Computational design of new active sites has generally proceeded by geometrically defining interactions between the reaction transition state(s) and surrounding side‐chain functional groups which maximize transition‐state stabilization, and then searching for sites in protein scaffolds where the specified side‐chain–transition‐state interactions can be realized. A limitation of this approach is that the interactions between the side chains themselves are not constrained. An extensive connected hydrogen bond network involving the catalytic residues was observed in a designed retroaldolase following directed evolution. Such connected networks could increase catalytic activity by preorganizing active site residues in catalytically competent orientations, and enabling concerted interactions between side chains during catalysis, for example, proton shuffling. We developed a method for designing active sites in which the catalytic side chains, in addition to making interactions with the transition state, are also involved in extensive hydrogen bond networks. Because of the added constraint of hydrogen‐bond connectivity between the catalytic side chains, to find solutions, a wider range of interactions between these side chains and the transition state must be considered. Our new method starts from a ChemDraw‐like two‐dimensional representation of the transition state with hydrogen‐bond donors, acceptors, and covalent interaction sites indicated, and all placements of side‐chain functional groups that make the indicated interactions with the transition state, and are fully connected in a single hydrogen‐bond network are systematically enumerated. The RosettaMatch method can then be used to identify realizations of these fully‐connected active sites in protein scaffolds. The method generates many fully‐connected active site solutions for a set of model reactions that are promising starting points for the design of fully‐preorganized enzyme catalysts. We reasoned that potential losses in activity from less optimal placement of individual functional groups relative to the transition state could be more than compensated by the advantages of catalytic network connectivity. The new method, HBNetGen, starts from a ChemDraw‐like specification of the hydrogen‐bonding and partial‐covalent interactions between side‐chain functional groups and the reaction transition state , and between the catalytic side chains themselves (much like textbook schematic depictions of enzyme active sites). The transition state is placed on a grid, and side‐chain functional groups are placed around the transition state according to the active‐site specification. The grid enables complete enumeration of compatible functional‐group placements at a resolution set by the (user‐specified) grid spacing. At a grid spacing of 0.2 Å, there are typically ~10To test HBNetGen, we selected seven examples of fully‐connected catalytic networks in the PDB and ran HBNetGen on these crystal structures and that of the evolved retroaldolase, starting from the backbone coordinates and a ChemDraw description of the connectivity of the desired active site. For direct comparison with the crystal structures, the inhibitor or reaction product was used in these tests rather than the computed reaction transition state. The results are summarized in Figure To identify placements of the connected networks in sets of protein scaffolds, we adapted the previously described RosettaMatch method to input the rigid‐body transformations for each residue in each network expressed in a hierarchical XML file: a RosettaMatch calculation using the side‐chain conformations from a single HBNetGen network will by construction only identify fully‐connected active sites. However, even with the efficiency of RosettaMatch, searching for placements of hundreds of thousands of designed, connected active sites in protein scaffolds, each with a unique set of side‐chain conformations, is not computationally feasible for any but the simplest active sites. To make this problem tractable, we experimented with pooling the side‐chain conformations from all HBNetGen networks, and then using RosettaMatch to find combinations of side chains making the specified interactions with the transition state. This solution has the advantage of ensuring that each rotamer at each position is only considered once during the calculation, which considerably increases computational efficiency, but has the consequence that side‐chain conformations from different networks can be mixed during matching. We speculated that side‐chain rotamer conformations in the same HBNetGen network would have a higher likelihood of being placed in the same active site by RosettaMatch because of their geometric complementarity, leading to recovery of many of the input networks. However, fully‐connected networks were recovered infrequently with this approach (see next paragraph). To modulate the extent of network remixing, we experimented with clustering the set of networks on the coordinates of the central functional groups; this allows smooth interpolation between the case where each network is treated independently (cluster size 1), and that where all networks are combined (one large cluster). A larger number of smaller clusters will produce fewer models with incomplete networks but will increase the runtime because a separate calculation must be performed for each cluster; we found that using ~250 clusters balanced performance and the run‐time increase.To evaluate the performance of HBNetGen‐guided match generation, we matched the set of networks inspired by the evolved retro‐aldolase (RA95.5‐8F) active site, with the additional criterion that each side chain must interact with the ligand directly , into a de novo designed catalytic sites. It allows exploration of different catalytic‐site specifications (at the ChemDraw level), completely independent of a particular protein backbone. This capability enables determination of the extent to which different sites can be realized in three dimensions with full hydrogen bonded connectivity, and investigation, again independent of any protein backbone, of whether the active site configurations found in nature were favored because of the transition state stabilization they provide or because of the connectivity of the catalytic side chains. It is likely that algorithms for finding matches to the HBNetGen connected sites in actual protein structures can be developed that are more efficient than the simple RosettaMatch implementation described here which breaks up the networks for computational tractability. Experimental characterization of HBNetGen fully‐connected active sites should provide insight into the contribution of preorganization and side‐chain connectivity to catalysis.HBNetGen explicitly generates fully‐connected active sites that are likely to be more preorganized than previous The authors declare no competing financial interest.AppendixS1 Supporting InformationClick here for additional data file.


To examine if congenital visual impairment is associated with differences in brain anatomy in children.SVI; >0.8 logMAR) or mild‐to‐moderate visual impairment  were compared to 21 typically sighted comparison (TSC) children. Thalamus volume, grey matter density, white matter microstructure, and integrity of visual tracts were investigated in SVI, MVI, and TSC groups with anatomical and diffusion‐weighted magnetic resonance imaging.Ten children (8–12y) with congenital disorders of the peripheral visual system with severe visual impairment  and lower left thalamus volume . Neuroanatomical differences were greater in the SVI group, while no consistent differences between the MVI and TSC group were observed.Compared to the SVI group.Posterior tracts of the visual system are compromised in children with congenital visual impairment versus those who are typically sighted. The severity of visual input appears to have affected neuroanatomical development as significant reductions were only found in the Severe visual impairment in mid‐childhood is associated with reduced integrity of visual pathways and reduced thalamus volume. Severe visual impairment in mid‐childhood is associated with reduced integrity of visual pathways and reduced thalamus volume.https://doi.org/10.1111/dmcn.14339 of this issue.This article is commented on by Bauer on page  CDPVSCongenital disorders of the peripheral visual systemFSLFMRIB Software LibraryMVIMild‐to‐moderate visual impairmentPCCPosterior corpus callosumROIRegion of interestSVISevere visual impairmentTSCTypically sighted comparisonSevere visual impairment in mid‐childhood is associated with reduced integrity of visual pathways and reduced thalamus volume.A large proportion of the cortex is dedicated to the processing of visual information in typically sighted children and adults. Visual experience in development appears vital for establishing the necessary architecture in visual cortical areas that support visual processing throughout life.Congenital visual impairment has been found to be associated with several structural and functional brain differences in adulthood. The strongest effects have been found in neural structures that are involved in visual processing. The optic nerve and optic chiasm are reduced in volume, as well as alterations in microstructural organization of their white matter.We investigated the relationship of congenital visual impairment with structural brain organization during middle childhood and, specifically, compared the brain organization associated with differing levels of visual impairment. The study focused on children aged 8 to 12 years who had congenital disorders of the peripheral visual system (CDPVS), i.e. disorders of the globe, retina, or anterior optic nerve, and vision level of severe visual impairment (SVI) or mild‐to‐moderate visual impairment (MVI). According to the existing adult literature, we predicted differences in brain structures, particularly in regions associated with vision processing, in children with visual impairment versus those with typical sight and greater differences in those with SVI versus MVI. In addition to whole‐brain comparisons, we focused on the thalamus, optic radiations, and corpus callosum, as highlighted in the adult literature.The analysis presented here were part of a wider cross‐sectional study on the neural and cognitive sequelae of congenital visual impairment during mid‐childhood. This study was performed in accordance with the Declaration of Helsinki. The study was approved by the National Health Service Research Ethics Committee (12/LO/0939). All parents provided written informed consent for participation and publication of results, and children provided verbal assent for participation.The inclusion criteria were as follows: (1) children with CDPVS and no other known CNS disorder in the paediatric diagnosis, according to ophthalmological report; (2) age 8 to 12 years; (3) congenital visual impairment, with estimated best‐corrected visual acuity of logMAR 0.6 or worse; (4) verbal IQ greater than 75 or attending mainstream school and performing at age‐appropriate level; and (5) English as their first language. Exclusion criteria were preterm birth , cerebral visual impairment, ‘complex’ CDPVS (including known CNS involvement), endocrine