCM to provide a better overall view of analyzer function and thereby improve the potential for identification and resolution of analyzer issues.Noncellular QCMs, including manufactured beads, have many of the benefits of FC‐QCM and have much longer stability and shelf life. Bead‐based QCMs and calibration materials are commonly used as standards to verify fluidics, fluorescence, and optical performance in flow cytometers.Some in‐clinic hematology analyzers, like the IDEXX LaserCyte DX analyzer, already use bead‐based QCM. Qualibeads are run with each sample to ensure that the analyzer functions properly and is standardized within and across runs on this analyzer.† Bead‐based QCMs, like FC‐QCMs, do not directly mimic patient sample cells and require QCM‐specific algorithms and workflow steps Table . As suchIntegration of patient samples into the QC strategy can help to fill some of the gaps left by FC‐QCM or bead‐based QCM. QC uses patient samples and can identify species‐specific issues related to analyzer chemistry that are missed by other QCM. Several methods using patient samples have been described and found to be both practical and successful at evaluating the function of veterinary hematology analyzers.Patient‐based population analysis quality control (PBA‐QC) uses populations of patients previously analyzed on the instrument to identify drift, species‐specific bias, or problems with analyzer chemistry. Since it uses data from actual patient samples, these data are generated with the normal patient workflow and can be used to assess all steps of the analysis, including preanalytical sample handling. Weighted moving averages from patient samples have been used to monitor analyzer performance for over 45 years for human hematology analyzers.Repeat patient testing (RPT‐QC) uses patient samples differently and has been investigated in both human and veterinary medicine.Strategies for using patient samples have different strengths and drawbacks from FC‐QCM and bead‐based QCM. Since patient samples are used, there are no QC‐specific analyzer workflows Table , and speTraditionally, quality evaluation and maintenance of in‐clinic analyzers have been entirely the responsibility of the clinic staff. However, there is wide variability in the commitment of clinics to perform QC and to understand the importance and significance of QC and quality management. There can also be logistical hurdles for clinics due to high patient volume and staff turnover. Automation of QC provides a path around these hurdles, presenting an opportunity to improve confidence in results from in‐clinic analyzers across all clinics and to ensure that QC is performed at the recommended minimum frequency.Automated QC can be performed by the analyzer outside of work hours and without requiring effort or commitment from clinic staff. Additionally, automation of QC interpretation would improve and standardize recognition of analyzer problems and assist in troubleshooting for in‐clinic analyzers. Centralization of QC interpretation could also more easily identify drift or other subtle issues by comparing an analyzer in one clinic to the general population of all similar analyzers. These subtle problems could be centrally identified with notifications automatically sent to clinic staff with appropriate next steps. Additionally, QC could automatically be repeated if there was a run failure, and analyzer or calibration issues could be fixed remotely. Partially transferring responsibility for performing QC analysis from the clinic to a centralized, automated system would improve overall compliance with QC recommendations and decrease strain on clinics. Automated QC could also facilitate using more stringent interpretive rules by removing resource and time barriers for more stringent analyses from the clinic. Automating both the QC and its interpretation should improve analyzer performance and instill better confidence in results from in‐clinic hematology analyzers.Despite the importance of QC, it is often misunderstood or even ignored by clinicians. There are pervasive misconceptions and myths about quality control, the nature and requirements of QCM, and QC for in‐clinic hematology analyzers. While FC‐QCM provides valuable information about the health and performance of the analyzer system, they do not directly mimic patient samples and patient workflow, and they have a short shelf life. This short shelf life can be financially and logistically burdensome for clinics with low sample numbers and infrequent QC analysis. Direct mimicry of patient samples is not necessary for the benefits associated with QCM. Noncellular QCM can have similar benefits without the short shelf life of FC‐QCM. However, both cellular and noncellular QCM fail to assess all aspects of patient sample analysis. Combining QCM and patient sample analysis  can provide more ideal QC information about the analyzer function and identify species‐specific issues with analyzer chemistry or drift.Risk assessment for in‐clinic analyzers is different than for reference laboratory analyzers. As a result, the QC frequency and analysis recommended for the laboratory analyzer might not be appropriate for many clinics, especially ones with low sample numbers. There are unique challenges to ensuring appropriate analyzer function for in‐clinic analyzers and QC compliance. Automation of QC analysis can help to improve compliance with minimum QC recommendations and to ensure accurate results for in‐clinic analysis. This automation can also relieve some of the burden of QC from busy clinics.There are opportunities to continually improve the QC experience and compliance for in‐clinic analyzers by extending QCM shelf life, improving thermal stability of QCM, and automating many of the QC activities. The concepts described here can be adapted and applied to different hematology analyzer technologies and are not specific to any particular manufacturer. Hematology analyzer manufacturers should strive toward improving QC in ways that increase the quality of results from in‐clinic analyzers, reduce the financial and logistical burdens of QC, and improve overall patient care by minimizing analytical errors.JH, HTM, and DBD are or have been full‐time employees of IDEXX Laboratories, Inc. All other authors serve as consultants for IDEXX Laboratories, Inc.


