The results confirmed that both SVR and correlation strength affect greatly the CCA stability—the CCA stability cannot be guaranteed if the SVR is not sufficiently high or the brain‐behavior relationship is not sufficiently strong. Based on our quantitative characterization of CCA stability, we provided a practical guideline to help correct interpretation of CCA results and proper applications of CCA in neuroimaging studies on brain‐behavior relationships.Canonical correlation analysis (CCA), a multivariate approach to identifying correlations between two sets of variables, is becoming increasingly popular in neuroimaging studies on brain‐behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Although it is known that the number of subjects should be greater than the number of variables due to the curse of dimensionality, it is unclear at what subject‐to‐variable ratios (SVR) and at what correlation strengths the CCA stability can be maintained. Here, we systematically assessed the CCA stability, in the context of investigating the relationship between the brain structural/functional imaging measures and the behavioral measures, by measuring the similarity of the first‐mode canonical variables across randomly sampled subgroups of subjects from a large set of 936 healthy subjects. Specifically, we tested how the CCA stability changes with SVR under two different brain‐behavior correlation strengths. The same tests were repeated using an independent data set  is becoming increasingly popular for studying brain‐behavior relationships. However, the CCA stability in neuroimaging applications has not been systematically investigated. Here, we systematically tested the CCA stability and confirmed that both subject‐to‐variable ratios and correlation strength affect greatly the CCA stability. Although all these methods aim to examine relationships between different variables, different method fits in different situation. Pearson's correlation analysis is for examining the relationship between two single variables. Multiple regression analysis is for examining relationships between a single variable  and a set of variables . CCA, firstly introduced by Hotelling in 1936  sampled from a large data set of healthy young adults (n = 936). As the whole data set from which each subgroup is sampled is homogeneous, the CCA results obtained based on each subgroup are expected to be similar if the results are stable. Considering that subject overlapping rate may influence the similarity of the CCA results between different subgroups due to inter‐subject differences, subjects in each subgroup were sampled using a pseudorandom manner to obtain a series of subject overlapping rates between subgroups. Therefore, for each SVR, correlation strength scenario, and subject overlapping rate, the CCA stability was assessed by the similarity of the first CCA mode between randomly sampled subgroups from three aspects: the similarity of canonical correlation coefficients (CCC), the consistency of the statistical significance, and the similarity of canonical variables. Importantly, we further validated our findings by repeating the same analyses in another independent data set (n = 700) from the Human Connectome Project (HCP). Note that real data were used to test the CCA stability in the present study as it is very difficult to mimic all aspects of a real data set using simulated data. For example, the variables within each variable set are likely to be related to each other in a very complex manner which is very difficult, if possible at all, to be captured using simulated data. In addition, although a ground‐truth CCC may be predefined and thus available in simulated data, it is difficult to use it to practically guide real data analysis as the true correlation strength between two sets of variables is unknown in real situations.Therefore, in the present study, we aimed to test the stability of CCA between brain imaging  and subject measures  data by examining whether similar results could be obtained when using randomly sampled subjects from a homogeneous population for different data dimensionalities and brain‐behavior correlation strengths. More specifically, we used PCA, the most commonly used dimension reduction method in CCA studies  were included in the Tianjin data set. Further exclusion criteria include MRI contraindications, non‐Han Chinese, left‐handedness or ambidexterity, more than 20 cigarettes smoked by the time of enrollment, history of excessive drinking, drug abuse or dependence, women in pregnancy or menstruation, neuropsychiatric disorders or major physical diseases in present or past, any current medication (include birth control pills), color blindness or color discrimination disorder, brain lesions or structural abnormalities. All participants were recruited from two sites in Tianjin, China, including Tianjin Medical University General Hospital and Tianjin Medical University Cancer Institute and Hospital. From each participant, we collected the structural and functional MRI data along with a series of assessments including basic demographical, environmental, and cognitive questionnaires. Ethical approval was obtained from the medical ethics committee of Tianjin Medical University General Hospital and the medical ethics committee of Tianjin Medical University Cancer Institute and Hospital prior to the study and written informed consent was obtained from each participant before enrollment.A total of 1,104 healthy right‐handed participants  aged between 18 and 30 years  sequence with TR = 2,000 ms, TE = 30 ms, ST = 3 mm, spacing between slices = 4 mm, FA = 90°, number of slices = 36, and matrix size = 64 × 64. The resting‐state fMRI scan lasted 6.17 mins, resulting in 185 volumes.MRI was performed on 3 ‐Tesla GE Discovery MR750 scanners using an eight‐channel head‐receiving coil at Tianjin Medical University General Hospital and Tianjin Medical University Cancer Institute and Hospital. T1‐weighted MRI data were acquired using the following parameters: repetition time (TR) = 8.16 ms, echo time (TE) = 3.18 ms, inversion time (TI) = 450 ms, slice thickness (ST) = 1 mm, flip angle (FA) = 12°, field of view (FOV) = 256 × 256 mmPersonal information and assessments were also collected for each participant and there are 91 items in total including demographics , cognitive and behavioral performances  and natural and social environmental measures . These 91 items are referred to as “subject measures” hereafter, as opposed to “imaging measures.”http://www.neuro.uni-jena.de/vbm8)  running on Matlab platform . More specifically, a structural brain template was firstly generated based on the same group of subjects using the high‐dimensional Diffeomorphic Anatomical Registration Through Exponentiated Lie Algebra (DARTEL) algorithm  were entered into the subsequent CCA analyses.The structural T1‐weighted images were processed to extract voxel‐wise gray matter volume (GMV) using the Voxel‐Based Morphometry toolbox  software  with 3‐Tesla T1 anatomical MRI data from the Human Connectome Project (HCP) . In brief, all participants were scanned at Washington University, using a Siemens MR scanner with a 32‐channel receiving head coil. Structural T1‐weighted MRI data were acquired using the following parameters: TR = 2,400 ms, TE = 2.14 ms, TI = 1,000 ms, FA = 8°, FOV = 224 × 224 mm2, and voxel size = 0.7 × 0.7 × 0.7 mm3. Subject measures, including 494 variables in total, were also collected for each participant, including demographics  and cognitive and behavioral performances .Data acquisition details can be found in , 10 variables were used to assess menstruation in women and missing in all men, 2 variables were bedtime and getting‐up time, 2 variables were zygosity‐related indicators and 8 variables were hormone‐related endocrine indicators, and thus these 204 variables were discarded in the subsequent analyses. Among the remaining 290 subject measures, missing values still existed in 413 subjects and thus these 413 subjects were discarded. Finally, 290 subject measures of 700 subjects were used in the subsequent CCA analyses.2.2X  and Y , CCA seeks two transformation vectors A1 and B1 such that the new variables U1 = A1TX and V1 = B1TY are maximally correlated, where A1 and B1 are often called the first pair of canonical vectors or weights and the superscript T indicates the operator “transpose”. The new variables U1 and V1 are called the first pair of canonical variables. The Pearson correlation coefficient between the canonical variables U1 and V1 is called the canonical correlation coefficient (CCC) of the first pair. And this maximal canonical correlation pattern is called the first mode of CCA. Then the second pair of canonical variables U2 and V2 that is orthogonal to the first pair can be similarly obtained. And this procedure can continue until up to p pairs of canonical variables are identified where p is the maximum number of variables between the two sets. Therefore, the first mode represents the pattern with the maximal canonical correlation between two variable sets. In addition, to determine the relationship between each original variable and the canonical variable, the loading of each original variable is often calculated as the correlation coefficient between the original variable and the corresponding canonical variable . For each set, the loadings of all variables compose the loading vector. The variables with the highest loadings can be interpreted as most strongly associated with, and thus have the most contributions to, the canonical correlation pattern  between the two sets of variables  was pseudorandomly split into two subgroups  while controlling the subject overlapping rate of the two subgroups. Although there are usually no overlapping subjects in real situations, to test how robust the CCA results would be when the sampled subjects were different, we generated pairs of subgroups with a series of subject overlapping rates ranging from 0 to 450 overlapping subjects with an increment of 50 in each step using the following procedure. To generate two subgroups with 0 overlapping subjects, 468 subjects were randomly selected to form one subgroup and the remaining 468 subjects were used to form the second subgroup; and this procedure was repeated 1,000 times, resulting in 1,000 pairs of subgroups with 0 overlapping subjects. To generate two subgroups with 50 overlapping subjects, 50 subjects were randomly selected and assigned to both subgroups, and 418 subjects (468–50 = 418) were randomly selected from the remaining 886 subjects (936–50 = 886) and assigned to the first subgroup and another 418 subjects were randomly selected from the remaining 468 subjects (936–50−418 = 468) and assigned to the second subgroup; and this procedure was repeated 1,000 times, resulting in 1,000 pairs of subgroups with 50 overlapping subjects. Similarly, we also generated 1,000 pairs of subgroups with 100, 150, up to 450 overlapping subjects. To avoid over‐fitting problem, PCA was applied to imaging data (GMV/ReHo) and subject measure data separately to reduce the data dimensionality. Note that, each variable of the brain imaging data and the subject measure data was standardized to Z score (with zero mean and unit variance) before PCA, and the obtained PCs were standardized to Z scores again before entering CCA. To test how dimensionality affects the CCA stability, we reduced the imaging data to a series of dimensionalities ranging from 50 to 450 with an increment of 50, while the dimensionality of the subject measure data was fixed to 50 since there were only 78 variables in total. This resulted in a series of SVRs from 9.36  to 1.04 . Here, the SVR was calculated based on the dimensionality of the imaging measures as the dimensionality of the subject measures was kept fixed.The procedure of the stability assessments for CCA results is illustrated in the workflow chart in Figure n = 100, in order to reduce the computational load). Here, as the two subgroups of subjects in each pair were randomly selected from a homogeneous data set, the CCA results  were expected to be similar if they are stable. Therefore, the stability of CCA results was assessed as the similarity between the CCA results obtained from two randomly selected subgroups of subjects in each pair for each subject overlapping rate and each data dimensionality from the following three aspects: (a) the similarity of the first‐mode CCCs obtained from two paired subgroups (Assessment 1): it was measured by the absolute difference of the CCCs obtained from the two subgroups for each pair, and then the mean and the SD of the absolute differences in CCC