abnormalities, epilepsy, and additional neurological impairments, e.g. motor.Recruitment was undertaken through initial identification via the patient database of the tertiary developmental vision neurodisability clinic , which is the primary research site, or the local collaborator of a tertiary children's eye hospital  and an open recruitment call through charitable and educational agencies associated with visual impairment. Parents could also self‐refer. Information on the child's ophthalmological disorder and vision level were obtained from medical notes by the clinician (ND or collaborator).Children in the typically sighted comparison (TSC) group were recruited via local advertisement for a parallel study that used the same MRI protocol. Inclusion criteria included same age and similar level of verbal intelligence, normal or corrected‐to‐normal vision, and English as a first language. Children were excluded if they had any known neurological or psychiatric conditions.Congenital childhood vision disorders with SVI are rare, with an estimated prevalence of less than 2 to 3 per 10 000 live births in the UK, rising to 5 to 6 during childhood.p=0.796). No differences in movement (maximum framewise displacement) were found between the vision level groups .In the wider study, 18 children with visual impairment consented (via parents) to participate over the duration of the study (2012–2014). Twelve of the children with visual impairment participated in this part of the study. The remaining six children did not participate because their parents were concerned about safety or time commitment. Of those participating, two  had to be excluded from final analysis owing to poor data quality. The final sample consisted of five children with SVI  was trained by a paediatrician specialized in visual impairment to assess visual acuity using the Sonksen logMAR test.3 image resolution . Diffusion MRI was acquired using echo‐planar diffusion‐weighted images with an isotropic set of 60 non‐collinear directions at b=1000s/mm2, interleaved with four b0 volumes. Whole‐brain coverage was obtained with 60 contiguous axial slices at 2.5mm3 resolution .All scans were performed on a Siemens Avanto 1.5 T clinical system , using a self‐shielding gradient set with maximum gradient strength of 40mT/m and a 32‐channel quadrature head coil. T1‐weighted volume scans were acquired using a whole‐brain coverage three‐dimensional fast low angle shot structural image acquired at 1mmt‐tests with 10 000 permutations and cluster‐free threshold enhancement to correct for multiple comparisons using FSL randomise.Whole‐brain voxelwise comparison of grey matter compartments were performed. Local differences in grey matter volume were analysed with the voxel‐based morphometry pipeline part of the FMRIB Software Library (FSL), an optimized VBM protocol carried out with FSL tools.To obtain the volume of the thalamus, a model‐based segmentation and registration approach was used (FSL FIRST). Accurate segmentation of the thalamus was visually inspected for all participants. The total brain volume for each participant was obtained using FSL SIENA.Diffusion‐weighted imaging allows the quantification of water diffusion in tissue in vivo. Correction for eddy current induced artefacts, motion, and field inhomogeneities were applied using the FSL eddy. Susceptibility artefacts were corrected with FSL topup using the b0 images. The images were then submitted to a non‐local means denoising algorithm in the Diffusion Imaging in Python (DiPy) v0.8.0 package to boost the signal‐to‐noise ratio. Next, a brain mask of the b0 image was created using FSL BET. White matter integrity was quantified as fractional anisotropy.Differences in white matter integrity were compared between the groups using tract‐based spatial statistics.For reconstruction of the optic radiation, seed and inclusion regions were defined on fractional anisotropy maps for each participant in subject space.For reconstruction of the posterior corpus callosum (PCC), a seed mask was defined on the posterior quarter of the corpus callosum on a sagittal slice. To define the posterior quarter, the number of voxels greater than 0.1 fractional anisotropy were counted on a medial slice and divided into equal‐length parts  followed by a post hoc Dunn test for group contrasts carried out using the scikit‐posthocs package v0.6.1 for Python. A significance threshold of Table Anatomical MRI scans were obtained from the 10 children with visual impairment; visual inspection of the images indicated no gross structural abnormalities in eight of the 10 children. Reduction of the right optic nerve was apparent in one child; small eyes were apparent in another child . There was a trend‐level difference in total grey matter with lower values in the SVI group than in the TSC group (SVI vs TSC: p=0.091). Voxelwise comparison indicated no significant between‐group differences after correction for multiple comparisons. The corrected statistical map showed a cluster of decreased grey matter in the SVI group versus TSC at pcorrected=0.08. This cluster was adjacent to the optic radiations and seemed to extend into the white matter, indicating that this may be an artefact of white matter differences.There was no statistically significant difference in total grey matter volume between the vision groups . Follow‐up contrasts indicated a smaller left thalamus volume, corrected for whole‐brain volume, in the SVI group versus the TSC group .The volume of the thalamus was compared between the vision groups. Statistical comparison indicated a trend‐level difference between the groups for the relative volume of the left thalamus . Differences in white matter microstructural organization across the whole brain were investigated. Tract‐based spatial statistics analysis indicated a significant reduction of fractional anisotropy around the optic radiations and the PCC in the SVI group compared with TSC . There were no significant differences after correction for multiple comparisons for the MVI versus TSC, or MVI versus SVI comparisons.There was no significant difference between the groups in total white matter volume . Follow‐up contrasts indicated lower fractional anisotropy in the SVI than in the TSC group in the left and right optic radiations .Statistical analysis of fractional anisotropy within the reconstructed optic radiations indicated significant differences between the groups . Follow‐up contrasts showed a reduction in the SVI group versus the TSC group and the MVI group  and differences in thalamus volume in mid‐childhood. These findings suggest that the organization of central visual structures is influenced by the quantity or quality of sensory visual inputs during development before mid‐childhood.


The choice of zebrafish larval hindbrain ventricle is key to providing a reliable observation of metastasis in vivo in this studyMetastatic melanoma ranks amongst the deadliest types of cancer, and metastasis itself is the primary killer in most solid tumours, yet gaps still exist in our understanding of this process. We know that innate immune cells, particularly tumour‐associated macrophage, play a significant role in promoting metastasis (Kitamura, Qian, & Pollard, In this study, Roh‐Johnson et al. establish a zebrafish larval xenograft melanoma model and image the interaction of macrophage with melanoma cells in vivo. The authors employ several new techniques to describe the unexpected mechanism of contact‐dependent cytoplasmic transfer which macrophage uses to promote melanoma cell invasion Figure .The choice of zebrafish larval hindbrain ventricle is key to providing a reliable observation of metastasis in vivo in this study as it allows transplantation into a well‐isolated tissue, which is well served by the smaller circulatory vessels. Similar to the mammalian situation, cancer cells from here need to invade surrounding tissue, undergo intravasation and extravasation so as to seed in a distant site, such as the caudal hematopoietic tissue of the zebrafish. Furthermore, the transparent zebrafish larvae facilitate the visualization of cell–cell interaction during cancer cell metastasis, allowing the authors to describe the role of components of the immune system in establishing secondary tumours. Mirroring data from mammalian models, host macrophage recruitment to the primary xenograft of human melanoma cells enables tumour metastasis but is prevented by the removal of innate immune cells in pu.1‐negative larvae. Live imaging with a novel TNF‐α reporter also enabled the authors to classify the tumour‐associated macrophage spending a prolonged amount of time in contact with melanoma cells as having an alternatively activated phenotype as opposed to classically activated, proinflammatory macrophage.From observing the cell‐to‐cell contact of macrophage and tumours, and supported by previous studies describing the potential of macrophage to transmit cytoplasmic material to other cell types (Saha et al., Further evidence for the process is provided by genetic modification of macrophage recruitment to the implantation site through the modification of the Toll‐like receptor adaptor molecule Myd88. Myd88‐negative zebrafish showed decreased macrophage recruitment to the implantation site, whereas macrophage expressing a constitutively active Myd88 showed greater macrophage infiltration. Downstream this resulted in a corresponding decrease or increase in tumour dissemination, respectively, demonstrating the role of macrophage in the process. With intravital time‐lapse imaging studies, a specific strength of the zebrafish model, the authors describe the correlation between macrophage and melanoma cell contact and subsequent melanoma metastasis, and are able to confirm these observations with an in vitro equivalent of the same model, also showing an increase in cancer cell directional persistence post‐cytoplasmic transfer. Finally, the macrophage cytoplasmic transfer to melanoma cells correlating with their metastatic potential is also confirmed in a murine lung metastasis model using subcutaneous injection of mouse melanoma B16F10 cells and genetic labelling of macrophage cytoplasmic transfer. The study provides a novel insight into the mechanisms by which the innate immune system, specifically macrophage, interact with the tumour to encourage metastasis, a mechanism potentially amenable to therapeutic intervention in treating cancer. Transfer of cytoplasmic material from macrophage is itself not a novel observation, but in the cancer context of macrophage aiding metastasis is an exciting discovery. The next question is the identity of pro‐metastatic factors involved. Conventional candidate molecules include matrix metalloproteinases, miRNAs and molecules regulating cell motility; however, transcription factors such as ZEB1 (Cortés et al., In this study, the authors use a highly innovative approach, with robust confirmation in murine and human models, to demonstrate a strong link between the pro‐growth functions of the innate immune system and cancer metastasis, providing strong parallels between regeneration and the promotion of tumour growth. This study greatly advances the field and provides exciting new avenues to address the cancer problem.The authors declare no conflict of interests.