Specifically, Treatment 3 significantly increased caregivers' knowledge of OFSP production, consumption, and vitamin A. Treatment 2 significantly improved their attitude too. It concludes that an integrated complementary nutrition education approach targeting preschooler–caregiver pairs is more effective in increasing knowledge of cultivation and consumption of OFSP. It discusses the implications for the design of more effective nutrition programs targeting households with preschoolers to accelerate the fight against vitamin A deficiency (VAD).This 2018 randomized controlled trial examined the role behavioral nudges can play in improving caregivers' knowledge, attitude, and practices (KAP) relating to biofortified orange‐fleshed sweetpotato (OFSP). The experiment involved 431 preschooler–caregiver pairs in 15 villages. The preschoolers were enrolled in public‐run Early Childhood Development and Education (ECDE) centers in the respective villages. Caregivers were first exposed to the routine OFSP promotion activities in the area – invited to cooking demonstration workshops and issued with free OFSP vines to plant. A baseline survey followed. Next, the 15 villages were randomized into four study groups (a control and three treatments). The interventions were deployed for 30 days as follows: Treatment 1 – preschoolers issued OFSP‐branded exercise books, class posters, and poems; Treatment 2 – caregivers received phone‐mediated text messages; and Treatment 3 – received the full suite of interventions. This study analyzed the endline and baseline data and finds that, in general, changes in KAP scores were negatively associated with control group  messages and sending these text messages to the caregivers’ mobile phones were tested in a village‐level cluster randomized control trial. These interventions showed significant positive effects on caregivers’ knowledge and attitude towards the OFSP. There is a great potential to improve the performance of nutrition programs involving biofortified crops by embracing preschoolers ‐ as agents of change ‐ and sending mobile phone‐mediated text reminders to the caregivers. Specifically, this study sought to assess the effects of (i) preschooler‐focused nutrition education interventions on the caregivers' KAP relating to OFSP; (ii) caregiver‐targeted mobile‐phone‐mediated nutrition education reminders/messages on the caregivers' KAP relating to OFSP; and (iii) integrated complementary nutrition education interventions on the caregivers' KAP relating to OFSP.To the best of our knowledge, no study has examined the role that preschooler‐focused nutrition education interventions can play in influencing households' KAP towards production and consumption of nutritionally enhanced biofortified food. Two studies, Nabugoomu et al.  and Humm22.1This study was conducted in Homa Bay County, a county with a very high prevalence of undernutrition in Kenya. Over one‐quarter of children under 5 years in the county are stunted . A random sample of 431 preschooler–caregiver pairs was drawn for the experiment following McConnell and Vera‐Hernández  center was selected for the study, thus 15 ECDE centers. Next, households with preschoolers enrolled at the selected ECDE centers were listed and caregivers of the enrolled children were invited to cooking demonstration workshops. The workshops were held at each of the ECDE centers in February 2018. Each caregiver was then given 200 cuttings (each 30 centimeters long) of free OFSP vines, in April 2018 to plant. These two activities (free vine dissemination and cooking demonstration workshops) define the routine OFSP promotion interventions targeting rural farmers that were carried out under multiple OFSP farmer outreach projects in western Kenya. In this study, the caregivers who participated in the cooking demonstrations and received the free vines formed the sampling frame  at village level – all preschooler–caregiver pairs in a given village were assigned into one study group. The control group participated only in the routine OFSP promotion activities – cooking demonstration workshop and got free vines. Conversely, the treatment groups in addition received complementary nutrition education interventions via three distinct approaches for 30 days in September and October 2018. We describe the three treatments below.2.2.1Dholuo – the local language .Data were collected from caregivers using individually administered pretested and validated questionnaires at baseline and the end of the intervention period. The interviews were conducted in August 2018 (baseline) and October–November (follow‐up). Verbal informed consent was obtained from all the caregivers before proceeding with the interviews. A total of 390 and 360 complete interviews were conducted during the baseline and follow‐up surveys, respectively. The 7% attrition resulted from school absenteeism by preschoolers' and/or caregivers' refusal to participate in the follow‐up survey. However, there were no significant differences in the attrition rates between the study groups , consumption knowledge (4 items), and vitamin A knowledge (6 items). For both the positively and negatively framed production and consumption knowledge statements, codes 0, 1, and 2 were entered for incorrect, correct, and “Don't know” responses, respectively. The “Don't know” responses were treated as unanswered questions to avoid penalizing respondents for their nonresponse to the items   and Kruskal–Wallis tests for normally and non‐normally distributed variables, respectively. We also used the Bonferroni adjustment method for multiple pairwise comparisons of differences of the variables across the study groups (using Stata command ) Dinno, . Additio2.5.2Yij is the change in the relative KAP scores for individual i in village j; and PT, IT, and CT are the random treatment group assignment variables coded as a dummy (1 for the given intervention group and 0 for being in the control group). The main parameters of interest β1, β2, and β3 represent the estimated average treatment effects of the interventions on the caregiver's KAP scores. KAPt=0 is the relative score of the respective KAP constructs at baseline for individual i. The vector X' contains individual‐level, household‐level, and institutional variables that relate to psychosocial constructs on food items. The random error term is notated as εi.Our primary outcomes of interest, changes (follow‐up minus baseline) in the relative KAP scores, appear on a continuous scale with values from −1 to 1. Following McCullagh , we emplbootstrap‐t procedure to estimate the p‐values for correct testing of the hypotheses – a solution to the problem of few clusters   group was, on average, significantly younger than the rest of the groups. Perumal et al. . Okello et al.  documentA majority (67%) of the sample grew the white‐fleshed and yellow‐fleshed sweetpotato varieties with no significant differences across the study groups. On the other hand, about one‐half of the total sample harvested OFSP in the first season. However, a significantly higher percentage (81%) of the control group grew the OFSP than in the other study groups. This was mainly due to the differences in rainfall patterns during planting season, which largely favored one village in the control group.The preschooler's average age was 6 years (and ranged 4–7 years); hence, a majority could relay information received in school to the caregivers. Children were used in the study as change agents, and their effectiveness may be related to their ages. Hence, the preschoolers who participated in the group could potentially serve the intended role.The ECDE centers are indeed a good avenue for reaching households with VAD high‐risk groups. About 76% and 75% of the households had under‐5‐year‐old children and female caregivers of reproductive ages (15–49 years), respectively. In the latter case, the remaining one‐quarter