across the 1,000 pairs were calculated; (b) the consistency of the statistical significance of the first‐mode CCCs obtained from two paired subgroups (CCCs were considered to be significant when p < .05) (Assessment 2): there are three possible outcomes when comparing the statistical significance of CCCs between the two subgroups for each pair—both were significant, both were insignificant, and one significant but the other insignificant—and the percentage of each outcome was calculated over the 1,000 pairs of subgroups; (c) the similarity of the canonical variables of the first modes  obtained from two paired subgroups, assessed for brain imaging measures and subject measures separately (Assessment 3): it was measured by the absolute correlation coefficient of the loading vectors between the two subgroups for each pair and then their mean and the SD across the 1,000 pairs were calculated. Note that, the similarity of the first modes of the two subgroups in each pair  cannot be directly assessed by calculating the correlation coefficient between the U1/V1 of the first subgroup and the U1/V1 of the second subgroup because the direction of U1/V1 is dependent of the order of the subjects included in a particular subgroup but there is no correspondence between the subjects of the two subgroups. Calculating the correlation coefficient between the loading vectors of the two subgroups avoids this problem because the loading vectors are composed of variables (which are the same for the two subgroups) rather than subjects (which are different for the two subgroups).After PCA, for a given subgroup of subjects, CCA was applied to identify the relationship between brain imaging measures and subject measures. Only the first mode, corresponding to the maximal correlation between the two types of variables and thus the most important and the most commonly reported results in a CCA study, was used for the subsequent CCA stability assessments to avoid excessively complicated analyses. The statistical significance of the first‐mode CCC of each subgroup was determined by permutation tests  before CCA in the above procedure (denoted as “main procedure” hereafter). Although the subjects of each subgroup were randomly sampled from a homogeneous population, the obtained PCs for the two subgroups within each pair may not be the same because the subjects composing each subgroup were not the same  and each of the 10 subject overlapping rates (ranging from 0 to 450 in step of 50), 1,000 pairs of subgroups were created and thus CCA between imaging measures (GMV/ReHo) and subject measures was performed 2,000 times , one for each subgroup. The mean and the 3.1.2SD across the 1,000 pairs are shown in Figures The absolute difference of the two CCCs within each of the 1,000 pairs of subgroups was then calculated, and their mean and 3.1.3The consistency of the statistical significance of CCCs was represented by the percentages of the three possible outcomes  over the 1,000 pairs of subgroups shown in the pie charts in Figure 3.1.4U1/V1) obtained from the two subgroups within each of the 1,000 pairs was measured by the absolute correlation coefficient of the loading vectors between two subgroups for brain imaging measures and subject measures separately. Their mean and SD across the 1,000 pairs for all combinations of data dimensionality and subject overlapping rate are shown in Figures SD) when the subject overlapping rate was 0 in Figures The similarity of the canonical variables of the first modes , we noticed that three subject measures  had very large loadings , we repeated the same analyses for GMV using 10,000 permutations. We observed similar results, shown in Figure We found that, after removing the three subject measures, although many trends of how these stability measures change with the subject overlapping rate and with the data dimensionality remained the same, several differences between the two sets of results Figures , and S3 3.3All the above analyses were repeated using the HCP data set in a “strong correlation” scenario as well as a “moderate correlation” scenario (only GMV was used because very similar stability assessment results were observed for GMV and ReHo when using Tianjin data set). When using all subject measures, we observed very similar results with those obtained using Tianjin data set in the “strong correlation” scenario Figures : (a) the3.4When controlling for the possible contribution of PC inconsistency to the CCA stability assessment in the “control procedure” using Tianjin data set, we found almost identical results in the “strong correlation” scenario Figures , and S3.4n = 936) when changing data dimensionality, correlation strength between brain imaging measures and subject measures, and subject overlapping rate. The stability of the CCA results was assessed systematically from three aspects: the similarity of the first‐mode CCC (Assessment 1), the consistency of the corresponding statistical significance (Assessment 2) and the similarity of the first‐mode canonical variables (Assessment 3) between paired subgroups. Importantly, we repeated all analyses using two different brain imaging measures  and also in a second independent data set (n = 700) to validate the results. We observed similar results using structural  and functional  imaging measures in two independent data sets, with and without the possible confound of PC inconsistency. The main findings are summarized as follows. First, both the data dimensionality  and the correlation strength between brain imaging measures and subject measures affect the CCA stability considerably—the stability of CCA results decreased with the increase of data dimensionality and with the decrease of the correlation strength between the two sets of measures. Specifically, when the correlation strength was strong , the CCA results were generally stable as long as the data dimensionality was considerably below the sample size ; however, the stability of CCA results cannot be guaranteed if the correlation between the two sets of variables was only moderate  even for a very high SVR —in our case, for a sample size of 468 of Tianjin Data set, the CCA results were still instable when the dimensionality of imaging measures was no higher than 50 with the dimensionality of subject measures being fixed to 50. In other words, the CCA results are expected to be stable only when the SVR is high and, at the same time, the correlation between the investigated brain imaging measures and subject measures is strong. Second, if the above stability requirements  were not met, even just a slight change of subjects in the data to be analyzed could change the CCA results greatly; that was, for an insufficient SVR or a moderate correlation, the CCA results could still be instable even if the subjects in the two subgroups were highly overlapped  and the correlation strength between the two sets of variables of interest .Regarding the correlation strength between brain imaging measures and subject measures, we found that CCA results were generally stable  for a range of higher SVRs  if there was a strong correlation between the two sets of variables  but became clearly instable  even for very high SVRs  if there was only a moderate correlation between the two sets of variables. This result was observed for all subject overlapping rates , for both the structural imaging measures  and the functional imaging measures (ReHo), and in both data sets, indicating that CCA is recommended only when a strong relationship between the two sets of variables is expected. This finding is particularly important as the influence of the correlation strength on the stability of CCA results has been largely overlooked in previous studies. Indeed, many previous studies reported moderate CCCs—for example, 0.43 for a SVR of 8  and functional imaging measures (ReHo), and in both data sets. This result is also consistent with the previous findings obtained based on simulated data showing that the power of CCA to detect a significant canonical correlation decreased as the SVR decreased , the CCA results were still instable—inconsistent statistical significance and/or very low correlation coefficients between loading vectors when the dimensionality was high  obtained from a discovery data to a held‐out data and tested whether the statistical significance of the correlation between the resultant pseudocanonical variables of the held‐out data were consistent with that of the discovery data. The detailed analysis procedure is provided in the Supplemental Methods  and the detailed results are shown in Figure In addition to the above assessments of CCA stability which were based on the similarity of the CCA results obtained from two independent CCAs performed in two subgroups, we also assessed the CCA stability using a different strategy—we directly applied the canonical weights  would be considerably worse based on the current results—the stability of CCA results is strongly dependent on the correlation strength. Indeed, the CCCs of the second or the third mode would be smaller than that of the first mode by design , and thus all subsequent pairs of canonical variables are less likely to be stable compared with the first pair. Similarly, we did not include a “weak correlation” scenario in the present study because it is expected that the CCA results would be instable when the two sets of variables are only weakly correlated.Furthermore, it is worth mentioning that, although only the first CCA mode was tested in the present study to avoid excessively complicated analyses, it seems reasonable to infer that the stability of the subsequent CCA modes . Therefore, the CCC alone is not interpretable—the CCC should always be considered and reported together with the corresponding SVR. However, this issue is sometimes overlooked in previous studies where CCCs were interpreted as strong or weak without considering the SVRs , the number of variables  in each variable set should not be higher than two thirds  of the sample size as the stability of the CCA results is not guaranteed otherwise. In practice, for a given sample size, if the number of the original variables does not meet this criterion, it is recommended to perform PCA on the original variable set and select the appropriate number of PCs which can explain as much original variance as possible.The SVR  should be reported alongside with the CCC. Interpreting the magnitude of CCC as an absolute measure of the correlation between brain imaging measures and subject measures should be avoided.Based on the findings discussed above, we provide the following guidelines if one plans to apply CCA in neuroimaging studies to investigate the relationship between brain imaging measures and subject measures:4.46 CCAs in total for just the Tianjin data set, our assessment of the statistical significance of each first‐mode CCC was determined by a relatively small number of permutation tests (n = 100). Although it is unlikely that the observed instable results in the “moderate correlation” scenario were due to a low number of permutations as similar results were obtained even using a much larger number of permutations  is recommended in a real CCA application. Third, a few modified versions of the classical CCA, such as kernel CCA  for each of the 720 combinations , leading to 1.44 × 105Although CCA is a promising multivariate approach which holds many advantages in exploring the relationship between the human brain and behavior, CCA cannot be used without restriction. The results of our systematic investigation of CCA stability in the context of neuroimaging, replicated in both structural and functional imaging measures and in two independent data sets, showed that two requirements need to be met at the same time to ensure the stability of CCA results: a sufficiently high SVR and a sufficiently strong correlation between the investigated brain imaging measures and subject measures. Importantly, we characterized the CCA stability quantitatively from three aspects using a series of SVRs in different correlation strength scenarios and provided a guideline for proper applications of CCA in neuroimaging studies based on our findings.The authors declare no competing financial interests.Ethical approval of the Tianjin Dataset was obtained from the medical ethics committee of Tianjin Medical University General Hospital and the medical ethics committee of Tianjin Medical University Cancer Institute and Hospital prior to the study and written informed consent was obtained from each participant before enrollment.Appendix S1 Supporting Information.Click here for additional data file.