This review aims to answer frequently asked questions related to the clinical implementation of screening and the management of screening results.The current approach to screening for pre‐eclampsia is based on guidelines that rely on medical and obstetric history in early pregnancy to select a high‐risk group that might benefit from low‐dose aspirin. However, combined screening tests with the addition of biophysical and biochemical measurements have shown significantly better detection rates for preterm pre‐eclampsia. Furthermore, the administration of aspirin for the 10% screen‐positive group can lead to a significant reduction in severe and preterm forms of  In the last updated Guideline, “Management of Hypertensive Disorders of Pregnancy”, in 2014, the Society of Obstetric Medicine of Australia and New Zealand recommends assessment of maternal medical and obstetric history for risk indicators that predispose women to pre‐eclampsia (PE). Women who are considered at high risk are recommended prophylactic treatment with low‐dose aspirin,Unfortunately, the prediction of PE with maternal history alone is limited. When history‐based screening is used, just 40% of women who will develop preterm PE are identified.This article aimed to answer common questions in routine clinical practice about the combined screening test for PE.A good screening test should identify important health problems at asymptomatic or early stages of disease, be easily accessible, fast, economically balanced and should have a reasonably acceptable false positive rate to minimise possible harm from unnecessary intervention.The Society of Obstetric Medicine of Australia and New Zealand advises screening for all women at their first prenatal visit by assessing for predisposing risk indicators according to maternal demographic characteristics, and medical and obstetric history.In 2013, the American College of Obstetricians and Gynaecologists (ACOG) recommended screening by taking an accurate history,Similar to the new approach by ACOG, the National Institute for Health and Care Excellence (NICE) in the UK identifies high‐risk pregnancies by the distinction between major‐ and moderate‐risk indicators according to maternal characteristics and medical history.Although recommended by many Obstetrics and Gynaecology societies, screening tests based on maternal characteristics and history alone perform poorly.Several models have been developed for the prediction of PE.https://fetalmedicine.org/research/assess/preeclampsia.The new FMF competing risks model assumes that all women would develop PE if they remained pregnant indefinitely. The algorithm estimates the distribution of disease across gestation by combining maternal characteristics and history with the results of biophysical , mean uterine artery pulsatility index), and biochemical measurements ).Studies have shown that the highest detection rate for PE is achieved using the FMF algorithm with a combination of maternal characteristics and history, MAP, mean uterine artery pulsatility index, and PLGF. In the ASpirin for evidence‐based PREeclampsia prevention (ASPRE) trial, a risk cut‐off of 1:100 led to a DR of 76.7% for preterm PE, but just 43.1% for term PE, at 9.2% false positive rate (FPR).The FMF model has been internally and externally validated,The risk cut‐off used in PE screening is determined by: (i) the background prevalence of PE in a given population; (ii) the accepted SPR for treatment with aspirin; and (iii) cost‐effectiveness analysis.As the background risk for PE in twins is higher than in singletons, the combined screening for PE has a poorer performance in multiple pregnancies, resulting in a very high SPR to achieve reasonable sensitivity.A subgroup analysis of the ASPRE trial showed that patients who are identified as screen‐positive according to the NICE or ACOG guidelines, but are screen‐negative with combined screening, have a lower risk of preterm PE than the background risk in the obstetric population (NICE: 0.65% (95% CI 0.25–1.67%) ACOG: 0.25% (95% CI 0.18–0.33%)).Offering cell‐free DNA testing as a primary screening tool for trisomy 21 has shown convincing results.Various combinations of biophysical and biochemical markers in the FMF algorithm can be used to achieve different detection rates Table .6, 8 WheSecond trimester screening using maternal factors, mean uterine artery pulsatility index, MAP and PLGF at 19–24 weeks of gestation is of superior predictive value to first trimester screening. Studies have shown that screening at 19–24 weeks is associated with a prediction of 99% for early PE, 85% for preterm PE and 46% for term PE, at FPR of 10%.Although screening at later gestations performs better because of its proximity to the event, and may allow for increased surveillance and tailored models of care, late prophylactic interventions have not been proven to reduce the risk of the disease. Although aspirin may still be beneficial when initiated after 16 weeks of gestation,Given its excellent detection of preterm PE, a positive screening result should inform closer follow up for signs of PE. In the setting of suspected PE after 20 weeks of gestation, the implementation of sFLT‐1/PLGF ratio with its excellent negative predictive value of 99.3% (95% CI 97.9–99.9%) for the development of PE within 1 week can also help in the clinical decision‐making process.In women who have been noted to have a higher MAP in the first trimester, commencement of antihypertensives should be considered, as less tight control of severe hypertension has been associated with adverse maternal and perinatal outcomes.Women who are screen‐positive also have a higher incidence of small‐for‐gestational age infants than the general obstetric population.Cost‐effectiveness depends on various factors, which are not always clearly ascertainable. The cost of the screening test, implementation of preventative interventions and closer follow up need to be compared with cost savings from prevention of PE, preterm delivery, and long‐term morbidity for mother and child. There is a growing body of evidence that especially early‐onset PE and extreme prematurity are associated with an increased economic burden.The ASPRE trial showed an impressive reduction of preterm PEPrevention of PE with aspirin seems to be safe and inexpensive. For these reasons, universal prophylaxis has been discussed.Combined screening for PE at 11–14 weeks of gestation shows good detection rates for early and preterm PE, and is superior to the current recommended approach by the Society of Obstetric Medicine of Australia and New Zealand, NICE and ACOG guidelines. This patient‐specific approach is now acknowledged and supported by international bodies, and further trials with a focus on cost‐effectiveness, and the affects on other maternal and perinatal outcomes are likely to follow in the near future.


We next used structural equation modeling to test if variation in Pgi mediated gene flow as measured by Fst at putatively neutral loci. In a year when the population was recovering following a large decline, individuals with a genotype associated with greater dispersal ability were found at significantly higher frequencies in populations isolated by water and forest, and these populations showed lower levels of genetic differentiation at neutral loci. These relationships disappeared in the next year when metapopulation density was high, suggesting that the effects of individual variation are context dependent. Together our results highlight that (1) more complex aspects of landscape structure beyond just the configuration of habitat can be important for maintaining spatial variation in dispersal traits and (2) that individual variation in dispersal plays a key role in maintaining genetic variation across fragmented landscapes.There is now clear evidence that species across a broad range of taxa harbor extensive heritable variation in dispersal. While studies suggest that this variation can facilitate demographic outcomes such as range expansion and invasions, few have considered the consequences of intraspecific variation in dispersal for the maintenance and distribution of genetic variation across fragmented landscapes. Here, we examine how landscape characteristics and individual variation in dispersal combine to predict genetic structure using genomic and spatial data from the Glanville fritillary butterfly. We used linear and latent factor mixed models to identify the landscape features that best predict spatial sorting of alleles in the dispersal‐related gene  Understanding how fragmentation affects dispersal and gene flow across human‐modified landscapes has long been a goal in evolutionary biology. Growing evidence suggests that individuals of the same species can vary considerably in their dispersal and movement traits. While the effects of this individual dispersal variation on range expansions and invasions have been well characterized, knowledge of how it might mediate genetic responses to landscape fragmentation are almost entirely lacking. Here, we demonstrate that individual variation in dispersal is key to the maintenance of genetic variation during recovery following a large decline in a butterfly metapopulation. We further show that spatial variation in dispersal is not maintained by the configuration of habitat patches alone, but by a more complex genotype–environment interaction involving the landscape matrix . This challenges the simplified landscape representations typically used in studies of dispersal evolution that ignore heterogeneity in the landscape matrix. More broadly, our results highlight the interplay of adaptive and neutral processes across fragmented landscapes, suggesting that an understanding of species vulnerability to landscape fragmentation requires consideration of both.Dispersal is key to the maintenance of genetic variation and adaptive potential in fragmented landscapes. Differences in species ability to maintain genetic diversity in fragmented landscapes can, in part, be explained by interspecific differences in dispersal capacity  in the Åland Islands, Finland. Importantly, individuals vary in their dispersal ability: butterflies heterozygous or homozygous for the “c” allele in a SNP associated with the gene phosphoglucose isomerase (Pgi) have higher flight metabolic rate, which translates to substantially increased dispersal propensity and dispersal distance in the field, especially at cooler temperatures  that contain one or both of its host plants, Pgi (Mc1:1687:14486), Mc1:1687:14486, Mc1:1873:36910, Mc1:1124:71239, Mc1:752:33517 , we excluded them from downstream analysis. We further excluded 