of the households had caregivers who form an active social environment to the children  and should be actively integrated into agriculture–nutrition promotion interventions for optimal success . However, only the caregivers in the IT recorded significant increase in their OFSP production and consumption knowledge. None of the study groups recorded a statistically significant improvement in their level of practice regarding OFSP. This could be because of the relatively short period between baseline and follow‐up. Caregivers may not have had enough time to learn about OFSP and implement recommended practices.As shown in Table Figure Ribeiro et al.  noted thPostintervention, there were increased mean scores in the production knowledge constructs across the study groups. However, the changes in the IT treatment group were more significant than for the rest of the groups. Also, all the treatment groups recorded improvements in their consumption knowledge mean scores. This could imply the possibility of erosion of knowledge of the nutritious crops in the absence of continuous nutrition education and reminders. In addition, all the groups recorded increases in their vitamin A knowledge and attitude towards OFSP. Overall, the variation in the behavior of the treatment groups against the control groups suggests the need to further systematically explore how the different interventions worked in follow‐up studies.3.3p = .005), and positively associated with their assignment into the IT group . In terms of the magnitude of change, only the mean changes in OFSP consumption knowledge and vitamin A knowledge scores were significantly higher in the PT and IT groups than in the control group. However, we acknowledge that these associations do not sufficiently imply causations. Thus, the effectiveness of these reported relationships in informing substantive policy recommendations is limited. We, therefore, explore the cause‐and‐effects in section 3.4 below.Every third and fourth row of Table 3.4Table The performance of the interventions suggests significant improvements in the different dimensions of nutritional knowledge relating to OFSP if an integrated nutrition education intervention is used . Comparatively, the CT intervention performed better than the PT intervention in improving the knowledge constructs. Given the fact that, overall, the entire sample had very poor attitude scores, a deliberate focus on improving their attitude scores is more critical. The results show that the phone‐mediated messages and reminders are potentially more effective means of influencing the caregivers' attitude towards OFSP. Furthermore, as mediators of behavior change, improvements in both knowledge and attitude may have trickle‐down effects on the availability of the OFSP in the household menus and intake of vitamin A.These results are broadly in line with the propositions of the social cognitive theory and findings of Mutiso , which sFurthermore, with the positive results of phone‐mediated messages on improving the caregivers' overall knowledge and attitude towards OFSP in the CT approach, the study underscores the role ICT can play in influencing knowledge, attitude, and practices relating to biofortified crops, their acceptance and utilization in the rural households.The findings also contribute to the literature on the effectiveness of information and communications technology (ICT)‐integrated nutrition education in improving the diets of rural farming households Webb, . The CT 4This study examined the effects of nutrition education interventions targeting preschoolers, as change agents, and their caregivers with nudges designed to influence caregivers' knowledge, attitude, and practices relating to OFSP production and consumption. The participants – preschooler–caregiver pairs – were randomly assigned into treatments/interventions promoting OFSP production and consumption. The study finds that participants who received all the interventions had significantly higher knowledge scores than those who received none of the interventions. It also finds that none of the interventions had a significant effect on farmer practices relating to OFSP production and consumption.Based on the findings, we conclude that an integrated nutrition education intervention involving preschoolers and phone‐mediated text messages to the caregivers is an effective strategy for influencing caregivers' knowledge and attitude. We further conclude that interventions targeting preschoolers only are more effective in improving knowledge of caregivers, while those that specifically target caregivers through mobile‐phone‐mediated messaging are effective on caregivers' knowledge of and attitude towards OFSP.The study findings suggest that preschoolers and their learning materials can significantly nudge the caregivers and influence their knowledge of OFSP production, consumption, and nutritional content. This implies that government and basic education providers could effectively scale up the impact of nutrition programs by strategically integrating nutrition education messages on the learning materials of children enrolled in the ECDE centers.The findings also imply that mobile‐phone‐mediated text messaging can be used effectively to influence the caregivers' knowledge and attitude towards OFSP. The advantages of using mobile‐phone‐mediated education and extension over face‐to‐face have been widely documented in research and development literature and include, among others, the low cost. However, this is not yet established for this approach targeting improved psychosocial constructs among caregivers. Overall, a cost‐effectiveness analysis of the three approaches will be a significant contribution to knowledge and nutrition policy suggestions and should be considered for further research.The authors declare that they do not have any conflict of interest.This study followed the ethical guidelines in the Declaration of Helsinki. It was authorized by the Homabay County Early Childhood Development Education Office under REF: HBC/EDUC&ICT/PTN/VOL. 1/4/1/8/034 of July 30, 2018. It was also approved by the University of Nairobi's Graduate School under Ref: A56/89965/2016.The caregivers gave written informed and voluntary consents on behalf of themselves and their preschoolers.Appendix S1: Supporting informationClick here for additional data file.


Situs ambiguus is a rare congenital abnormality with outcomes ranging from asymptomatic to fatal. Here we present a woman with an incidental finding of situs ambiguus hinted by her chest radiograph. This case highlights the importance of actively seeking diagnosis when right sub‐diaphragmic air is noted when viewing a chest radiograph. We present a case of incidental finding of situs ambiguus hinted by chest radiograph. This case highlights the importance of actively seeking diagnosis when right sub‐diaphragmic air is noted when viewing a chest radiograph. An otherwise healthy 46‐year‐old woman presented to the Pulmonary Outpatient Department due to dry coughs for 1 week. On physical examination, she was alert and oriented with stable vital signs. Antitussive medication was given at the office, and a chest radiograph was taken. Her condition was resolved at her follow‐up visit. However, the chest radiograph at the first visit revealed abnormal gas collection below the right hemidiaphragm and a prominent azygous vein which prompted further investigation Figure  The findYung‐Chia Huang wrote a manuscript for this manuscript and conducted a literature review. Shih‐Wei Lee contributed to the collection of case information and the revision of the manuscript. Shih‐Sin Wu and Mei‐Yin Chen reviewed the final manuscript. All authors have read and approved the final manuscript.None declared.The authors declare that appropriate written informed consent was obtained for the publication of this manuscript and accompanying images.