N = 293). We revealed the developmental trajectories of 20 neurochemicals in two key developmental brain regions . We found that certain neurochemicals exhibited similar developmental trajectories across the two regions, while other trajectories were region‐specific. Crucially, we mapped the connectivity of the brain regions IPS and MFG to the rest of the brain across development as a function of regional glutamate and GABA concentration. We demonstrated that glutamate concentration within the IPS is modulated by age in explaining IPS connectivity with frontal, temporal and parietal regions. In mature participants, higher glutamate within the IPS was related to more negative connectivity while the opposite pattern was found for younger participants. Our findings offer specific developmental insights on the interplay between the brain's resting activity and the glutamatergic system both of which are crucial for regulating normal functioning and are dysregulated in several clinical conditions.Neurochemical profile and brain connectivity are both critical aspects of brain function. However, our knowledge of their interplay across development is currently poor. We combined single‐voxel magnetic resonance spectroscopy and resting functional magnetic resonance imaging in a cross‐sectional sample spanning from childhood to adulthood which was reassessed in ~1.5 years . We demonstrated that glutamate concentration within the IPS is modulated by age in explaining IPS connectivity with frontal, temporal, and parietal regions. Our findings offer specific developmental insights on the interplay between the brain's resting activity and the glutamatergic system both of which are crucial for regulating normal functioning and are dysregulated in several clinical conditions. Specifically, various neurochemicals exert differential effects on brain function with excitatory  and inhibitory  neurochemicals inducing neural excitation and inhibition respectively  and creatine were shown to increase while levels of glutamate were shown to decrease across development  and quantified neurochemical concentrations, such as glutamate and GABA, with single‐voxel By combining MRS and resting fMRI technologies in a cross‐sectional design, our aim was two‐fold: to (a) delineate the developmental trajectories of a wide range of neurochemicals from early childhood to adulthood in two key developmental regions , and to (b) investigate the interplay between neurochemicals and brain connectivity across development. We focus on the left frontal and parietal regions as their functional profile in various domains of functioning was shown to change from childhood to adulthood  on a 3T Siemens MAGNETOM Prisma MRI System equipped with a 32 channel receive‐only head coil. Anatomical high‐resolution T1‐weighted scans were acquired .2.2.1https://www.cmrr.umn.edu/downloads/mrspa/). Two 20 × 20 × 20 mm3 voxels of interest were manually centered in the left intraparietal sulcus (IPS) and the left middle frontal gyrus (MFG) based on the individual's T1‐weighted image while the participants were lying down in the MR scanner. Acquisition time per voxel was 10–15 min including voxel placement and B0 shimming  sequence  Cramér–Rao bounds and (b) the signal‐to‐noise ratio  absolute concentration referenced to water, (b) absolute concentration referenced to total creatine, (c) tissue‐corrected concentration, and (d) T2‐corrected concentration. The first neurochemical quantification method  is automatically obtained from the LCModel, and it is the water reference neurochemical concentration. The second neurochemical quantification method  is also automatically obtained as part of the LCModel output and it is essentially the concentration obtained from the first quantification method but referenced to the concentration of total creatine. Please note that, no tissue and no relaxation corrections are featured in these two concentration quantification methods . However, the third neurochemical quantification method , is the tissue‐corrected concentration and it is calculated based on Equation Absolute neurochemical concentrations were then scaled based on the structural properties of the selected regions and based on the predefined values shown Equation The values 43,300, 35,880, and 55,556 are the water concentrations in mmol/L for GM, WM, and CSF, respectively, and these are the default water concentrations values employed by LCModel  met these criteria and any measures contained those were excluded from the analyses. Creatine and phosphocreatine had an absolute value of cross‐correlation >.5 and thus we report only their sum instead of reporting them separately  . The present study featured two seeds or regions of interest: the left MFG and the left IPS. The MFG region was defined based on the Harvard‐Oxford Atlas and the IPS which was defined based on the Dorsal Attention network atlas; both atlases are featured within the CONN toolbox. The reason we used a different atlas for the IPS is that this region is not featured in the other atlas. We run seed‐to‐voxel analyses which probe how a seed is connected to the (voxels in the) rest of the brain. We choose to focus on the seed‐to‐voxel approach than the seed‐to‐seed approaches as we aimed to examine the impact of local neurochemical concentration on the whole‐brain rather than to nodes of a particular predefined network. In other words, we choose to remain agnostic concerning the target regions and employed a more conservative seed‐to‐voxel method. The benefit of this approach is that it allowed us to empirically map a “neurochemically‐defined” network that is not based on any prior regional knowledge of the classical resting networks, as this was already been done previously  and the voxelwise p‐value threshold was (p‐uncorrected <.001).Functional images were acquired with a multi‐band acquisition sequence . Resting fMRI data were preprocessed and analyzed using the CONN toolbox , we run a variant of Equation Lastly, to define the cutoffs of the developmental findings we utilized the Johnson–Neyman technique  delineated the developmental trajectories of understudied neurochemicals in two key developmental regions, and we (b) delineated how development modulates the relationship between regional neurochemical concentration and brain connectivity.The main aim of the present study was to investigate the neurochemical basis of brain connectivity fluctuations as a function of development. Importantly, our study was specifically designed to examine whether age modulates the impact of neurochemical concentration on brain connectivity. We found that glutamate concentration within the IPS is modulated by age in predicting connectivity between IPS and frontal, parietal, and temporal regions. What does this modulation of age on the relationship between glutamate concentration and brain connectivity tell us about how activity at a cellular level relates to long‐range network connectivity across development? The cortical organization is hierarchical in that neurons form micro‐scale circuits which in turn form nodes which then, in turn, form macro‐scale networks  receptors , taurine and macromolecules. Indeed, our study quantified the two most abundant antioxidants in the central nervous system, glutathione, and ascorbate (vitamin C). Antioxidants counteract oxidative stress which can lead to compromised brain function in normal aging and neurodegenerative disease Foster, . GlutathTaurine is the second most abundant amino‐acid in the brain, and our study revealed a reduction in taurine levels over human development  their signal of which originate from cytosolic proteins and includes numerous resonances with a complex pattern (Koob et al., In sum, by combining MRS with resting fMRI we examined the interplay between neurochemicals and brain connectivity from early childhood to adulthood. First, we tracked the developmental trajectory of multiple neurochemicals in key developmental regions, revealing novel associations, and highlighted their developmental significance. Importantly, we delineated the role of age in shaping the relationship between parietal glutamate and parietal‐based brain connectivity. Our findings offer specific developmental insights on the interplay between the brain's resting activity and the glutamatergic system both of which are crucial for regulating normal functioning and are dysregulated in several clinical conditions.The authors declare no potential conflict of interest.Roi Cohen Kadosh conceived the idea; Roi Cohen Kadosh, Uzay Emir designed the experiments; George Zacharopoulos performed the experiments; George Zacharopoulos, Uzay Emir analyzed the data, George Zacharopoulos, Uzay Emir, and Roi Cohen Kadosh wrote the article.Appendix S1: Supporting Information.Click here for additional data file.