98 patches in 2011 and 129 in 2012 with only a single larval nest where the effects of genetic drift are expected to be especially strong, giving a sample size of 152 patches  in 2011 and 193 patches  in 2012.In 2011 and 2012, three larvae per nest were collected from patches across Åland and were genotyped at 40 neutral markers and five putatively function markers from candidate genes related to flight or dispersal traits including M. cinxia individual—either a value of 1 (not resistant to movement) or 10 (restricts movement), generating a total of 20 surfaces with different combinations of resistant and nonresistant features  or resistance distance (from CIRCUITSCAPE) between focal patch i occupied in year t and source patch j occupied in the previous year (t – 1), and jN is the number of M. cinxia nests found in source patch j in year t – 1. The constant α scales the dispersal kernel and should be equal to 1/mean dispersal distance of the species, which has been estimated to be 1 km , isolated populations and at the lowest in old, isolated populations because dispersive individuals also have high emigration rates , and was determined for each year separately using BAPS6 . We used this approach since alternatives such as model averaging and summing akaike weights can lead to flawed interpretation of effects when variables are even weakly collinear  simultaneously. This applies a stronger control of background genetic structure, particularly when structure is complex and hierarchical , and also allowed us to confirm that we only see associations between connectivity and allele frequencies for Pgi and not for other loci. A drawback is that it cannot incorporate additive or interactive effects, and thus we used it only to confirm results in the partitions of the data that were found to be significant in linear mixed effect models . In addition to testing the individual Si variables identified as important in the mixed models, we explored potential additive effects by testing for association between loci and Si variables that incorporated the resistance of two or more of the landscape features. For example, the additive effects of Siwater and Siforest were included in the model as the predictor Siwater+forest , or indirectly through its effect on the spatial distribution of individuals with contrasting Pgi genotypes.Our second objective was to test if intraspecific variation at the st .Direct effects were tested using linear mixed effect models with neutral genetic differentiation as a response variable. A patch‐specific measure of genetic differentiation was calculated by taking the average pairwise Weir and Cockerham unbiased FPgi using structural equation modeling (SEM) implemented in the lavaan library  connectivity directly affects genetic differentiation, and (2) connectivity indirectly affects genetic differentiation through its effects on the frequency of the Pgi‐c allele. Connectivity was included in the model as a latent variable – i.e., a construct that is not easily measured directly but can be indicated by a number of observed, and potentially correlated, variables that have some level of measurement error . We included connectivity as a latent variable, with the five connectivity hypotheses Simetapop, Siwater, Siforest, Siagriculture, and Siroads as indicators. We estimated model parameters using maximum likelihood and assessed model fit with chi‐square tests, where a P‐value > 0.05 indicates the model‐implied covariance fits the observed covariance well . The relative effects of paths were evaluated from standardized path coefficients and individual significance of paths.We next tested for indirect effects of landscape structure on genetic differentiation in the partitions of data where we found strong association between connectivity and  Rosseel . Originaforest and Siwater, and seven of the models were embellishments of higher ranked models—i.e., they were the same as a higher ranked model but included uninformative parameters with little effect on model fit  in this partition. The full structural equation model including all five landscape connectivity indicator variables showed poor fit as indicated by a significant deviation of the observed covariance from the model‐implied covariance . Because Simetapop did not have strong effects in our earlier models, we removed it as an indicator variable. This model fit the data well as indicated by no significant deviation of the observed and modeled‐implied covariance . The removal of Simetapop did not affect the strength or significance of associations. Siwater and Siforest were the strongest and only significant indicators of connectivity . We further found that patch connectivity alone did not predict genetic differentiation at neutral markers, but rather the effect of landscape on genetic structure was mediated through individual variation in the Pgi locus; populations with higher frequencies of the Pgi‐c allele had lower Fst. In the following year when the density of populations increased, these relationships disappeared, suggesting that good dispersers only have an advantage when there are many empty patches to colonize. Together our results suggest that both individual variation in dispersal traits and landscape matrix heterogeneity are important for predicting spatial patterns of genetic variation.Here, we show that patch connectivity metrics incorporating landscape matrix best predicted the distribution of a dispersal polymorphism during a population recovery in the Glanville fritillary butterfly. In particular, newly colonized populations that were isolated by water and forest matrix had significantly higher frequencies of an allele associated with increased dispersal ability , and geographic distance might have been sufficient to capture patterns. It should also be noted that previous studies tested only a single model of patch connectivity, whereas we competed several models assuming different landscape structures.We found that spatial sorting of individuals based on their ers Fig. . In conters Fig. . This suPgi dispersal polymorphism in the M. cinxia system in the Åland islands is not maintained by variation in patch configuration alone , but that the landscape matrix further influence dispersal patterns. This is an important finding given that studies investigating the drivers of dispersal evolution almost exclusively use simplified landscape models that assume a homogenous matrix  at moderate and cool temperatures, but individuals without a c allele fly better at very warm temperatures ; an allelic combination at these two loci was associated with maximal metabolic endurance in M. cinxia. Linkage of Pgi to other functional loci have not been resolved, but the low frequency and fitness of individuals homozygous for the C allele suggest possible linkage to a deleterious mutation . Interestingly, the four other candidate loci included in our study did not show significant associations with landscape structure, despite being found as outliers in previous work . Future work would benefit from fine‐tuning landscape resistance surfaces to better account for these potential small additive effects , which were excluded from our analysis because estimating Fst requires larger samples  and 2012 (B). Each point is a patch and the colour indicates membership to a particular cluster.Figure S2. Associations between the frequency of the Pgi c allele and two indices of connectivity (Siwater+forest and log‐transformed Simetapop) for each genetic cluster in 2011 (A) and 2012 (B).Figure S3. Q‐Q plots showing observed and expected –log10 p‐values for latent factor mixed models testing for associations between 39 loci and connectivity metrics in new populations in 2011 (A‐E) and Supporting Figures – DiLeo et al. Evolution Letters 2012 (F‐G).Figure S4. Scatterplots showing the relationship between the frequency of the Pgi‐c allele and a patch connectivity metric incorporating roads as a facilitator to dispersal (A) and water as a barrier to dispersal (B) for the year 2012.Figure S5. Scatterplots showing relationships between population‐specific genetic differentiation (Fst) and the frequency of the Pgi‐c allele (A), Fst and Siforest (B) and Fst and Siwater (C) for newly colonized populations in 2011.Figure S6. Sources of variation in patch connectivity per year. Yearly differences in connectivity for newly colonized populations, Simetapop, (A), geographic distance between source and target patches for newly colonized populations, dij (B), number of nests in source patches, Nj (C), and total number of source patches (D).Figure S7. Relationships between connectivity and minor allele frequencies (MAF) of the four other candidate loci in newly colonized and old populations.Click here for additional data file.Table S1. Descriptions of landscape connectivity hypotheses.Table S2. Pearson correlation coefficients among the five landscape connectivity variables.Table S3. Posterior mean estimates, standard deviation, and quantiles of Bayesian nonspatial and spatial INLA models.Table S4. Results of model selection on linear mixed effect models testing for associations between the frequency of the Pgi‐c allele, population age (old or new), and competing metrics of patch connectivity (Si) for the year 2011.Table S5. Results of model selection on linear mixed effect models testing for associations between the frequency of the Pgi‐c allele, population age (old or new), and competing metrics of patch connectivity (Si) for the year 2012. Only the top 15 models are shown.Table S6. Genomic inflation factors of for each value of k and each predictor tested in latent factor mixed models.Table S7. Results of model selection on linear mixed effect models testing for associations between population genetic differentiation (Fst), population age (old or new), and competing metrics of patch connectivity (Si) for the year 2011.Table S8. Results of model selection on linear mixed effect models testing for associations between population genetic differentiation (Fst), population age (old or new), and competing metrics of patch connectivity (Si) for the year 2011 with a singly outlier with a high Fst value removed. Only the top 15 models are shown.Table S9. Results of model selection on linear mixed effect models testing for associations between population genetic differentiation (Fst), population age (old or new), and competing metrics of patch connectivity (Si) for the year 2012.Click here for additional data file.Appendix A – Supplementary methods and resultsClick here for additional data file.Appendix B: Information about the 5 candidate and 40 neutral SNPs used.Click here for additional data file.