Keratosis pilaris was significantly associated with palmar hyperlinearity  and the filaggrin loss‐of‐function mutation 2282del4 . The prevalence of KP in the cohort was generally low and KP seems to be infrequent in Finnish AD patients. This may be explained by the fact that the tested FLG loss‐of‐function mutations are rarer in the Finnish population compared for example, with central Europe or Asia. Mutations in other locations of the FLG gene or other genes of the epidermal barrier may play a more important role.Keratosis pilaris (KP) associates with epidermal barrier defects in atopic dermatitis (AD) but its role in disease severity and concomitant atopic diseases seems to vary between populations. We performed a cross‐sectional observational study with 502 randomly selected AD patients of a Finnish tertiary health care center. At a single clinical examination, disease severity (Rajka Langeland severity score and EASI), clinical signs and patient history were evaluated and total IgE levels and frequent filaggrin (FLG) loss‐of‐function mutations were investigated. There was no link with disease severity ( At the clinical visit, we investigated the total serum IgE‐levels and a panel of FLG null‐mutations. Genetic testing was carried out by sequencing four FLG LoF mutations that are frequent in the European population , two FLG LoF mutations enriched in the Finnish population , and the 12‐repeat allele (rs12730241). In addition, we sequenced 59 functional variants of 10 different genes associated with skin barrier defects . The genetic testing, sequencing methods and complete list of genotyped variants have been described in detail in a former study.We performed a cross‐sectional observational study with 502 randomly selected AD patients of a Finnish tertiary health care center. Patient recruitment was carried out in the years 2011–2015 and inclusion criterion was AD. During a single clinical examination, in each patient, disease severity (Rajka Langeland severity score [RLS] and EASI), clinical signs  and patient history were evaluated. Data on disease onset, hereditary factors regarding first‐degree relatives, contact allergy (confirmed by patch testing), prick positivity  and RLS , representing severity of the past year. In addition, there were no significant links to total serum IgE levels, early AD onset or positive history of hand dermatitis, contact allergy, asthma, prick positivity to frequent aeroallergens or peanut allergy , but not with the other sequenced mutations R50X, R2447X or combined heterozygosity FLG_152285076_12 & RS61816761_AG or RS138726443_GA & FLG_152285076_12. We found no association of AD with the tested functional variants of epidermal barrier genes. Patients with KP showed a significantly higher occurrence of palmar hyperlinearity .There was a relatively low number of patients with KP in the investigated cohort (28 KP vs. 319 with no KP). KP was not associated with AD severity based on EASI at the clinical visit (4The study shows that KP in Finnish AD patients may be associated with the FLG LoF mutation 2282del4 and the clinical sign of palmar hyperlinearity.Limitations of the study were the generally low number of patients with KP and the cross‐sectional design which represents only a time point in the patients' history. In addition, the data was mostly based on retrospective patient‐derived information and there was only a limited panel of FLG mutations tested. We also did not measure blood eosinophil levels or other possibly laboratory values relevant in atopic dermatitis, because we wanted to concentrate on FLG‐mutations and clinical characteristics.Former studies have underlined the genetic heterogeneity of atopic dermatitis and its association with KP.Although genetic sequencing from different countries have shown links to FLG‐mutations, especially R501X and 2282del4, they do not account for the complete KP phenotype or explain the wide clinical spectrum.Thus, association of KP with AD seems to be infrequent in Finnish patients which may be explained by the fact that the tested FLG LoF mutations are overall much rarer in the Finnish population compared for example, with central Europe or Asia.The authors state no conflicts of interest.


Juniperus virginiana and four common North American prairie grass species. We assessed how soils that had been occupied by redcedar, a pervasive woody encroacher in the Great Plains of North America, affected the growth of Andropogon gerardi, Schizachyrium scoparium, Bromus inermis, and Pascopyrum smithii over time. We evaluated the effect of redcedar on grass performance by comparing the height and biomass of individuals that were grown in live or sterilized conspecific or redcedar soil. We found redcedar created a negative plant–soil feedback that limited the growth of the cool season grasses B. inermis and P. smithii, reducing their overall biomass by >60%. These effects were found in both live and sterilized redcedar soils. In live soils, some growth suppression can be attributed to the negative effects of soil microbes. The limitation of grass growth in sterile soils indicates redcedar may exude an allelochemical into the soil that limits grass growth. Our results demonstrate that plant–soil feedback created by redcedar inhibits the growth of certain grass species. By creating a plant–plant interaction that negatively affects competitors, redcedars increase the probability of seedling survival until they can grow to overtop their neighbors. These results indicate plant–soil feedback is a mechanism of native woody plant encroachment which could be important in many systems yet is understudied.The encroachment of woody plants into grasslands is an ongoing global problem that is largely attributed to anthropogenic factors such as climate change and land management practices. Determining the mechanisms that drive successful encroachment is a critical step towards planning restoration and long‐term management strategies. Feedbacks between soil and aboveground communities can have a large influence on the fitness of plants and must be considered as potentially important drivers for woody encroachment. We conducted a plant–soil feedback experiment in a greenhouse between eastern redcedar  Juniperus virginiana), a widespread conifer that is encroaching into the grasslands of eastern North America, creates a plant–soil feedback that suppresses the growth of common prairie grasses. This suppressive effect could provide a crucial driver for the success of eastern redcedar establishment in highly competitive prairie plant communities.The alteration of the near soil environment by plants, deemed plant–soil feedback, can have differential effects on conspecific and heterospecific plants and is a process that has been identified as an important contributor to the success of range‐expanding species. Using an experimental approach, we demonstrate that eastern redcedar ( Eastern redcedar (hereafter redcedar) is the most common, widely distributed conifer native to eastern North America . The analyses presented here evaluate whether redcedar creates plant–soil feedback with those grass species and determines the strength and direction of that feedback relative to feedbacks in conspecific soil. If plant–soil feedbacks are a mechanism that help redcedars following encroachment into prairies, we hypothesize we would observe the following outcomes: (a) grass growth in redcedar soils would be reduced more when compared to growth in intraspecific soils; (b) grass growth in live redcedar soil would be reduced when compared to sterile redcedar soil.We conducted a fully crossed greenhouse experiment between redcedar and four common North American prairie grasses  and Schizachyrium scoparium (little bluestem) are common, native warm‐season C4 bunchgrasses with overlapping ranges in tall‐ or mixed‐grass prairies  is a common, native cool‐season C3 rhizomatous grass occurring in mixed‐grass prairies  is a common, Eurasian cool‐season C3 rhizomatous grass that has rapidly spread across North American grasslands since its introduction in the late 1800s  of each species were transplanted into 5.6‐L pots of ProMix Ultimate potting mix . Plants were grown in a greenhouse and received auxiliary lighting using 1000 W high pressure sodium bulbs from 5:00–8:00 PM nightly to promote growth. We deemed the timespan from seedling to mature flowering grass was sufficient to