Anopheles hinesorum is a mosquito species with variable host preference. Throughout New Guinea and northern Australia, An. hinesorum feeds on humans  while in the south‐west Pacific's Solomon Archipelago, the species is abundant but has rarely been found biting humans (it is exclusively zoophagic in most populations). There are at least two divergent zoophagic (nonhuman biting) mitochondrial lineages of An. hinesorum in the Solomon Archipelago representing two independent dispersals. Since zoophagy is a derived  trait in this species, this leads to the question: has zoophagy evolved independently in these two populations? Or conversely: has nuclear gene flow or connectivity resulted in the transfer of zoophagy? Although we cannot conclusively answer this, we find close nuclear relationships between Solomon Archipelago populations indicating that recent nuclear gene flow has occurred between zoophagic populations from the divergent mitochondrial lineages. Recent work on isolated islands of the Western Province (Solomon Archipelago) has also revealed an anomalous, anthropophagic island population of An. hinesorum. We find a common shared mitochondrial haplotype between this Solomon Island population and another anthropophagic population from New Guinea. This finding suggests that there has been recent migration from New Guinea into the only known anthropophagic population from the Solomon Islands. Although currently localized to a few islands in the Western Province of the Solomon Archipelago, if anthropophagy presents a selective advantage, we may see An. hinesorum emerge as a new malaria vector in a region that is now working on malaria elimination. This meRitchie, . Both AnAnopheles hinesorum—previously An. farauti 2, belonging to the Anopheles farauti complex. Although phylogeographic, behavioural and ecological studies have been performed on many members of this species complex  being found in coastal and inland habitats up to of over 1000 m above sea level . These HLCs were performed near (within 50 m of) productive larval sites, no one has yet been able to collect blood fed adults from these populations, and their hosts remain unknown. A previous study also showed that exclusive zoophagy in An. hinesorum is a derived trait, finding two distinct zoophagic mitochondrial (mtDNA) lineages  of a new mitochondrial lineage. Finally, it is possible that the initial population on the islands  evolved or already exhibited zoophagy and contained multiple mitochondrial lineages which subsequently became dominant in the north and the south of the Archipelago.Ambrose et al.  hypothesAn. hinesorum in the Solomon Archipelago are exclusively zoophagic, including populations from Bougainville and Guadalcanal  on humans  published in Ambrose et al. (n = 61). We develop 14 novel microsatellite primers for the species and generate microsatellite data from throughout the species range (n = 456). We include mitochondrial and nuclear microsatellite data from samples collected in human landing catches by Burkot et al.  for 60 individuals in this study. We aligned this with previously published homologous sequence data , multivariate analyses, estimation of fixation indices, a neighbour‐joining tree based on pairwise G′ST and AMOVA. Initially, we assessed population structure using the Bayesian clustering program STRUCTURE v. 2.3.4 . Sites where mosquitoes were sampled were used to define location priors for populations from Australia and New Guinea. In the Solomon Archipelago, we used the islands that individuals were sampled from as location priors. We ran STRUCTURE output through the CLUMPAK server  and K = 10 (predicted by the ‘Estimated Ln Prob of Data’). Additional STRUCTURE plots for all K values run can be found in Data We performed a variety of analyses to assess population structure of adegenet package , discriminant analysis of principal components  to retain for each analysis. For the full data set, we retained 80 PCs and ten discriminant axes (DAs), and for the Solomon Archipelago data alone (n = 177), we retained 30 PCs and 4 DAs. We present two types of plots that were generated from the DAPC: a composition plot (a bar plot—similar to a STRUCTURE plot) and pairwise plots of the first two discriminant axes.We initially defined populations (as requested by DAPC) based on results by Ambrose et al.  as well GST, G′ST and Jost's D, between the populations identified by STRUCTURE and multivariate methods. We plotted results for one of these indices (G′ST) in tabular form, as well as building a neighbour‐joining tree based on pairwise G′ST using the R package ape  in the data compared with the null (random) expectation.We used the program GenAlEx v.6.5  and is shared with individuals from the Papuan peninsula. The other haplotype falling in this group was only sampled in one larva collected from Ghizo. Thirdly, apart from Western Province, all newly sampled Solomon Archipelago COI haplotypes belong to the northern island lineage.The mtDNA haplotype network Figure  expands 3.2K = 2, STRUCTURE separated individuals from the Solomon Islands from individuals sampled from Australia and New Guinea, which form a single group. At K = 10, STRUCTURE identified two groups in Australia (Northern Territory and Queensland), four in New Guinea  and four in the Solomon Archipelago . DAPC analysis revealed evidence of eleven distinct clusters, in agreement with those found by STRUCTURE but with additional genetic differentiation between Guadalcanal and Nggela , between populations within regions  and between regions  and significantly lower than expected variance within samples  compared with the null model  population is the most distantly related at the nuclear level based on consistently high pairwise fixation indices relative to the other populations Figure . Within K2 also clearly separates the Solomon Archipelago from populations from Australia and New Guinea  and Santa Isabel (Solomon Islands) appear as closest relatives in the DAPC  and at least once in An. hinesorum  in a widespread zoophagic population in the Solomon Archipelago. Despite the southern and northern Solomon Archipelago mtDNA lineages being highly divergent, all populations from the Archipelago form a closely related group at the nuclear DNA level. These close relationships suggest that recent nuclear gene flow has occurred between them or that they share a more recent common ancestor than their mtDNA genomes suggest. The northern mtDNA lineage dominates the islands extending from Bougainville in the north to Nggela in the south, although several individuals sampled from Santa Isabel possess a haplotype commonly found on Guadalcanal (southern lineage). This pattern suggests either past connectivity between these islands or long‐distance migration of females.Three distinct evolutionary scenarios may have generated the observed pattern of exclusive zoophagy in two divergent mtDNA lineages of compoplots show mixed assignment of individuals from different islands in the Solomon Archipelago. Comparatively low fixation indices also indicate that recent gene flow has occurred. This result makes sense given that most islands of the Solomon Archipelago were connected by land bridges and formed a larger island known as Greater Bukida, separated from Guadalcanal by only 2 km of ocean at times of lowest sea level  of samples from this population fell into three genetically distinct and geographically defined groups: one from New Guinea (Papuan Peninsula) and two from the Solomon Archipelago (northern and southern lineages). One Western Province haplotype sampled in eleven individuals is identical to a sequence sampled from an anthropophagic population in New Guinea—the only case of haplotype sharing observed between Solomon Archipelago and New Guinean populations. This raises the possibility that female An. hinesorum from New Guinea were able to arrive in the Western Province of the Solomon Islands, reproduce with local mosquitoes, and in the process transfer anthropophagy to this island population.Recent fieldwork in Solomon Islands Western Province identified An. hinesorum from New Guinea to the Western Province. During the World War II Pacific engagement, American airbases connected the Western Province to New Guinea via airstrips in Lae and Milne Bay (the eastern point of the Papuan peninsula—see Figure An. hinesorum were abundant at these New Guinean airbase sites (Beebe & Cooper, There are two obvious recent dispersal routes for Even though a commonly sampled mitochondrial haplotype in the Solomon Islands Western Province is identical to a haplotype sampled in a New Guinean population—the only case of haplotype sharing between New Guinean and Solomon Archipelago populations—the nuclear microsatellites suggest the genomes of the Western Province population appear to be mostly of native Solomon Archipelago origin. This is shown by the close relationships at microsatellite loci between individuals from Western Province and the rest of the Solomon Archipelago. In mosquitoes, olfaction is the primary sense that governs host preference Takken, , and it An. farauti (Beebe et al., An. hinesorum from the Solomon Archipelago has serious implications for the transmission of malaria, especially if this phenotype spreads through other populations in the Archipelago. Anopheles hinesorum is common and abundant through the Solomon Islands showing larval site plasticity, and existing both inland and at elevation (Beebe et al., An. hinesorum through the Solomon Islands.Today, the only common species known to transmit malaria in the Solomon Archipelago is the coastally restricted 5An. hinesorum in the Western Pacific, clarifying population subdivisions. This lays the groundwork necessary to use this species as a novel model system for studying human host preference in mosquitoes. Large mtDNA divergences likely do not indicate species boundaries, as nuclear gene flow is evident between some highly diverged lineages in the Solomon Archipelago. Although we cannot be certain that exclusive zoophagy in the Solomon Island populations was transmitted between these divergent lineages via gene flow, our results suggest that gene flow between islands of the Archipelago has occurred. Further work is necessary to disentangle the hypotheses regarding the origins of zoophagy in the Solomon Archipelago. We detected New Guinean mitotypes in a recently discovered anthropophagic population from the Solomon Islands indicating that human‐mediated transport of the species may have resulted in anthropophagy being introduced to the Archipelago. The emergence of this phenotype may have ramifications for the epidemiology and transmission of malaria on the Solomon Archipelago; specifically, it may result in increased malaria transmission in inland villages.In this study, we have achieved a more complete understanding of population genetic relationships of The authors declare no conflicts of interest.Fig S1‐S3Click here for additional data file.Table S1‐S4Click here for additional data file.Supplementary MaterialClick here for additional data file.