Promising HIV vaccine candidates are steadily progressing through the clinical trial pipeline. Once available, HIV vaccines will be an important complement but also potential competitor to other biomedical prevention tools such as pre‐exposure prophylaxis (PrEP). Accordingly, the value of HIV vaccines and the policies for rollout may depend on that interplay and tradeoffs with utilization of existing products. In this economic modelling analysis, we estimate the cost‐effectiveness of HIV vaccines considering their potential interaction with PrEP and condom use.We developed a dynamic model of HIV transmission among the men who have sex with men population (MSM), aged 15‐64 years, in Seattle, WA offered PrEP and HIV vaccine over a time horizon of 2025‐2045. A healthcare sector perspective with annual discount rate of 3% for costs (2017 USD) and quality‐adjusted life years (QALYs) was used. The primary economic endpoint is the incremental cost‐effectiveness ratio (ICER) when compared to no HIV vaccine availability.HIV vaccines improved population health and increased healthcare costs. Vaccination campaigns achieving 90% coverage of high‐risk men and 60% coverage of other men within five years of introduction are projected to avoid 40% of new HIV infections between 2025 and 2045. This increased total healthcare costs by $30 million, with some PrEP costs shifted to HIV vaccine spending. HIV vaccines are estimated to have an ICER of $42,473/QALY, considered cost‐effective using a threshold of $150,000/QALY. Results were most sensitive to HIV vaccine efficacy and future changes in the cost of PrEP drugs. Sensitivity analysis found ranges of 30‐70% HIV vaccine efficacy remained cost‐effective. Results were also sensitive to reductions in condom use among PrEP and vaccine users.Access to an HIV vaccine is desirable as it could increase the overall effectiveness of combination HIV prevention efforts and improve population health. Planning for the rollout and scale‐up of HIV vaccines should carefully consider the design of policies that guide interactions between vaccine and PrEP utilization and potential competition. Seattle‐King County Public Health surveillance rigorously monitors epidemic indicators and care cascade milestones, and it was the first US urban city to reach the “90‐90‐90” goal set by WHO. In King County, health officials estimate 6980 residents lived with diagnosed HIV infection in 2014, totalling more than half of all HIV cases in the state Seattle has combated new infections with pre‐exposure prophylaxis (PrEP). As a complement to national guidelines for prescribing and monitoring PrEP Recent progress in HIV vaccine development means another biomedical product for prevention is approaching the horizon of availability Real tradeoffs have to be made when offering imperfect prevention products. This is an important question because all available biomedical products for HIV prevention are imperfect, and the evidence of PrEP cost‐effectiveness is mixed 2We conducted a health economic modelling analysis to estimate the impact and cost‐effectiveness of an HIV vaccine offered alongside PrEP in Seattle, WA.2.1The study population includes MSM ages 15‐64 in Seattle, WA, using Public Health Seattle‐King County and the Washington State Department of Health reports as the primary source for population data 2.2We developed a deterministic dynamic compartmental mathematical model Figure  to simulThe model is used to simulate the HIV epidemic without an HIV vaccine to provide a reference scenario for the evaluation of the vaccine impact. PrEP is introduced in 2015 followed by HIV vaccine availability in 2025. The effectiveness of the intervention over the 2025‐2045 period is evaluated by comparison of the intervention to the reference scenario assuming no changes in the current Centers for Disease Control (CDC) guidelines for HIV prevention and treatment We followed recommendations from the ISPOR‐SMDM Dynamic Transmission Modeling Task Force and the Second Panel on Cost‐Effectiveness in Health and Medicine 2.3The model is parameterized with epidemiological data representative of the HIV epidemic among MSM in Seattle. Calibration occurs from 2004 to 2014. Demographic and sexual behaviour characteristics including average number of partners per year, frequency of sex acts, proportion of acts protected by condoms, and lifetime duration of sexual activity were collected from published data Tables  and S3. 2.42.4.1® , a combination product of 200 mg emtricitabine (FTC) and 300 mg tenofovir disoproxil fumarate (TDF), for PrEP is introduced in the model beginning in 2015. After 2014, the model assumes that 25% of high‐risk MSM and 6% of low‐risk MSM start using PrEP annually with 20% discontinuation rate. This assumption closely reproduces the expansion of PrEP usage in Seattle up to 2017 Daily oral Truvada2.4.2® adjuvant  beginning in 2025  and bivalent gp120 protein subunit boost with MF5925 Table  13. The 2.4.3The model explores potential interactions among HIV vaccines, PrEP and condom use that risk mitigation of clinical and economic impact. We assume the protection from dual‐use of PrEP and vaccine is multiplicative. Condom use may decrease as PrEP use increases. The demand for PrEP may decrease when another biomedical HIV prevention choice is on the market and HIV vaccine utilization increases. We explore the following utilizations of PrEP and HIV vaccine delivery: (i) vaccine licensure in 2025; (ii) PrEP is targeted to high‐risk MSM while the vaccine is targeted to all MSM; (iii) HIV vaccination campaign cover 60% of low‐risk MSM every five years beginning in 2025  gained due to the vaccine programme. Person‐time in each health state is multiplied by the corresponding preference‐based utility weight, discounted 3% annually, and summed over the time horizon to calculate total QALYs 2.6Costing of HIV prevention services follows a unit costing approach, also known as ingredients‐based, while the cost of HIV treatment relies on published studies based on aggregate healthcare costs. The cost of a clinic visit for HIV prevention services with risk reduction counselling is based on Centers for Medicare and Medicaid Services reimbursement rates for the corresponding relative value units in the Physician Fee Schedule January 2018 release PrEP costs include medication, quarterly clinic visits, testing for HIV and STIs, and other routine laboratory tests for monitoring Table . The lau2.7Parameter uncertainty was evaluated in a sensitivity analysis 3Maintaining the current trends of PrEP use, rates of diagnoses, linkage to care, treatment, and viral suppression, our analysis estimates 3074 new HIV infections between 2025 and 2045. We project an HIV prevalence of 7.7% among MSM living in Seattle in 2045, a decrease from 13.9% in 2018. On the path to this decline in prevalence, the model projects almost 15,000 HIV‐uninfected men using PrEP in 2045, double the number in 2018, resulting in the partial protection in one‐third of MSM.HIV vaccines are projected to decrease the number of new infections, lower HIV prevalence and gain QALYs Table . SeattleWith model parameters based on US cost data over a 20‐year time horizon, the total incremental cost of introducing an HIV vaccine in Seattle is estimated to be $30 million from a healthcare sector perspective. Given the assumed rate of product substitution, PrEP costs would decrease by $450 million and the cost of $532 million on HIV vaccines would be introduced  of $42,473 per QALY gained. The cost per HIV infection avoided was $26,151. Scenarios in Figure 3.2The greatest source of uncertainty and driver of cost‐effectiveness is HIV vaccine efficacy. Vaccine efficacy of 70% had an ICER of $4136 per QALY while 30% was $108,824 per QALY compared to no vaccine  the opportunity to vaccinate a larger fraction of the population than with PrEP alone and (ii) the downside from potential substitution with a less effective prevention product. Assuming most high‐risk and some low‐risk MSM in Seattle are using PrEP in 2025, rapid uptake of HIV vaccine by 60% of men, and declines in PrEP use with increasing vaccine uptake, we found an ICER of $42,473 per QALY gained compared to PrEP with no vaccine. In this case, HIV vaccines would be cost‐effective using a 1× GDP or 3× GDP per capita, cost‐effectiveness threshold. Though HIV vaccines increased total healthcare costs by $240 million, some costs were offset by reduction in HIV treatment and PrEP medications.Key uncertainties in the analysis affect the results under different scenarios. As expected, scenarios with greater vaccine efficacy were more likely to find vaccines cost‐effective and led to a higher maximum threshold price where the vaccine would remain cost‐effective. Given that an HIV vaccine candidate is in development, the exact frequency of vaccine administrations will be determined with more data about the durability of protection. We have previously explored the impact of HIV vaccine durability and we have been examined implementation approaches for three‐year HIV vaccine campaigns in another study This modelling analysis yields two important lessons: (i) the cost‐effectiveness of HIV vaccines will depend on the utilization and cost of PrEP at the time of launch and (ii) condom displacement with vaccines could diminish the potential benefit of vaccines for the population and lower its value. Even if, however, vaccines induce some condom displacement and decline in PrEP use, we project overall population health benefits. Policies guiding the interactions between these interventions could have substantial impact on the value of each product alone and in combination.We compared the results of this model with two other published studies evaluating the cost‐effectiveness of HIV vaccines in the United States, and our conclusions were consistent when assuming the same vaccine price. A dynamic transmission model of HIV vaccines conducted in the pre‐PrEP era estimated an ICER of $91,000/QALY for universal HIV vaccination and net cost‐savings from targeting MSM when assuming a cost of $500 per vaccine series There are several limitations to this modelling study that deserve mention. Alternative modelling structures, such as sexual network‐ or agent‐based models, could be developed to strengthen the assumption of structural sensitivity. Bernard and colleagues suggest, however, that among a set of specific model structures and parameter sets examined – when applied to this HIV vaccine question – produced similar cost‐effectiveness results, despite the differences in their structure The findings from this study point to several policy considerations. First, further public investment in US HIV vaccine clinical trials is warranted to reduce the uncertainty in expected vaccine efficacy. When regulatory bodies deem an HIV vaccine as having “good enough” efficacy, commercialization may be a challenge. This economic model does not include novel incentives that may be needed to encourage industry partners to commercialize and manufacture the product for global distribution. A target product profile to guide the innovation of better, cheaper, faster and point‐of‐care STI diagnostics may also be needed. Second, more research on and education to prevent a decrease in condom displacement with biomedical HIV prevention products is needed to optimize the potential effectiveness and prevent further outbreak of other STIs such as syphilis and gonorrhoea. Third, value‐based pricing of the vaccine at launch should consider both the risk level of the indicated population and the current cost of PrEP. Risk to public investment in immunization campaigns could be mitigated with outcomes‐based risk‐sharing agreements between government payers and the manufacturer with support from existing CDC surveillance systems.5Moderately effective HIV vaccines have the potential to be a cost‐effective intervention implemented alongside PrEP in Seattle. The potential population health gains from and value‐based price of an HIV vaccine in this setting depends on the degree of interaction and substitution with PrEP. Dual methods could be implemented as complementary products if lower PrEP pricing could be negotiated. Access to an HIV vaccine is desirable as it could increase the overall effectiveness of combination HIV prevention efforts and improve population health. HIV vaccines may have the potential to reach subpopulations that PrEP has been unable to reach. The barriers to implementation of and access to vaccines could be lower with provision at clinic visits compared to prescription drugs that require high adherence to be effective. Planning for the rollout and scale‐up of HIV vaccines should carefully consider the design of policies that guide interactions between potentially competing biomedical HIV prevention strategies.BA is a current employee at Flatiron Health, an independent subsidiary of Roche. JK is the Executive Director of the HVTN. DD is a Senior Staff Scientist working with both the HIV Prevention Trials Network and HVTN.BA, DD, JC, RB, JK and LG contributed to study design. BA and DD modelled the study; BA, DD, JC, RB, JK and LG involved in analysis; BA, DD, JC, RB, JK and LG involved in interpretation of results. BA, DD, JC, RB, JK and LG involved in manuscript writing.Appendix S1. Supplementary material.Click here for additional data file. Click here for additional data file. Click here for additional data file.