train soils. Ten randomly selected pots of each grass species were harvested in mid‐June following ~16 weeks of growth. In addition, we randomly selected 10 pots from a pool of ~18‐month‐old redcedars that had been growing in 5.6 L pots of the same common potting soil in the same greenhouse for the previous 10 months. Prior to growth in our greenhouse, these redcedar seedlings were greenhouse‐raised at Pinelands Nursery, New Jersey, USA. Due to their slower overall growth rate, we determined that approximately 1 year of growth was sufficient to train soils for this experiment. Grass and tree samples were clipped at the root collar and aboveground biomass was dried in a 65°C oven and weighed. Training soils were separated from root materials manually by running material through a 2 mm sieve. Half of the soil (>2 L) collected from each sample was set aside for sterilization in an autoclave. Each pot was processed individually, and all materials used in processing were sterilized with an alcohol solution in‐between each sample. This procedure was established to prevent the transfer of soil particles and microbes between samples.In the training phase of the experiment individuals were grown in potting mix to condition (or train) soils for use in the feedback phase. In February 2020 four shallow trays were filled with sterilized sand. Sand was steam sterilized in a pressurized autoclave at 121°C for ~60 min, cooled and then sterilized for an additional cycle  log10‐transformed height (logy) as a function of the interaction between the factors conditioning species (x1) and sterilization status (x2), the sum (∑) of smoothing  variables time (ti) and time given each level of the interaction of the two factors , and a random intercept (1∣x3) using the unique ID for each pot in the phytometer phase of the experiment. The random intercept was selected to account for repeated measures on each phytometer  to evaluate grass growth over time. GAMs are similar to generalized linear models except that they replace linear covariates with local smoothing functions that enable modeling of nonlinear processes  as a function of the conditioning species, the sterilization status of the soil, and the interaction between the two. The pot ID number of the conditioned training soil was used as a random intercept with a fixed mean. Conditioned soils came from individual pots in the training stage that may differ in their abiotic and biotic features, so we chose to use mixed‐effects models to account for the variance in the strength of feedback due to these differences. A random effect was determined to be meaningful if the variance differed from zero, indicating individual pots from the training stage differed in their effect on the feedback. If the random effect was not meaningful, we ran the same formula as a generalized linear model (GLM). For GLMMs or GLMs of aboveground, belowground, and overall biomass data, the most parsimonious model was selected through comparison of AIC between full and reduced models. The type of model, whether an interaction term was used, and the R2 value for each model is indicated  relating biomass  suppressed the C3 grasses Pascopyrum smithii and Bromus inermis relative to growth in their home soils . Height of A. gerardi in sterile home soils was greater than in sterile redcedar soils , but greater than height in home live soils . Similarly, S. scoparium height in home sterile soils was much greater than in home live soil , indicating a strong negative feedback. S. scoparium height in live  and sterile  redcedar soils were shorter than in home sterile soils. There was no detectable difference in S. scoparium height when comparing growth in home live soils and sterile or live redcedar soils . The height of B. inermis was suppressed in sterile redcedar soils relative to live  and sterile  home soils and was also suppressed in live redcedar soils relative to live  and sterile  home soils . The height of P. smithii growth was suppressed in sterile redcedar soils relative to sterile  and live  home soils. The height of P. smithii growth was also suppressed in live redcedar soils relative to sterile  and live  home soils. Live redcedar soils suppressed the height of P. smithii relative to growth in sterile redcedar soils  when compared to the biomass of samples grown in sterile home soils. Shoot biomass of A. gerardi grown in live home soils was less than its biomass when grown in redcedar soils that were live  or sterile . No significant effects or interactions were found when modeling shoot biomass as a function of growth in home or redcedar soils and soil sterilization status.Plant–soil feedbacks where soil conditioned by redcedar suppressed shoot biomass were not detected for either C3 grasses in this experiment showed strong negative feedbacks when grown in redcedar soil  was contrasted. Shoot biomass of B. inermis was reduced when grown in live  or sterile  redcedar soils in comparison to shoot biomass in live home soils. Similarly, shoot biomass of B. inermis was reduced when grown in live  or sterile  redcedar soils in comparison to shoot biomass in sterile home soils. Shoot biomass of B. inermis did not differ when grown in live or sterile redcedar‐conditioned soils  and did not differ when grown in home live or sterile soils . Shoot biomass of P. smithii grown in sterile redcedar soils was reduced significantly when compared to live  or sterile  home soils and was reduced when grown in live redcedar soils when compared to live  or sterile  home soils. Shoot biomass of P. smithii did not differ when grown in live or sterile redcedar conditioned soils .The C4Bromus inermis and Pascopyrum smithii) grown in soil conditioned by redcedar experienced negative plant–soil feedback that suppressed their height and biomass. This suggests plant–soil feedback may increase survivorship of redcedar seedlings in their encroaching range depending on the local plant community at the site of establishment, much as it has done for other species combinations , making the observation of measurable feedbacks derived from biotic and abiotic sources noteworthy.In our experiment, grass growth in live and sterilized redcedar soil was reduced when compared to growth in live and sterilized home soils for the Cs Bever, . Therefos Bever,  or from s Bever, . In this3 species showed negative feedbacks and not C4 species. A possible explanation is that the C3 redcedar has novel weapons against these two species . Further studies are needed to determine the strength of this effect in field conditions, the size of the area of impact around trees, the longevity of the effect in the soil, and how the strength of suppression changes with tree size or age and with the age of surrounding perennial grasses.Our experimental results show a negative feedback for certain grasses grown in soil conditioned by redcedar, but interpretation of these results must also consider the myriad factors that influence plant–plant interactions in the field. Our study examined growth of individuals in a greenhouse, using potting mix and sand as soil substrates, and comparing live inoculations of conditioned soil with those that had been sterilized under heat and pressure. The microbial community of the potting soil at the onset of the training phase represents an unknown variable, outside of the mycorrhizal fungi that the manufacturers state they add. The strength of plant–soil feedbacks measured in artificial conditions have been found to be inflated relative to those observed in field conditions ; data curation (lead); formal analysis (lead); investigation (lead); methodology (lead); project administration (lead); resources (supporting); software (lead); supervision (supporting); validation (lead); visualization (lead); writing – original draft (lead); writing – review and editing . David Ward: Conceptualization (supporting); data curation (supporting); formal analysis (supporting); funding acquisition (lead); investigation (supporting); methodology (supporting); project administration ; resources (lead); software (supporting); supervision ; validation (supporting); visualization (supporting); writing – original draft (supporting); writing – review and editing .None declared.https://doi.org/10.21038/benn.2022.0901.This article has earned Open Data and Open Materials badges. Data and materials are available at Appendix S1Click here for additional data file.