Remote and rural communities in low‐ and middle‐income countries (LMICs) are disproportionately affected by infectious animal diseases due to their close contact with livestock and limited access to animal health personnel). However, animal disease surveillance and diagnosis in LMICs is often challenging, and turnaround times between sample submission and diagnosis can take days to weeks. This diagnostic gap and subsequent disease under‐reporting can allow emerging and transboundary animal pathogens to spread, with potentially serious and far‐reaching consequences. Point‐of‐care tests (POCTs), which allow for rapid diagnosis of infectious diseases in non‐laboratory settings, have the potential to significantly disrupt traditional animal health surveillance paradigms in LMICs. This literature review sought to identify POCTs currently available for diagnosing infectious animal diseases and to determine facilitators and barriers to their use and uptake in LMICs. Results indicated that some veterinary POCTs have been used for field‐based animal disease diagnosis in LMICs with good results. However, many POCTs target a small number of key agricultural and zoonotic animal diseases, while few exist for other important animal diseases. POCT evaluation is rarely taken beyond the laboratory and into the field where they are predicted to have the greatest impact, and where conditions can greatly affect test performance. A lack of mandated test validation regulations for veterinary POCTs has allowed tests of varying quality to enter the market, presenting challenges for potential customers. The use of substandard, improperly validated or unsuitable POCTs in LMICs can greatly undermine their true potential and can have far‐reaching negative impacts on disease control. To successfully implement novel rapid diagnostic pathways for animal disease in LMICs, technical, regulatory, socio‐political and economic challenges must be overcome, and further research is urgently needed before the potential of animal disease POCTs can be fully realized. Infectious agricultural and zoonotic diseases do not respect wealth or borders, and outbreaks of novel, re‐emerging and transboundary animal diseases can have devastating impacts on individual, community, regional and even global levels—as has become all too apparent with the ongoing African swine fever (ASF) epidemic, and the global COVID‐19 pandemic that is currently upending the lives and livelihoods of billions of people worldwide.Livestock and poultry provide food, agricultural labour, clothing, fertilizers and social status and act as wealth assets to more than 600 million livestock‐dependent people around the world  of infected animals can avert future medical costs associated with advanced disease in both animals and people, thereby benefiting health and livelihoods of subsistence farmers, communities and resource‐limited government services. From a global perspective, such early diagnosis and management decisions can prevent onward transmission of infectious pathogens, protecting the health and wellbeing of human and animal populations worldwide, and potentially containing epidemics that would otherwise have serious and far‐reaching consequences.Few infectious diseases have pathognomonic clinical signs or syndromes, which makes presumptive diagnosis, treatment or management based on clinical appearance alone inadvisable. One study evaluated the diagnostic accuracy of Bangladeshi veterinarians identifying foot and mouth disease (FMD) and peste des petits ruminants (PPR) in cattle and goats based on clinical presentation and reported overall sensitivity and specificity of 54% and 81%, respectively, compared to polymerase chain reaction (PCR) testing  are defined as, “a fully or partially automated table‐top, portable or disposable device able to be operated in a non‐laboratory environment by non‐technical staff to deliver a same‐day, on‐site, clinically relevant, diagnostic test result”  in LMICs, published in English, in any year through and including January 2020. We selected studies that were relevant under the following categories: 1) usage, including reviews, trials and comparative studies of diagnostic infectious animal disease POCTs in LMICs; 2) considerations for aspects of the “ideal” POCT, with particular emphasis on applications in LMICs; 3) barriers to usage and uptake of infectious animal disease POCTs in LMICs.Exclusion criteria: Any publications involving non‐diagnostic POCTs, or POCTs for diagnosis of non‐infectious diseases in animals or of human‐only diseases; publications relating to non‐POCT animal diagnostic methods; any media in any languages other than English. Foreign language material was excluded because of the cost and time required for translation.Where specific examples of publications regarding usage, implementation or impact of veterinary POCTs in LMICs were missing from the literature, we searched for relevant examples from the medical literature in order to provide a comparison for discussion. We also searched organizational websites including the World Organisation for Animal Health (OIE) and Food and Agriculture Organization of the United Nations (FAO) for information relating to POCTs for diagnosis of animal diseases. We also identified manufacturers of POCTs from key publications and documents and searched the internet for additional POCT manufacturers to identify those currently producing commercial diagnostic veterinary POCTs, and to obtain validation data and test kit inserts for infectious animal disease POCTs where available. The final bibliography included 567 documents  and 19 commercial POCT kit inserts from 11 manufacturers.33.1Anaplasma marginale  and other desired attributes including characteristics of safety and efficacy OIE, . An ASF Once these characteristics have been established to ensure fitness for purpose, other considerations should also be addressed. The “ASSURED” criteria set out by the World Health Organisation (WHO) state the ideal characteristics for a field‐ready diagnostic test as Affordable, Sensitive , Specific , User‐friendly , Robust (no cold storage needed) and rapid (results available in under 30 min), Equipment‐free and Deliverable to those who need it  test costs $2–10 million USD and takes 5–10 years . The assessment process takes an estimated 135 days and costs 4,500 Euros. While generally accepted as “best practice” for veterinary diagnostics, as of August 2020 only 14 kits are registered for a total of 11 pathogens, and only two are POCTs. Because this process is not mandatory, and because the register is not widely used, manufacturers—particularly smaller, underfunded POCT manufacturers in LMICs—may see little value in registering. This lack of a comprehensive, widely used reference point for approved diagnostic animal disease POCTs makes it difficult for LMICs and other users to differentiate reliable POCTs from cheaper, unvalidated tests that are entering the market at present. Without reliable evidence of a test’s diagnostic accuracy or fitness for purpose, many LMICs may simply be selecting POCTs based on purchase price alone.The WHO publishes R&D Blueprints for priority human diseases in which ideal product specifications for POCTs are described WHO, . They al3.3.3The OIE Terrestrial Manual has a section outlining the recommended validation pathway for novel diagnostic tests for infectious animal diseases OIE, , includiWithout regulations enforcing test validation, animal disease diagnostics—including POCTs—of varying quality and effectiveness can be sold and used, often in the developing world, with limited, misleading or incorrect data about their diagnostic validity in the target population , and ruggedness against external conditions such as climatic conditions and levels of proficiency (reproducibility). To assess and monitor reliability, it is important to include internal quality controls to confirm the basic functioning of the device; for example a weak positive control ; a negative control ; and for nucleic acid detection assays, an internal control to identify the presence of matrix inhibitors.With some notable exceptions, including some described earlier  can cause incorrect and unspecific reactions that could affect treatment and management decisions and may undermine user confidence in POCTs. Several LFAs for detecting rabies in brain material showed better diagnostic performance on samples from South Africa than from Eurasia and northern America  of infected animals can avert future medical costs associated with advanced disease in both animals and people, thereby protecting the livelihoods and wellbeing of subsistence farmers and their communities and reducing burdens on resource‐limited medical and veterinary services. Increased empowerment of farmers with regard to animal treatment and management decisions may particularly benefit women and children, who are the primary carers of pigs, poultry and small ruminants in many developing countries (Donadeu et al., Target users of POCT in LMICs are typically not laboratory personnel and are likely to have limited capacity building opportunities necessary for using, interpreting and applying results of diagnostic tests Nichols, . Furthermalaria treatment was reportedly often prescribed despite a negative result (Johansson et al., …respondents also believed the tests could identify any cause of illness, beyond malaria (Ansah et al., Other social considerations include community perception and acceptability of POCTs, which can influence POCT uptake in different settings. Communities may have traditional or cultural beliefs that make them averse to particular sampling methods, such as needle use in pigs (Hobbs et al., 4a traumatic and devastating experience for all those who were affected by it…a national crisis…probably one of the greatest social upheavals since the war (Mort et al., Economic impacts of infectious animal and zoonotic diseases can be severe and far‐reaching, as has become all too apparent during the global COVID‐19 pandemic—a zoonotic virus that is widely accepted to have a wildlife origin (Zhou et al., POCTs have the potential to significantly disrupt traditional laboratory‐based diagnostic pathways, especially in remote decentralized settings where sample referral networks and adequately equipped laboratories are particularly lacking. Diagnostic medical and veterinary POCTs are being used in LMICs and show clear benefits for disease diagnosis and surveillance, particularly when supported by policymakers (Mabey et al., Inadequate regulatory guidance and poor industry oversight has led to a proliferation of POCTs of varying quality and fitness for purpose released onto the market, presenting challenges to potential end users who are, by design, expected to have limited diagnostic experience. Accurate, independent test validation data for commercial POCTs are often incomplete or absent. Even after robust initial validation testing, POCTs that demonstrate excellent diagnostic performance in the laboratory can show markedly lower accuracy under field conditions, for a variety of reasons. Similarly, incorporation of POCTs in diagnostic pathways and disease testing algorithms that are successful in one setting may not have adequate uptake in others due to the varying and complex interplays between political, sociocultural and geographic factors, among others. Government veterinary services in LMICs should be aware of the costs, impacts, cost‐effectiveness and operational feasibility of incorporating POCTs into diagnostic workflows in their specific country context, and invest their resources accordingly. However, the lack of clear POCT‐specific validation guidelines promoted by an independent organization such as the OIE, the absence of a mandated or widely used centralized register of approved POCTs, and the dearth of independently validated field studies in the literature at present means that many LMICs may simply be selecting POCTs based on purchase price alone—a practice which may be creating more problems than solutions.Urgent action is needed. Public and philanthropic funding agencies need encouragement to engage with stakeholders for development of TPPs, and to invest in the subsequent development and independent validation of targeted animal disease POCTs for use in field settings, especially for diseases that are only present in developing countries and consequently for which commercial manufacturing interest is low. The international community should be urged to adopt regulatory frameworks for the manufacture and commercialization of diagnostic POCTs, and to mandate transparent publication of comprehensive, independent POCT validation data about test accuracy and fitness for purpose. Standardized technical guidance and training should be made available for POCT users and policymakers in the developing world, with advice for evaluating POCT characteristics based on manufacturer information and available literature, conducting in‐house field validation and verification of diagnostic POCTS, and correctly interpreting and applying POCT results according to the relevant clinical setting.Without government and community support, even the most accurate and cost‐effective diagnostics will fail to have any substantial or sustained impact in LMICs. Sociological research and user needs analyses are required to understand the drivers of animal disease transmission in LMICs, to investigate local knowledge, attitudes and practices relating to animal disease diagnosis and surveillance, to identify barriers and facilitators to POCTs use in regional contexts and to develop strategies for implementing POCTs in the most impactful way to benefit animals, people and communities. Even with reliable access to properly validated, fit for purpose POCTs, for diseases of high importance there will still be a need for confirmatory laboratory diagnosis and pathogen typing in LMICs for the foreseeable future. Therefore, support should also be given to initiatives that strengthen national veterinary and laboratory capacity, such as the Performance of Veterinary Services and laboratory twinning programs supported by the OIE and other international partners. Advances in and increasing accessibility to technology should also be utilized, for example using drones to assist with sample transport in remote locations (Mitchell, Infectious animal and zoonotic diseases have been increasing in incidence in recent times, particularly in LMICs with high human and animal density (Bordier & Roger, The authors have no conflicts of interests to declare.The authors confirm that the ethical policies of the journal, as noted on the journal’s author guidelines page, have been adhered to. No ethical approval was required as this is a review article with no original research data.