An evidence‐base is emerging indicating detrimental and beneficial effects of social media. Little is known about the impact of social media use on people who experience psychosis.ESM).Forty‐four participants with and without psychosis completed 1084 assessments of social media use, perceived social rank, mood, self‐esteem and paranoia over a 6‐day period using an experience sampling method (Social media use predicted low mood, but did not predict self‐esteem and paranoia. Posting about feelings and venting on social media predicted low mood and self‐esteem and high paranoia, whilst posting about daily activities predicted increases in positive affect and self‐esteem and viewing social media newsfeeds predicted reductions in negative affect and paranoia. Perceptions of low social rank when using social media predicted low mood and self‐esteem and high paranoia. The impact of social media use did not differ between participants with and without psychosis; although, experiencing psychosis moderated the relationship between venting and negative affect. Social media use frequency was lower in people with psychosis.Findings show the potential detrimental impact of social media use for people with and without psychosis. Despite few between‐group differences, overall negative psychological consequences highlight the need to consider use in clinical practice. This is the first study to use ESM to highlight the significant association between self‐reported social media engagement and subsequent reductions in mood at the next time‐point in a sample of individuals with and without experience of psychosis.Types of self‐reported behaviours on social media were associated with subsequent changes in mood and paranoia at the next time‐point. Specifically, posting about daily activities was associated with subsequent improvements in positive affect and self‐esteem, whilst viewing social media newsfeeds led to significant reductions in negative affect and paranoia and content consumption was associated with subsequent reduction in negative affect. Conversely, posting about feelings and venting on social media were associated with subsequent reductions in mood and self‐esteem and increases in paranoia and viewing profiles of individuals who were not ‘friends’ on social media and commenting on the posts/pictures of others led to increases in paranoia.People with psychosis are significantly less likely to use social media than people without psychosis.The sample was limited to mostly White British participants and clinicians may have been more likely to refer patients who had previously expressed positive or negative experiences of engaging with social media, which may limit the generalisability of the findings.The design of the study was correlational in nature, so other factors may have also contributed towards the impact of social media use.The study was reliant on participants self‐reporting social media use, which may be subject to inaccurate recall. However, the use of ESM for self‐reported use over a short time‐scale is likely to have helped to mitigate this limitation.The use of social media websites such as Twitter, Facebook and Instagram is widespread. Social media websites allow individuals to construct profiles in which they can maintain and create social networks, circulate details about their daily lives and respond to posts written by others People with mental health problems already use social media websites to self‐manage their mental health. For example, social media can be a helpful coping mechanism to facilitate self‐expression and communication with others with similar experiences and to access motivational content Despite some evidence for the potential therapeutic benefits of social media use, social media engagement may also be harmful for an individual's mental health and wellbeing. For example, several studies have reported a significant link between high social media use and low mood and depression Recent findings suggest there may be specific aspects of social media use that determine whether it is beneficial or detrimental. One such explanation is that social media may elicit downward online social comparisons; that is, comparing oneself less favourably to others, leading to negative feelings per se.Researchers have now begun to explore whether social media behaviours contribute to psychological outcomes. Burke et al. In order to examine the real‐time relationship between social media use and mental health and wellbeing, some researchers have employed experience sampling methodology. For example, Kross et al. Previous research has mainly employed retrospective accounts of social media use, SR, mood, self‐esteem and paranoia, with many studies conducted in adolescent non‐clinical samples. This study aimed to explore in real time the impact of social media use on mood, self‐esteem and paranoia. Specifically, we hypothesised:H1: Social media use will predict low mood and self‐esteem and high paranoia.H2: Passive social media use (content consumption) will predict low mood and self‐esteem and high paranoia, whilst active social media use (content posting and direct communication) will predict high mood and self‐esteem and low paranoia.H3: Higher perceived SR in comparison with others on social media will predict high mood and self‐esteem and low paranoia.H4: The impact of self‐reported social media use and behaviours on mood, self‐esteem and paranoia will be moderated by a diagnosis of psychosis, with a stronger relationship evident in the clinical group.Experience sampling method was used to capture momentary assessments of social media use, mood, self‐esteem and paranoia (within‐subjects), and paper‐based questionnaires were used to capture retrospective reports of these variables (between‐subjects). Experience sampling methodology (ESM) was chosen to explore the impact of social media use because it involves the repeated assessment of variables over a specified time‐period, which is more ecologically valid than traditional retrospective measures n = 44) due to the small number of participants in the sample with bipolar disorder (n = 6). Findings including participants experiencing bipolar disorder are available from the author on request.Participants were recruited via National Health Service (NHS) secondary care mental health services in the UK and through promoting the study on research volunteering websites. Clinical participants were eligible to participate if they had a clinician‐verified experience of first episode psychosis or had received a diagnosis of DSM‐IV schizophrenia‐spectrum disorder or bipolar disorder. Non‐clinical participants were eligible to participate if they self‐reported no current experiences of mental health problems. Further eligibility criteria for all participants were as follows: (i) 18 years of age or over; (ii) able to speak and read English; (iii) able to provide informed consent; (iv) available for a week‐long study; (v) self‐report Facebook or Twitter account; and (vi) self‐report social media use at least three times per week. This paper reports findings from the non‐clinical and psychosis participants only (Trait‐level mood was measured using the Positive and Negative Affect Schedule r = 0.85–0.88) Trait self‐esteem was measured using the Rosenberg Self‐esteem Scale (RSES), which consists of 10 statements on a 4‐point scale  and can be used to measure both trait and state self‐esteem r = 0.70) Trait paranoia was measured using the Paranoia Scale, which comprises 20 statements on a 5‐point Likert scale . The scale has been used in both clinical and non‐clinical samples and has shown good construct validity (α = 0.84) and test–retest reliability  r = 0.80) and can be modified for other social media platforms Baseline assessments of social media use were produced after a review of the literature to identify items that had been used in previous studies. The Social Media Use Integration Scale (SMUIS) was included to measure participants' emotional connection to social media and integration into their daily lives The first question of the ESM assessments asked participants whether they had used social media since the last assessment. If participants selected ‘yes’, they were asked additional sets of questions regarding social media use prior to completing the remaining ESM assessments: (i) social media website used; (ii) social media activities; and (iii) feelings in comparison with others on social media using the SCS To account for the potential that other forms of personal interaction may lead to changes in the variables of interest, participants were asked whether they had spoken with another person since the last assessment (yes/no) and to indicate whether this communication was face‐to‐face, online, text‐message, telephone or a messaging smartphone application. Participants were presented with nine mood‐related adjectives on a 7‐point Likert scale A full list of ESM items is available in Appendix Ethical approval was granted by the local research ethics committee. After consent, participants completed a demographics questionnaire and the baseline and trait‐level measures in order to provide a description of the sample. Participants were given a unique username and password to access the ESM assessments and completed a trial run either on their own smartphone or a smartphone loaned to them for the duration of the study. Participants who were loaned a smartphone for the study were asked to only use the smartphone for activities associated with the study. Specifically, participants were asked to only use the smartphones to contact the researcher with any technical questions that arose during the study period and to complete the momentary assessments. ESM assessments commenced the following morning. Text‐messages containing a link to a secure online site were sent to participants at six pseudo‐random times a day over a 6‐day period. Participants had up to 15 min to click the link and complete the assessments after each text‐message received. The data collection period ranged from 10:00 and 21:00; although, this could be adapted to allow for individual differences in waking hours. Participants were asked to complete an exit evaluation detailing reasons for missed assessments, debriefed by the researcher about the study and provided £20 vouchers (not contingent on the number of assessments completed).t‐tests in spss Version 22 . ESM data were assessed for normality through visual inspection of histograms and analysis of skewness and kurtosis. Analyses of ESM data were performed using Stata, version 14 . The hierarchical structure of ESM data  requires multilevel modelling to be used due to the violation of the assumption of independence of observations. Using maximum likelihood, we fitted 3‐level random intercept models a