The advent of a new generation of targeted cancer drugs requires a radically different design of early clinical trials. Here, René Bernards discusses why new clinical trial designs are needed and what is being done to achieve this. Such innovative trials can lead to similar outcomes for cancer patients with fewer side effects while at the same time reducing the cost of cancer care. In a series of Viewpoints, EACR Board members present their thoughts on current topics in cancer research. Here, René Bernards discusses why new clinical trial designs are needed and what is being done to achieve this. Such innovative trials can lead to similar outcomes for cancer patients with fewer side effects while at the same time reducing the cost of cancer care. FDAFood and Drug AdministrationMDICTmethodology for the development of innovative cancer therapiesMTDmaximum tolerated doseRDRrecommended dose rangeRP2Drecommended phase 2 doseOver the past decades, advances in our understanding of the molecular basis of cancer have resulted in drugs that are fundamentally different from the conventional chemotherapies that predate this revolution. Targeted cancer therapeutics selectively inhibit the oncoproteins that drive cancer proliferation and survival or stimulate the immune system to kill cancer cells . DespiteSo, what should be done instead? Various publications have highlighted the need for change in phase 1 trial design , 4, and ’ concerns the length of drug treatment after cancer surgery (adjuvant therapy). Drugs like trastuzumab (which targets HER2) are typically given for 12 months postsurgery for HER2‐positive breast cancer, but a recent trial showed that 6‐month treatment was noninferior to 12 months but was associated with less cardiotoxicity and fewer severe adverse events [Another case of ‘more may not be bettere events . Similare events . This beversus 12‐month trastuzumab be financed? I argue for the establishment of a revolving fund, with initial start‐up funding from health insurance companies. When a study shows the noninferior benefit from shorter treatment (or using a lower drug dose), part of the savings will go to the health insurance companies and another part is re‐invested in new studies with a similar aim. Over time, this will lead to better overall health outcomes at lower cost to society. Who can be against that?Studies that aim to reduce drug treatment length or dosing are difficult to finance, given the lack of financial incentive for the pharmaceutical industry to perform such studies. Clearly, the benefit of such noninferior treatments lies with the patients that may suffer fewer side effects and with the health insurance companies that can save costs and get better overall health outcomes. How should future studies like the above‐mentioned 6‐ The authors declare no conflict of interest.


A growing number of grandparents are assuming care of their grandchildren, and both grandparents and children report higher levels of stress than their peers, although the source of this stress remains largely unexplored. In Conger & Donnellan’s Family Stress Model, a parent’s distress can impact the levels of distress experienced by their child, and previous research indicates that parent/child relationship quality impacts children’s mental health. This study extended this to custodial grandfamilies, by investigating whether the relationship quality of custodial grandparents (GP) and grandchildren (GC) would moderate the relation between the health of GC and the wellness of their GP. Participants include 322 custodial grandparents within the United States recruited via Qualtrics Research Panel Survey . Measures included the Achenbach Child Behavior Checklist, Kivnick’s Grandparent Meaning Scale, and grandparents’ wellness. Moderation of relationship quality on the relation between the GP’s wellness and internalizing behaviors of the GC was tested using SPSS’ Process macro. A significant interaction was found  wherein GP’s wellness was not predictive of changes in GC’s behavior for the group with high relationship quality, but greater GP wellness was associated with less GC internalizing behaviors when relationship quality was low (p < .001). These findings indicate that when a GP feels that they have a more positive relationship with their GC, there is a greater likelihood that they will report fewer health concerns and fewer GC’s internalizing behavior symptoms.


Insect populations have become increasingly threatened during the last decades due to climate change and landuse intensification. Species characteristics driving these threats remain poorly understood. Trait‐based analyses provide a straight‐forward approach to gain a mechanistic understanding of species' extinction risk, guiding the development of conservation strategies. We combined morphological traits and phylogenetic relationship for 332 European species of butterflies and 115 species of odonates (dragon and damselflies) to model their red list status via phylogenetically controlled ordered logistic regression. We hypothesized that extinction risk increases with increasing body volume and wing area, decreasing range size, and is larger for brighter species. All investigated traits exhibited a strong phylogenetic signal. When controlling for phylogenetic relationship, we found that extinction risk of butterflies increased with decreasing range size. The extinction risk of odonates showed no relationship with the selected traits. Our results show that there is no universal trait defining the extinction risk of our investigated insect taxa. Furthermore, evolutionary history, measured as the phylogenetically predicted part of our analyzed traits, poorly predicted extinction risk. Our study confirms the focus of conservation measures on European butterfly species with small range sizes. A trait‐based analysis for European butterflies and odonates to gain a mechanistic understanding of species’ extinction risk. We found no universal trait defining the extinction risk of our investigated insect taxa. When controlling for phylogenetic relationship the extinction risk of butterflies increased with decreasing range size while extinction risk of odonates showed no relationship with the selected traits. To esti3 as a measure of body size, color lightness via the additive color mixing with the basic colors red‐green‐blue , wing area in cm2, and the geographical range size. These traits were available for both butterflies and odonates. We followed Pinkert et al.  as an estimate of the body size of a species based on the assumption that bodies of butterflies and odonates generally have a cylindrical form. The calculations were performed using functions of the R‐package png , whereas dorsal and ventral color estimates are correlated  as an estimate of the color lightness of a species . For estimating the color lightness of butterfly species, we focused on the body and 1/3 of the wing area closest to the body because this area is probably the most important for thermoregulation  wing area and the log‐transformed  body volume  to 4 . Ordinal scaled data require ordinal regression models ; formal analysis ; writing – original draft (lead); writing – review and editing . Stefan Pinkert: Conceptualization ; data curation (lead); writing – review and editing . Roland Brandl: Conceptualization ; writing – review and editing (supporting). Simon Thorn: Conceptualization ; methodology ; writing – original draft (supporting); writing – review and editing .Open Access funding enabled and organized by Projekt DEAL.