The article Review of Effort‑Reward Imbalance at Work and Overcommitment in Patients with Acute Myocardial Infarction (AMI): Associations with Return to Work 6 Months After AMI, written by Inge Kirchberger, was originally published electronically on the publisher’s internet portal on 12 November 2020 without open access. With the author(s)’ decision to opt for Open Choice the copyright of the article changed on 9 July 2021 to © The Author(s) 2021 and the article is forthwith distributed under a Creative Commons Attribution.


We highlight the need for planning for mass workforce absentees as we prepare for subsequent surges. We suggest a multicomponent intervention including guiding return dates more by symptomatology and fitness for work rather than infectivity status. We highlight the need for planning for mass workforce absentees as we prepare for subsequent surges. We suggest a multicomponent intervention including guiding return dates more by symptomatology and fitness for work rather than infectivity status. It notes their demographic and clinical characteristics and suggests ways to support them returning to work in a graded and supported manner.Coronaviruses are known pathogenic microorganisms in both humans and animals.Four of the seven known coronaviruses that cause disease in humans only cause mild‐to‐moderate flu‐like illnesses.An early study of hospitalized patients from a single institution in Wuhan showed that on admission, most patients had fever or cough, and a third of patients had shortness of breath. Other symptoms included muscle ache, headache, confusion, chest pain, and diarrhea. Several patients proceeded to get organ function damage, including 17 (17%) with ARDS, eight (8%) with acute respiratory injury, three (3%) with acute renal injury, four (4%) with septic shock, and one (1%) with ventilator‐associated pneumonia.Our knowledge of COVID‐19 presentations is still evolving; there have been many case reports of atypical presentations of COVID‐19 infection including one case report suggesting altered mental status as a presenting complaint for an elderly patient.Other coronaviruses, SARS‐CoV and MERS‐CoV, have been associated with rapid spread in healthcare settings often resulting in large nosocomial outbreaks. Hospital overcrowding, workplace absenteeism, nonadherence to infection prevention and control measures, and possible environmental contamination are all thought to be implicated in such amplification in the case of MERS‐CoV outbreaks.Our understanding of COVID‐19 infection among health workers and those at risk of adverse outcomes and prolonged courses is important for our understanding of virus transmission patterns and for preventing the future infection of health workers and other patients. A better understanding of the workforce absentees and difficulties with returning to work will allow us to plan for appropriate staffing and support for our healthcare workers. It is also crucial for informing and updating infection prevention and control measures at healthcare facility and national level, and for reducing secondary COVID‐19 transmission within healthcare settings. Ireland faces one of the highest rates of diagnosed healthcare worker infection in Europe. The European Centre for Disease Prevention and Control found on 8 April that across Europe, “between 9% and 26% of all diagnosed COVID‐19 cases are in healthcare workers.” In Ireland, 8018 healthcare workers were diagnosed with COVID‐19 of which 4823 remain sick. 1600 of these were nurses and midwives.In this case series, we identified a cohort of health workers with a laboratory‐confirmed COVID‐19 infection who experienced a prolonged symptomatic course of COVID‐19 infection beyond 21 days.1.1To describe and identify the clinical characteristics of healthcare workers who experienced a prolonged symptomatic phase of COVID‐19 infection and required a more substantial recovery phase greater than fourteen days which was deemed the end of infectivity.2The hospital is a 781‐bedded tertiary care center in inner Dublin, Ireland, with over 5000 staff. Healthcare workers with suspected COVID‐19 infection on the basis of initial symptoms were referred for swabbing by the Occupational Health Department. If deemed positive, they were contacted by phone by the Infectious Diseases team to enable clinical care if they deteriorated and by Occupational Health, to discuss procedures for return‐to‐work evaluation. A direct line to Occupational Health was given to healthcare workers for contact if concerned about their symptoms. Contact was also made by Occupational Health to healthcare workers as they approached their return‐to‐work date to assess if suitable to return to workplace duties. When contact led to concern by the Occupational Health physicians, patients were referred for clinical review. Depending on the urgency of their clinical state as evaluated by telephone, this was either to the emergency department for immediate assessment or referral to a dedicated clinic setup for COVID‐positive healthcare workers. Our patients were recruited from the COVID‐positive healthcare worker clinic. Informed consent was obtained from all patients, and this study was approved by St. James and Tallaght Hospital Joint Research Ethics Committee.A confirmed case of COVID‐19 was defined by a positive result on a reverse transcriptase polymerase chain reaction (RT‐PCR) assay of a specimen collected on a nasopharyngeal swab. Only laboratory‐confirmed cases were included. The assay used to confirm COVID‐19 infection is based on the World Health Organization standard and was confirmed by RT‐PCR in the routine laboratory using either MutaPLEX (Immundiagnostik AG) or RealTime SARS‐CoV‐2 (Abbott).From a total of 456 healthcare workers with laboratory‐confirmed COVID‐19 infection in our institution, a total of 203 (44.5%) remained off work for greater than 21 days. Of these, we reviewed 19 healthcare workers in our clinic who were experiencing a prolonged symptomatic phase > 21 days from initial symptom onset and whose symptoms were deemed significant enough to require medical review and thus referred to our COVID‐19 healthcare worker clinic. We excluded healthcare workers who had been admitted with their symptoms. The longest period of symptoms since onset in our cohort was 57 days, and the shortest period was 27 days. The demographic and clinical characteristics of the patients are shown below. The majority of patients were female at 80% of the cohort (Table The mean (±SD) number of days since positive swab until review in clinic was 36.47 12.043) with a median of 32  were lymphopenic at the time of review Table . Most pa4This study describes 19 healthcare workers treated as outpatients with confirmed COVID‐19 infection who experienced a prolonged symptomatic phase and remained unfit to return to work greater than twenty‐one days from confirmation of diagnosis. This cohort represents a 4.17% subgroup of healthcare workers at our institution who were infected with COVID‐19. An evolution in symptomatology was observed from initial presentation with fever and headaches to respiratory symptoms and fatigue in the more chronic stage.The number of infected and exposed healthcare workers who remain unfit for workplaces an immense strain on an already pressurized healthcare system. As is the case in this cohort, many infected healthcare workers cannot return to work for prolonged periods due to persistence of COVID‐19 infection symptoms. Given that returning to work for a large proportion of healthcare workers involves long shift work hours often involving 12‐ to 13‐hour shifts, we must recognize and identify these patients who are experiencing a prolonged course and support their return to work in a graded fashion once they have recovered from their illnesses.When we look to other viral illnesses and their effect on the workforce, we can recognize the significance such prolonged courses may have. A study in the Journal of Occupational Health published in 1998 reviewed the impact of influenza and influenza‐like illness on healthcare resource utilization. Patients were incapacitated or confined to bed for 2.4 days, leading to workplace absenteeism of 2.8 days per episode of illness.If we look to previous experience with coronaviruses, longer‐term follow‐up studies have shown that patients with severe acute respiratory syndrome (SARS‐CoV‐1) may experience long‐term symptoms, such as restrictive pulmonary dysfunction, palpitations, hand tremors, and exertional dyspnea, all of which have an effect on their ability to return to their premorbid life and daily activities.Furthermore, data are beginning to emerge on the longer‐term lung damage that can be caused by COVID‐19 infection. A study by Wang et al looked at 70 patients who had survived COVID‐19 pneumonia in Wuhan China and found that 94% of discharged patients had evidence of residual disease on final CT scans, with ground‐glass opacity being the most common pattern of disease.Along with the risks of direct infection which arises from working in close proximity with COVID‐19–confirmed cases and with potentially infectious colleagues, healthcare workers are also under increasing stress and mental health risks. Observations were made by assessors of emotional lability of patients during assessment; however, due to inconsistent documentation of psychological state we were unable to include this in the analysis. It was noted that patients were often tearful during consultation and reported feelings of mental and social isolation. Comparatively, patients with SARS and MERS, in the recovery phase of the illness, experienced sleep disorder, frequent recall of traumatic memories, emotional lability, impaired concentration, fatigue, and impaired memory in more than 15% at a follow‐up period between 6 weeks and 39 months.This study has several limitations. The study population only included those healthcare workers who presented as they failed to meet their return‐to‐work date or whose ID was concerned enough about to refer for medical review. Many healthcare workers may have returned to work and yet may still continue to feel symptomatic. Another limitation is that the data were collected at a single time point from the electronic health record database; therefore, the level of detail was limited to documentation on initial consultation.Further research is needed to inform our recognition of healthcare workers who are experiencing prolonged symptomatic courses of COVID‐19 infection and to support these patients in achieving a full recovery and a return to the workforce. Consideration should be made for formalized long‐term follow‐up to ensure a return to full health or further medical support as required. Return‐to‐work dates should be guided more by patient symptomatology and fitness for work rather than ineffectively status. Healthcare workers should be given the opportunity to return to work in a more gradual manner, and a formal follow‐up should be arranged to ensure they are re‐adjusting back to both the physical and mental challenges of returning to duties in the COVID‐19 pandemic. Further research is needed to assess the role of rehabilitation programs for healthcare workers to assist them with returning to their normal duties and assessment of their workplace effectiveness on return to work. Our findings also highlight the need for planning for mass workforce absentees as we deal with the consequences of the COVID‐19 pandemic and prepare for subsequent surges.None declared.Catherine Murphy and Professor Hennessy: conceived the study. Noorin Noonan, Eilis O’Toole, Professor Plunkett, Mary Paula Colgan, Catrionia Canning, and Zenia Martin: initiated the study design and helped with implementation. Catherine Murphy: did the statistical analysis and prepared the manuscript. All authors: contributed to the refinement of the study protocol and approved the final manuscript.This study was ethically approved by the Joint James Tallaght Ethics Committee. JREC reference: 2020‐05 list 19.