random intercept for each participant, a random intercept for each participant‐day and participant‐beep error term.Trait‐level mood, self‐esteem, paranoia and perceived SR were analysed by comparing between‐group means using independent H1), social media behaviours (H2) and perceived SR (H3) predicted mood, self‐esteem and paranoia at the next time‐point, separate multilevel linear regression analyses were estimated with PA, NA, self‐esteem and paranoia as the outcome variables, and self‐reported social media use, behaviours and perceived SR as the predictor variables. In all models, socialisation  and group  were included as covariates to ensure any associations with the outcome variables could be attributed to the use of social media, rather than other forms of socialisation or group. To investigate whether the impact of social media use was moderated by a diagnosis of psychosis (H4), a further set of multilevel linear regressions were estimated by looking at the two‐way interaction between group and social media use, with employment status and socialisation included as covariates. Finally, whilst we did not make any specific hypotheses relating to between‐group differences in social media use, odds ratios (ORs) and the corresponding 95% confidence intervals were calculated through a multilevel logistic regression to compare the likelihood of social media use between the clinical and non‐clinical groups.To test whether self‐reported social media use (n = 6) were excluded due to the small sample size. Therefore, the data from a total of 44 participants  are reported in this paper. Demographic information is presented in Table M = 33.7, SD = 9.7, range = 22–54) and non‐clinical  groups did not significantly differ (t(42) = 0.45, P = 0.655). Three participants (16%) in the clinical group and one participant (4%) in the non‐clinical group borrowed a smartphone for the duration of the study. All borrowed smartphones were returned undamaged. Two participants in the clinical group and one participant in the non‐clinical group borrowed a smartphone because they did not own a smartphone; one participant in the clinical group borrowed a smartphone because their own smartphone had poor data connectivity.Fifty‐one people  consented to participate. Data provided from one clinical participant were excluded from analyses as they did not complete any assessments over the study period. Data from participants with bipolar disorder . The proportion of assessments completed per participant ranged between 25% and 91.6%. The mean number of completed assessments did not significantly differ between clinical and non‐clinical groups , males and females , on the basis of whether a participant borrowed or owned the smartphone that was used for data entry , employment status  = 0.86, P = 0.495) or level of education  = 0.22, P = 0.928). Finally, there was no correlation between assessment completion rate and age .Out of the total 1584 assessments that were possible during the study period, 1084 were fully completed by participants , but was associated with mental health status, with participants in the clinical group reporting lower levels of social media use in comparison with participants in the non‐clinical group . Social media use was not significantly associated with gender , whether a participant borrowed or owned the smartphone that was used for data entry , employment status  = 2.00, P = 0.114) or level of education  = 0.64, P = 0.638).General social media use across the study duration was not significantly related to participant age , NA , self‐esteem , paranoia  and SR  did not predict social media use. Therefore, it likely that social media use predicted mood, rather than being predicted by mood.Multilevel logistic regression analyses were conducted to explore whether trait variables predicted social media use across the study period. Trait‐level PA  or NA . This finding does not support H4 that psychosis would moderate associations between social media use and mood.Psychosis was not found to moderate the relationship between social media use and PA  or paranoia ; (ii) posting about daily activities and PA  or self‐esteem ; (iii) posting about feelings and PA , NA , self‐esteem , paranoia  or perceived SR ; (iv) venting on social media and PA , self‐esteem  or paranoia ; (v) content consumption and NA ; (vi) viewing social media newsfeeds and NA  or paranoia ; (vii) viewing profiles of friends and perceived SR ; vii) viewing profiles of strangers and paranoia ; and (viii) commenting on another person's post or picture .We also explored whether psychosis moderated the relationship between specific types of social media use that had led to significant changes in outcome variables. Psychosis did not moderate the relationship between: (i) content posting and NA . Separate analyses revealed that clinical participants were less likely to use Facebook , but there were no differences in Twitter  or Instagram  use.Clinical participants were less likely to use social media than non‐clinical participants  proposed that posting about feelings and venting on social media is an emotional form of self‐disclosure, whereas posting information about daily activities is general self‐disclosure In contrast with previous research Experiencing psychosis did not moderate the impact of social media use or behaviours; however, this did moderate the relationship between venting and NA. This suggests that maladaptive coping strategies, such as venting, may be more detrimental for individuals who experience psychosis in comparison with the general population. Although, on the whole, a diagnosis of psychosis did not moderate the impact of social media use and behaviours, the finding remains clinically important because the impact of social media use may have more serious consequences in individuals with psychosis due to lower mood prior to engagement. Therefore, the observed impact of social media use warrants further consideration in the context of psychosis and mental health and wellbeing more generally. Additionally, clinical participants were significantly less likely to use social media than non‐clinical participants. Small‐scale studies demonstrate lower social media website access by individuals with severe mental health problems than the general population A strength of the current study was the ecologically valid prospective measurement of social media use and psychological outcomes. Additionally, socialisation was included to identify whether other forms of social interaction may have predicted outcomes. Trait SR, mood, self‐esteem and paranoia were also measured at baseline and were not found to predict social media use. Therefore, although exact directions cannot be established, the inclusion of these trait measures improves confidence in conclusions regarding outcomes associated with social media use. Comparison of clinical and non‐clinical groups allowed the between‐group comparison of the impact of social media use.We decided not to actively track participants' social media use due to (i) the costs associated with the passive monitoring of accounts; (ii) concerns that seeking permission to view social media accounts would affect recruitment rates; therefore, negatively impacting the sample size; (iii) the potential for risk identification on accounts and the resulting responsibility for the researcher to report such information; (iv) the potential for participants to change their behaviours on social media if they are aware that a researcher is monitoring their profiles; (v) monitoring would only allow the identification of content posting behaviours; not content consumption; and (vi) accessing participant social media profiles would enable the identification of posts written by others on the individual's profile, who will not have provided consent for researchers to view their posts. ESM was used to reduce the likelihood of inaccurate retrospective recall due to the momentary nature of alerts; however, monitoring participants' social media access and behaviours may have produced more reliable data due to the self‐reported nature of the study. Additionally, we speculated that the detrimental impact of emotional self‐disclosures may be related to the feedback individuals received; however, without clear knowledge of user responses to posts, firm conclusions cannot be drawn. Therefore, future research should seek to identify the responses to such disclosures to understand whether response type mediates the relationship between disclosure posting and outcome. It is also possible that the information participants were presented with when viewing social media profiles and newsfeeds may have contributed towards reductions in mood when using social media. Participants were not asked to provide details about the information they saw each time they accessed social media sites. Future research should expand on these findings by exploring whether type of content viewed contributes towards the impact of use and behaviours. The sample was limited to mostly White British participants so findings are unlikely to be generalisable. Moreover, clinicians may have been more likely to refer patients who had previously described positive or negative experiences of engaging with social media. The design of the study was correlational in nature and it is likely that other factors may have also contributed towards the impact of social media use. Therefore, future research should seek to experimentally manipulate social media use to explore whether the effect is directly attributable specifically to social media use and behaviours. Finally, a diagnosis of SMI was not found to moderate the relationship between social media use and mood; however, the sample size may have not been sufficient for moderation. Therefore, future research should explore the impact of an SMI diagnosis with a larger pool of participants.Despite finding that psychosis did not moderate the impact of social media use per se, reductions in mood after social media use are likely to be more damaging for people with psychosis due to reporting lower levels of mood initially. The negative consequence of social media engagement in both groups highlights the importance of continued consideration of the impact of social media in mental health settings. Specifically, clinicians should ensure that they are aware of and explore any potential issues clients face when using social media, particularly with regard to online self‐disclosures. Additionally, the findings support the recent assertion made by the Royal Society for Public Health (2017) that social media websites should be used to discretely reach out to individuals who may be affected by content and signpost appropriate support options Appendix S1. ESM assessments.Click here for additional data file.