Ovarian cancer is the fifth most common female cancer in the United States. There have been very few studies investigating mental health diagnoses among ovarian cancer survivors with long‐term follow up. The aim of this study is to examine the incidence of mental illness among ovarian cancer survivors compared to a general population cohort. A secondary aim is to investigate risk factors for mental illnesses among ovarian cancer survivors.Cohorts of 1689 ovarian cancer patients diagnosed between 1996 and 2012 and 7038 women without cancer matched by age and birth state from the general population were identified. Mental health diagnoses were identified from electronic medical records and statewide healthcare facilities data. Cox proportional hazard models were used to estimate hazard ratios (HRs).Ovarian cancer survivors experienced increased risks of mental illnesses within the first 2 years after cancer diagnosis . The risks of depression among ovarian cancer survivors were nearly 3‐fold within the first 2 years of cancer diagnosis , and 1.69‐fold at 2–5 years after cancer diagnosis . Ovarian cancer survivors experienced an 80% increased risk of death with a mental illness diagnosis  and a 94% increased risk of death with a depression diagnosis .Higher risks of mental illnesses were observed among ovarian cancer survivors throughout the follow‐up periods of 0–2 years and 2–5 years after cancer diagnosis. Multidisciplinary care is needed to monitor and treat mental illnesses among ovarian cancer survivors. Furthermore, very few studies have investigated the risk factors for mental illness in ovarian cancer patients.The purpose of the present study is to estimate the incidence of mental health disorders among ovarian cancer survivors in comparison with a general population cohort. A secondary aim is to investigate risk factors for mental illnesses among ovarian cancer survivors.2An initial cohort of 2793 ovarian cancer survivors was identified from the statewide SEER Utah Cancer Registry (UCR). Each ovarian cancer survivor was matched with one to five cancer‐free Utah residents from the Utah Population Database (UPDB). The eligibility criteria were: Women diagnosed with an invasive first primary ovarian cancer at 18 years of age or older, between 1996 and 2012 in the state of Utah (SEER ICD‐O‐3 codes: C569). A total of 206 ovarian cancer patients were excluded: 122 because their cancer stage was missing, 24 because an individual from the general population could not be matched, and 60 because their Utah residence did not exceed 1 year. There were 1689 ovarian cancer survivors in the final study sample. 7038 women were matched with each ovarian cancer survivors by birth state, birth year, and follow‐up time from the non‐cancer general population. This study was approved by the University of Utah's Resource for Genetic and Epidemiologic Research (the oversight committee for the UPDB) and the University of Utah Institutional Review Board.Outcome data used for this study included statewide ambulatory and inpatient data from the Utah Department of Health and electronic medical record data from Intermountain Health Care and the University of Utah Health Sciences Center. Data from the UPDB included records from the UCR, Utah driver's licenses, vital records, and the Utah Department of Health. Outcome data included all available ICD‐9 diagnosis codes and diagnosis dates for mental illnesses. The Clinical Classification Software developed by the Health Cost and Utilization Project was used to categorize ICD‐9 codes into three levels of specificity (Levels 1–3) (Table 3), with Level 1 being the broadest  down to Level 2  and Level 3 being the most specific . Follow‐up time for incident cases of each outcome was calculated from the ovarian cancer survivor's initial cancer diagnosis to the date of diagnosis, last date of follow‐up, or date of death. The first date of follow‐up of the general population was established as the date of diagnosis of their matched ovarian cancer survivors. Individuals who did not have that outcome were censored at the date of last follow‐up if that date fell within the analysis period. Level 3 outcomes diagnosed prior to the start of each analysis period were considered prevalent cases of those outcomes, and individuals were excluded from the relevant models. Level 2 outcomes were broader and contained multiple disparate conditions; thus, we did not exclude prevalent diagnoses.Chi‐square tests were used to compare baseline characteristics between the ovarian cancer survivor and general population cohorts. Cox proportional hazards (PH) models were used to calculate hazard ratios (HR) for long‐term mental illness outcomes and risk factors. Cox PH models were adjusted for matching factors (age and birth state), baseline body mass index (bBMI), baseline Charlson Comorbidity Index (bCCI), and race/ethnicity. The bCCI was calculated using all medical record data prior to the date of cancer diagnosis.The PH assumption was checked for each model using a test for nonzero slope of the Schoenfeld residuals versus time. Models that were in violation of the PH assumption were then fit with flexible parametric survival models with restricted cubic splines. HRs from the Cox PH models were reported where there were no substantive differences.p < 0.05 was considered statistically significant. Analyses were performed using SAS 9.4, except the Flex Spline model using Stata 17.Baseline BMI values at least 1 year prior to ovarian cancer diagnosis were calculated from the driver's license records for both cohorts. For individuals missing BMI, values were imputed using fully conditional specification discriminant function methods with cancer diagnosis, bCCI, and age at ovarian cancer diagnosis as covariates. Models were run with and without the imputed values to assure that the inferences did not change due to the imputation of BMI. All statistical tests were two‐sided, and a 3Ovarian cancer survivors had a higher proportion of having a baseline comorbidity compared to the general population cohort (36.9% vs. 30.7%) Table . A higheApproximately 67.6% of the ovarian cancer survivors were diagnosed at distant stage, and 51.7% with a histology subtype of high‐grade serous Table . The majAn overview of adjusted HRs is shown in Figure Elevated risk for adjustment disorders was observed among ovarian cancer survivors compared with the general population cohort between 0–2 years  and between 2–5 years . Increased risk for bipolar disorder diagnosis was observed among ovarian cancer at 0–2 years  compared to the general population cohort. Although not statistically significant, an increased risk of bipolar disorder was also observed at 2–5 years . Ovarian cancer survivors were