Single‐chain nanoparticles (SCNPs) are highly versatile structures resembling proteins, able to function as catalysts or biomedical delivery systems. Based on their synthesis by single‐chain collapse into nanoparticular systems, their internal structure is complex, resulting in nanosized domains preformed during the crosslinking process. In this study we present proof of such nanocompartments within SCNPs via a combination of electron paramagnetic resonance (EPR) and fluorescence spectroscopy. A novel strategy to encapsulate labels within these water dispersible SCNPs with hydrodynamic radii of ≈5 nm is presented, based on amphiphilic polymers with additional covalently bound labels, attached via the copper catalyzed azide/alkyne “click” reaction (CuAAC). A detailed profile of the interior of the SCNPs and the labels’ microenvironment was obtained via electron paramagnetic resonance (EPR) experiments, followed by an assessment of their photophysical properties. The internal structure of single‐chain nanoparticles (SCNPs) has been investigated, forming nanosized domains preformed during the crosslinking process. We present proof for the presence of nano‐compartments within SCNPs via a combination of electron paramagnetic resonance (EPR) and fluorescence spectroscopy. A novel strategy to encapsulate labels within such water dispersible SCNPs is presented. Among many nanosized carriers single chain nanoparticles (SCNPs) are the most versatile, both in molecular design, structural diversity and embedded functionalities.Although the formation of nanosized compartments within the SCNPs has been often proposed and observed indirectly, a direct proof has not been accomplished as many of the used methodologies provide an only indirect measure of compartmentation. Thus monitoring the molecular dynamics of water molecules around SCNPs via Overhauser dynamic nuclear polarization (ODNP) hinted to supramolecular organized hydrophobic benzene‐1,3,5‐tricarboxamides (BTA) into chiral folds within the SCNP, resembling their self‐assembly in solution.rh<10 nm) SCNPs to allow sufficient permeation for cell‐incorporation.We here probe the formation of compartments within synthetic SCNPs based on amphiphilic polymers, equipped with an active spin label, via continuous wave electron paramagnetic wave resonance (CW EPR), allowing to probe the internal compartments of the formed SCNPs. Additionally, fluorescent dyes, appropriate for use in pump‐probe photoacoustic imagingWith the here reported single‐chain nanoparticles one can therefore generate a scaffold for encoding the required different functionalities stimuli‐response, for example, thermo‐ or pH‐responsivity,3c, 17polymer I) was synthesized by RAFT copolymerization of poly(ethylene glycol) methacrylate , azidopropyl methacrylate, and 3‐(trimethylsilyl)propargyl methacrylate in DMF, using AIBN as initiator and cyanoisopropyl dithiobenzoate (CPDB) as chain transfer agent (CTA), followed by the removal of the CTA using an excess of AIBN.polymer I: nM=36.1 kDa, PDI=1.7, DP=129). Furthermore, this composition allows to induce thermoresponsivity together with sufficient dispersibility and hydrophobic packaging of the labels. Final composition and structural integrity of the resulting polymer was proven by NMR‐spectroscopy, allowing to adjust the desired functionalities along the polymer chain in relation to the initially used amounts of the three monomers . Subsequently, single‐chain collapse of polymer I and labelling (see Scheme 4/NaAsc solution via a syringe pump to adjust low concentrations (cpolymer<10−6 M) for the required single‐chain folding. The amphiphilic polymer thus is adopting a preorganized nanoparticle by forming an intramolecular core–shell structure with the hydrophilic PEG sidechains in the shell and the reactive azide‐groups in the core, resulting in sole intrachain crosslinking of the reactive groups to form SCNP II via CuAAC‐reaction ‐label and aza‐BODIPY (aBOD)). This allows a reaction of the still present residual terminal azido moieties in the hydrophobic core to attach the two fluorescent dyes Rhodamine B (RhoB) and aBOD as a NIR‐fluorescent dye. Especially aBOD is attractive as it displays a tumor targeting functionality via deprotonation, resulting in intramolecular charge transfer, and thereby suppression of fluorescence.[The required poly(poly(ethylene glycol) methacrylate) (PEGMA)‐copolymer . A crosslinking density of ≈9 crosslinks per SCNP was roughly calculated from the stoichiometric ratios, leaving ≈4 remaining attachment sites per SCNP for the labels. The so obtained SCNPs are water dispersible and can be redispersed after freeze‐drying.Progress of the crosslinking was followed by attenuated total reflection infrared (ATR‐IR) spectroscopy  indicating a full disappearance of the alkyne band at 2178 cmpolymer I and SCNP II  and dynamic light scattering measurements of SCNPs IIIb and IIIc . During single‐chain collapse, the hydrophobic reactive groups  are proposed to be located in the inner core of the nanoparticles, hidden from the aqueous surroundings. We therefore observe a polarity shift in SEC, inline with an increased hydrophobic collapse (Tcp) after the single‐chain collapse and with further modification moving from the native polymer I (Tcp=48 °C) to SCNP II (Tcp=54 °C) to SCNP IIIa‐c   measurements in water further prove the covalent attachment of the labels to the SCNPs via an overlap of the R.I. and UV/Vis track for the dye‐labelled ollapse Tcp after SCNP IIIa with an equally labelled linear polymer, the TEMPO‐labelled polymer I′a was synthesized analogously to polymer I, but without the alkyne moieties (see Supporting Information).To achieve a deeper understanding of the microstructure and the distribution and location of the individual polymer and label segments within the SCNPs, CW EPR measurements were conducted. For the protective effect in SCNPs to be effective, the exact location  of the labels within the SNCPs and the desired location within the hydrophobic core is crucial. Moreover, a rough quantification of the number of labels in one SCNP is needed. With the CW EPR spectra of the attached TEMPO label it is possible to specify the chemical environment of the nanostructure surrounding the probe within a radius of ≈1 nm.polymer I′ yielding polymer I′a, a significant broadening of the EPR peaks is observed, caused by the hindered rotation of the label attached to the large polymer. This effect is even stronger in the TEMPO‐labelled SCNP IIIa, indicating a reduction of the mobility of the label upon attachment to the SCNP. Figure polymer I and SCNP II in water in pure physical mixtures with the free TEMPO probe. The three identical normalized spectra indicate free TEMPO in water, proving the absence of specific, mostly hydrophobic interactions of the amphiphilic TEMPO probe with the polymer and the SCNP, respectively, as we usually and regularly observe it with amphiphilic (LCST‐type) polymers.SCNP IIIa, but not for the respective polymers an exchange coupling of 6 MHz had to be included to the calculation, indicating high local concentrations and direct contact of two or more TEMPO labels with distances <1 nm. Even though a certain amount of TEMPO was probably deactivated by reduction during the labelling process because of sodium ascorbate present in the reaction mixture, the degree of labelling was approximated to be 1.3 active labels per particle . This, together with our spectra, leads to the conclusion that the SCNPs bear one to two chemically attached active spin labels and even more EPR‐inactive labels.Upon covalently binding TEMPO as a label Figure  to polymSCNP IIIa in Figure SCNP IIIa in a confined space surrounded by the non‐polar polymer backbone, which is reflected in the lower hyperfine splitting of labels on SCNPs, in the nanoparticles core, altogether as a strong proof for the formation of such a compartment. External influences do not affect the label, indicating its embedding in the SCNP's core. Considering that TEMPO is more hydrophilic than the hydrophobic dyes embedded for optical applications, the SCNPs are therefore able to protect a photoactive label from photooxidation or other degradational processes.Concentration dependent measurements of SCNP IIIb shows a red shifted absorption at 559 nm and a blue shifted fluorescence at 582 nm (stokes shift reduced to 23 nm). In the aBOD‐labelled SCNP IIIc, resonances are blue shifted by 6 nm in comparison to the free aBOD dye in aqueous phosphate buffer, with a stokes shift of 27 nm for the free aBOD dye and SCNP IIIc, respectively. In line with the results from EPR measurements, those shifts occur by a different solvation of the dyes in the particle's hydrophobic core, well separated from the surrounding bulk water phase, again indicative for the compartmented nanostructure of the SCNP.The optical properties of dyes can be constructively tuned by binding to SCNPs.12, 29polymer I′c see Supporting Information) to compare it to SCNP IIIc in view of their excitational behavior and heterogeneity.The near neighborhood of two or more bound dye molecules leads to interactions and, dependent on their distance and orientation, possible excitonic coupling between the dyes which changes the fluorescence quantum yield, the wavelength maximum and FWHM of the fluorescence emission.polymer I′c one can see a strong heterogeneity in the decay associated spectra  seems to be emitted from the energetically lower level while the 2.9±0.2 ns component (blue curve) results from the energetically higher level. The fastest component slows down further as compared to polymer I′c and was measured 300±50 ps with strong spectral asymmetry and negative value at 730 nm which is typical for an excitonic relaxation from strongly blue shifted states with an emission around 700 nm to the observed fluorescence maximum around 720–730 nm.For S Figure . The decThe possible band structure in the material is described schematically in Figure SCNP IIIc. Specific configuration for the bound molecules by specific molecular design of the binding sites in the SCNP would allow for the creation of highly individual optical properties of the coupled aBOD dyes and allow for the fine tuning of color and fluorescence yield lifetime of the bound molecules.The spectroscopic properties are strongly determined by the exact localization of the states at the binding sites of the aBOD molecules in pKa and thereby its pH‐responding functionality. As depicted in Table pKa of 6.8 for the free aBOD and 7.6 for SCNP IIIc. Changing the pH of the SCNP IIIc dispersion from 6.0 to 8.6 strongly decreases its fluorescence, by 92 %. The pH‐responsivity has no strong influence on the fluorescence lifetime . The lifetimes in both, the free aBOD dye and SCNP IIIc, stay nearly unchanged over the whole pH