more likely to be diagnosed with schizophrenia and other psychotic disorders during 0–2 years  and 2–5 years  after cancer diagnosis. The risk of substance‐related disorder among ovarian cancer survivors were also increased at 0–2 years  and 2–5 years after cancer diagnosis .Cancer treatment and later diagnosis year were associated with an increased risk of any mental illness at 0–2 years after cancer diagnosis among ovarian cancer survivors Table . DistantFigure 4To our knowledge, this is the first study estimating the risk of mental illness diagnoses among ovarian cancer survivors compared to a general population of women without cancer, and the first to follow ovarian cancer patients over both short‐term and long‐term periods. Our analyses show that ovarian cancer survivors had increased risks of various mental illnesses compared to the general population, as well as higher risks of psychological disorders including adjustment disorder, anxiety disorder, delirium dementia, and cognitive disorder, mood disorder (bipolar and depression disorder), schizophrenia and other psychotic disorders, and substance‐related disorders. We also observed that distant‐stage disease was a significant risk factor for both mental illnesses overall and depression among ovarian cancer patients. Ovarian cancer survivors with mental illnesses or depression had lower survival time than those without these diagnoses. Our findings provide further evidence of the psychological burden among ovarian cancer survivors and highlight the need to improve mental health support for ovarian cancer patients.We identified significantly increased risks of mental illnesses among ovarian cancer survivors compared to the general population, especially for depression and anxiety disorders. Similar observations regarding the high incidence of depression and anxiety within 2 years after cancer diagnosis were reported in previous studies.Our findings are consistent with prior studies indicating that cancer patients experience elevated risks of adjustment disorder.Long‐term substance abuse may be a trigger for bipolar disorder and schizophrenia.Among risk factors for mental illnesses, ovarian cancer survivors who receive combined treatment of surgery and chemotherapy had an elevated risk of any mental illness. This finding is consistent with the existing studies that ovarian cancer patients with surgery and adjuvant chemotherapy were more likely to experience mental disorders due to poor body image, side effects from treatment such as nausea and fatigue, and other fatal diseases.Compared to local‐stage ovarian cancer, our study suggests that distant‐stage ovarian cancer may be a potential risk factor of any mental illness and depression. Our results are consistent with findings from prior research reporting that ovarian cancer patients with more advanced or recurrent disease have greater psychological distress,We observed that ovarian cancer survivors who have developed mental illness or depression also had increased risks of death. Notably, the mortality risks in our study were higher than the mortality risks in a population‐based study of other types' cancer, which the risks ranged from 1.03 to 1.29, compared to the cancer patients without psychological distress.The major strength of this study is the population‐based design with a large sample size of over 1600 ovarian cancer survivors. This large population allowed us to study both common and rare mental health outcomes over a long‐term (up to 20 years) follow‐up period. The electronic medical record data from two of the largest health‐care providers in Utah  improve our capability to capture the important information that included patients at physical and psychological functioning. Additionally, this study does not rely on self‐reported data, which minimizes recall error and survival bias.This study also has a number of limitations. First, electronic medical record data led to a risk of coding errors which may overlook the true impact of mental illnesses among ovarian cancer survivors. Second, another limitation is missing bBMI data in some participants, which we calculated by imputation. However, we conducted sensitivity analysis to assure that the imputed BMI value did not influence the inferences in our study. Third, the treatment category on medical records was broad which also limited our analysis. Fourth, the high mortality rate of ovarian cancer patients and long‐term property in our study may still result in survival bias, which could underestimate the impacts of mental illnesses among ovarian cancer survivors. Fifth, recurrence information is not available in this study, which could be a strong risk factor for both mental illness and mortality.In summary, compared to the general population, the risks of a range of mental disorders were elevated among ovarian cancer survivors. These risks were decreased over the follow up years, but they remained high even in long‐term follow‐up, compared with the general population. We identified several potential determinants of mental illness and depression among ovarian cancer survivors, such as the treatment regimen including surgery and chemotherapy, distant cancer stage, mucinous histology subtype, and higher baseline CCI. This finding emphasizes the importance of providing psychological support after cancer diagnosis and provides new insight to potentially prolong the lives of ovarian cancer survivors.Siqi Hu: Conceptualization and design, formal data analysis, original draft. David Baraghoshi: Data analysis, review and editing. Chun‐Pin Chang, Kerry Rowe, John Snyder, Vikrant Deshmukh, Michael Newman, Alison Fraser, and Ken Smith: Data acquisition and method development. Anita R. Peoples and David Gaffney: Critical revision of the manuscript. Mia Hashibe: Funding acquisition, conceptualization, overall supervision.The authors declare no potential conflicts of interest.This work was supported by grants from the NIH , the Huntsman Cancer Institute, and the Cancer Control and Population Sciences Program (HCI Cancer Center Support Grant P30CA042014). This research was supported by the Utah Cancer Registry, which is funded by the National Cancer Institute‘s SEER Program, Contract No. HHSN261201800016I, the US Center for Disease Control and Prevention's National Program of Cancer Registries, Cooperative Agreement No. NU58DP0063200‐01, with additional support from the University of Utah, and Huntsman Cancer Foundation. Partial support for all datasets within the Utah Population Database is provided by the University of Utah, Huntsman Cancer Institute and the Huntsman Cancer Institute Cancer Center Support grant, P30 CA42014 from the National Cancer Institute.Supplementary Table 1Click here for additional data file.