range, prospective for a proper tumor tissue targeting. These data are in compliance with the overall fluorescence yield shown in Figure S7 with stronger amplitude reduction observed for SCNP IIIc  as compared to aBOD in solution .The location of the aBOD dye in the SCNP's core also effects its Tcp higher than that of the free chain of the native polymer I, indicative for the formation of core–shell structured nanoparticles with the hydrophilic PEG chains in the shell and the hydrophobic groups with the embedded labels located inside the core. The inherent dynamics of the SCNPs allows for a medium‐responsive nanostructure as indicated by their thermoresponsivities, and the formation of contacts between two or more labels in one SCNP. Definite proof of this compartmented model consisting of a core–shell structure was accomplished via EPR measurements, proving the presence of at least two covalently attached labels within a confined space with distances below 1 nm, surrounded by the non‐polar polymer backbone and shielded from outer influences by the particle's shell. Based on the quite unique combination of thermal, EPR and photophysical measurements we could prove the nanocompartmented structure of such SCNPs, consisting of the hydrophobic labels embedded within the corresponding hydrophobic compartments, with the short PEG‐chains generating the outer shell. Coupling between dye molecules and the local environment strongly influences their optical properties giving rise to a strategy that allows for specific molecular design of the optical properties. Due to the so enabled shielding of the fluorescent dyes inside the SCNPs, the fluorescent SCNPs IIIb and IIIc prospect their application as contrasting agents in photoacoustic imaging, still maintaining their targeting properties uninfluenced by their encapsulation in the SCNPs.In summary we could observe the formation of internal compartments within single chain nanoparticles (SCNP's) via EPR‐ and fluorescence spectroscopic methods. The SCNPs are synthesized by using a clickable PEGMA based copolymer synthesized by RAFT polymerization, followed by an intramolecular CuAAC within its hydrophobic core. DLS and DOSY‐NMR measurements proved single‐chain collapse with hydrodynamic radii of 4–5 nm. The water dispersible SCNPs all displayed a The authors declare no conflict of interest.As a service to our authors and readers, this journal provides supporting information supplied by the authors. Such materials are peer reviewed and may be re‐organized for online delivery, but are not copy‐edited or typeset. Technical support issues arising from supporting information (other than missing files) should be addressed to the authors.SupplementaryClick here for additional data file.


Understanding the implications of climate change for migratory animals is paramount for establishing how best to conserve them. A large body of evidence suggests that birds are migrating earlier in response to rising temperatures, but many studies focus on single populations of model species.Migratory patterns at large spatial scales may differ from those occurring in single populations, for example because of individuals dispersing outside of study areas. Furthermore, understanding phenological trends across species is vital because we need a holistic understanding of how climate change affects wildlife, especially as rates of temperature change vary globally.The life cycles of migratory wading birds cover vast latitudinal gradients, making them particularly susceptible to climate change and, therefore, ideal model organisms for understanding its effects. Here, we implement a novel application of changepoint detection analysis to investigate changes in the timing of migration in waders at a flyway scale using a thirteen‐year citizen science dataset (eBird) and determine the influence of changes in weather conditions on large‐scale migratory patterns.In contrast to most previous research, our results suggest that migration is getting later in both spring and autumn. We show that rates of change were faster in spring than autumn in both the Afro‐Palearctic and Nearctic flyways, but that weather conditions in autumn, not in spring, predicted temporal changes in the corresponding season. Birds migrated earlier in autumn when temperatures increased rapidly, and later with increasing headwinds.One possible explanation for our results is that migration is becoming later due to northward range shifts, which means that a higher proportion of birds travel greater distances and therefore take longer to reach their destinations. Our findings underline the importance of considering spatial scale when investigating changes in the phenology of migratory bird species. Millions of birds migrate annually providing valuable ecosystem services, but we know little about macroecological changes occurring due to climate change. Our results suggest that continental‐scale wading bird migration has become later over the last decade, contrary to many studies at the population‐level, suggesting that changes in the timing of bird migration might differ across spatial scales. More work is urgently needed to investigate large‐scale patterns in the phenology of migratory animals.  Many wa22.1Sightings of all wader species classified by Birdlife as being migratory were downloaded from the eBird citizen science database  the number of times a species was seen at each latitude  and (b) the total number of sightings of any wading bird species reported at any latitude. Therefore, for each day we had the number of sightings of a species in each latitudinal band and the total numbers of sightings of all species across all latitudinal bands. We then used the number of times a species was reported at each latitude as a proportion of the total number of sightings of any species seen across all latitudes to create a daily, weighted mean latitude for each species. For example, a species for which 100 sightings were reported from 35°N on a day in which 1,500 sightings of waders of all species were reported across all latitudes, was given a weighting of 100/1,500 for that latitude on that day. This proportion provided an index of the effort made to observe a species at a given latitude, relative to the total effort made to observe waders across all latitudes. Additionally, we removed any days on which the total number of sightings of all species within a flyway was less than five. This avoided biasing the data due to a relatively small number of observers being out on any given day , and considered each year individually. We then used changepoint detection analysis to detect these shifts.ytt=1,…,n2 represents our daily mean latitudinal observations of a species over a one‐year period, where n is the number of observations for that year and t is the day of the year. Then, a changepoint in these data, “τ,” corresponds to a point in time such that the statistical properties of ytt=1,…,τ, and ytt=τ+1,…,n differ in some way. A data set could contain multiple changepoints, which divide the data into segments; each of these segments will have some different statistical property. For example, if a data set contained changes in its mean, then each segment would have a different mean. There might be only one statistical property that changes, or there could be multiple properties. Figure Suppose that We used the “changepoint” package . Wader species can migrate either diurnally or nocturnally Lank, , but day2.3.3For each weather variable, we fitted a linear least‐squares regression over the forty‐day window and used the slope of that line in our models. We chose the forty‐day period because we were investigating changes in migratory behavior across large temporal and spatial scales. Furthermore, most of the individuals of a species are likely to migrate within a window of approximately this length . Analyses were carried out in the R environment , flyway, the mean breeding and wintering latitude defined using the method described above, and the total number of bird observations reported on the migration day. The latter variable was included to account for the increasing number of observations made over time. The indices of breeding and wintering latitude were included to account for potential differences in the response of species to climate change across latitudinal gradients. The weather conditions included were temperature and northward and eastward wind trends. For each model, we included all two‐way interactions, except for those involving the number of observations. All continuous variables were centered and scaled prior to analyses to allow direct comparisons between fixed effects in the model outputs , with changes in spring migration in the Afro‐Palearctic flyway occurring the most rapidly . Breeding latitude was an important predictor in the models of both migrations. Northern breeders migrated later in spring and slightly earlier in autumn than those breeding at more southerly latitudes, consistent with shorter breeding seasons at northerly latitudes ; in autumn there was no difference in the change in the timing of migration between northern and southern breeders  and when eastward wind became stronger more quickly  than when these were getting colder and weaker, respectively .4We found that both spring and autumn migration in waders are likely to have become later over time in the Nearctic and Afro‐Palearctic flyways, which is in contrast with the results from many studies of population‐level phenology in migratory species  individuals migrating further distances due to the colonization of new habitats ; data curation ; formal analysis (lead); investigation (lead); methodology ; validation ; visualization (lead); writing‐original draft (lead); writing‐review & editing . Matt Amos: Data curation ; formal analysis (supporting); resources ; writing‐review & editing . Jamie‐Leigh Chapman: Formal analysis (supporting); investigation (supporting); methodology ; resources ; validation (supporting); writing‐review & editing . Andrew MacColl: Conceptualization ; funding acquisition ; investigation (supporting); project administration (supporting); supervision ; writing‐review & editing . Stuart P. Sharp: Conceptualization ; formal analysis ; funding acquisition ; investigation ; methodology ; project administration (lead); supervision (lead); validation ; writing‐original draft ; writing‐review & editing .Supplementary MaterialClick here for additional data file.


Investigation of a pertussis outbreak and comparison of two acellular booster pertussis vaccines in a junior school in South East England, 2019’ by Tessier et al., published on 25 March 2021, Repevax was mistakenly stated as having ten times more pertussis toxid than Infanrix. The text was corrected on 13 January 2022 on request of the authors. In the article ‘