Recent topics about metformin mainly focus on clinical studies, briefly mentioning some new aspects on the mechanisms of metformin action. Metformin is the most prescribed oral hypoglycemic agent for the treatment of type 2 diabetes in Western countriesIt is well accepted that the antihypoglycemic actions of metformin include the reduction of gluconeogenesis in the liver and the increase of glucose uptake in skeletal muscle tissue. Recently, some interesting information has emerged that the gut might play an important role in hypoglycemic action by metforminFrom the viewpoint of clinical practice, there are many useful reports of clinical trials using metformin alone or in combination therapy with other drugs. In Japan, the recommended frequency of metformin is commonly three times or twice per day. No previous study has observed the effects of the dosing frequency of metformin on the relationship between daily blood glucose profile and plasma concentration of metformin in patients treated with this drug. A recent studyet al.2. A recent report of a community‐based cohort study supports cautious prescription of metformin for patients with type 2 diabetes and estimated glomerular filtration rate of ≥30 mL/min/1.73 m2Secondary failure of oral hypoglycemic agents during long‐term medication is a concern for patients and medical staff. A recent report by a Korean group on a prospective, multicenter, observational cohort study with 56 months of median follow‐up time showed that metformin was associated with a lower failure risk, as compared with sulfonylureas and meglitinides, from data on monotherapy in patients with type 2 diabetesThe presence of hyperglycemia, hypoglycemia and overweight is a matter of concern with pharmacotherapy in daily practice. However, these anxieties are not always easily resolved. Recently, the following four studies showed a positive outlook. In a randomized trial to compare the effects of metformin, gliclazide and liraglutide, a glucagon‐like peptide‐1 analog, on body composition in patients with both type 2 diabetes and non‐alcoholic fatty liver disease, either of these drug monotherapies for 24 weeks resulted in greater weight loss, reduction in body mass and better blood glucose control, contributing to a favorable hepatic functionThe present article on recent topics about metformin mainly focuses on clinical studies, briefly mentioning some aspects of the mechanisms of metformin actions. Although metformin is already expected to have anti‐aging effects and glucose‐lowering effects through the gut microbiome, promising future therapeutic targets of metformin are increasing, including reducing the risk of dementia‐type neurodegenerative disorders and certain cancers. All of these aspects are summarized in Figure The author declares no conflict of interest.


Mammalian glycan‐binding receptors, sometimes known as lectins, interact with glycans, the oligosaccharide portions of endogenous mammalian glycoproteins and glycolipids as well as sugars on the surfaces of microbes. These receptors guide glycoproteins out of and back into cells, facilitate communication between cells through both adhesion and signaling, and allow the innate immune system to respond quickly to viral, fungal, bacterial, and parasitic pathogens. For many of the roughly 100 glycan‐binding receptors that are known in humans, there are good descriptions of what types of glycans they bind and how selectivity for these ligands is achieved at the molecular level. In some cases, there is also comprehensive evidence for the roles that the receptors play at the cellular and organismal levels. In addition to highlighting these well‐understood paradigms for glycan‐binding receptors, this review will suggest where gaps remain in our understanding of the physiological functions that they can serve. BDCA‐2blood dendritic cell antigen 2CRDcarbohydrate‐recognition domainDCIRdendritic cell inhibitory receptorDC‐SIGNdendritic cell‐specific intercellular adhesion molecule‐grabbing nonintegrinITAMimmunotyrosine activation motifITIMimmunotyrosine inhibitory motifSRCLscavenger receptor C‐type lectinIn the nearly 50 years since the discovery of the first mammalian glycan‐binding protein, more than a hundred additional receptors have been described. For many of the receptors, it is known how they bind selected oligosaccharide structures and the current state of our molecular understanding of what types of proteins bind glycans and how they achieve selectivity has been recently assessed As discussed in detail in recent reviews, enumeration of the complement of mammalian glycan‐binding receptors rests on both biochemical studies and the availability of complete sequences of multiple mammalian genomes, which can be probed with protein sequence motifs that are associated with sugar‐binding activity, usually in modular carbohydrate‐recognition domains (CRDs) www.imperial.ac.uk/research/animallectins). In spite of these advances, identification of additional glycan‐binding receptors can be anticipated. It is particularly important to bear in mind that proteins which contain sugar‐binding domains that do not fall into the known categories of CRDs are not detected by the motif‐scanning methods. Thus, glycan‐binding proteins with novel mechanisms of sugar binding continue to emerge from biochemical, cell biology, and genetic studies. Genomic analysis also highlights the fact that the complement of glycan‐binding receptors differs significantly between different species and the technologies so far exploited mostly in humans and mice remain to be applied to most other species.In the most intensively studied human and mouse systems, the combination of genomic, binding and structural analysis has produced reliable catalogs of potential glycan‐binding receptors and has resulted in identification of novel receptors that fall into the existing structural classes  and galectins in interaction of dendritic cells and macrophages with other cells of the immune system, and Siglec‐4 (myelin‐associated glycoprotein) in maintaining the stability of layers of myelin in the nervous system Laminin provides a recent example of a new type of cell–matrix interaction involving a novel type of glycan‐binding receptor Fig. B. InteraThere are well‐defined roles for multiple classes of glycan‐binding receptors in the trafficking of glycoproteins from the endoplasmic reticulum to the cell surface and to other intracellular compartments. These have been extensively reviewed and summarized Because the quality control and sorting processes occur in the context of individual cells, it has been possible to analyze these functions in isolated cells, complemented by biochemical analysis of the activities of the various glycan‐binding receptors. Human diseases caused by mutations in glycan biosynthesis and glycan‐binding proteins, as well as knockout mice, also provide evidence for the biological significance of the sorting functions, particularly for the mannose‐phosphate receptors In addition to their importance in movement of glycoproteins through the luminal compartments, glycan‐binding receptors play additional roles inside cells. A recent novel example is recognition of damaged organelles of the vacuolar system by cytoplasmic galectins Fig.  38, 39. While the roles of glycan‐binding receptors in intracellular trafficking can be investigated largely in individual cells, the functions of receptors in glycoprotein uptake from blood and other extracellular fluids are only fully evident in the context of organisms. Multiple receptors that can mediate uptake and degradation of glycoproteins circulating in blood serve complementary functions by binding to distinct sets of glycoproteins that bear different types of glycans Fig. . These rThe mannose receptor, found on liver sinusoidal endothelial cells and Kupffer cells, the resident macrophages of the liver, as well as on other tissue macrophages and dendritic cells, has well‐defined roles in uptake and turnover of multiple different classes of glycoproteins 4‐GalNAc 4‐GalNAc and accumulate high serum levels of several lysosomal enzymes and other glycoproteins associated with inflammation and tissue damage In addition to the C‐type CRDs that bind to mannose‐containing oligosaccharides, the mannose receptor contains an R‐type CRD that binds selectively to terminal 4‐SOThe asialoglycoprotein receptor, expressed on hepatocytes in the liver, represents a second relatively well‐understood glycoprotein clearance system. Most glycoproteins in blood plasma bear complex N‐linked glycans that are capped with sialic acid, often in 2–3 linkage to galactose residues. When sialic acid residues are removed from these glycoproteins, they become ligands for the asialoglycoprotein receptor, which results in efficient uptake and degradation. The significance of this pathway in turnover of serum glycoproteins has been demonstrated in recent experiments with knockout mice that lack the asialoglycoprotein receptor 4‐GalNAc tag has been removed.Although the outlines of how the asialoglycoprotein receptor can function in glycoprotein turnover are clear, there are important aspects of the system that remain to be elucidated. In most cases, the removal of sialic acids from glycoproteins, which triggers uptake, must be catalyzed by a sialidase encountered by the glycoproteins. There is evidence that bacterial sialidases can provide this function under pathological conditions, but the role of specific endogenous sialidases in the normal physiological turnover of glycoproteins remains to be established x trisaccharide epitope, which is found on glycoproteins released from secondary granules of neutrophils A third type of clearance receptor, the scavenger receptor C‐type lectin (SRCL), is expressed on endothelial cells throughout the vasculature In spite of gaps in our knowledge of the multiple systems for clearance of glycoproteins from blood, they are already extensively exploited for targeting of glycoproteins in therapeutic contexts. Patients with Gaucher disease, a lysosomal storage disease, are now routinely treated successfully by enzyme replacement therapy, in which missing lysosomal hydrolases bearing appropriate mannose‐containing glycans are injected into the circulation for uptake into macrophages via the mannose 2+‐dependent signaling Glycan‐binding receptors embedded in the plasma membranes of cells can initiate or inhibit signaling in several different ways. In mammals, unlike in plants, there are no examples of receptors in which a cell surface CRD is linked in a single transmembrane polypeptide to a kinase domain on the cytoplasmic side of the membrane Other glycan‐binding receptors, including the C‐type lectins mincle and dectin‐2 on macrophages as well as blood dendritic cell antigen 2 (BDCA‐2) on plasmacytoid dendritic cells, lack signaling motifs but interact with the common Fc receptor γ chain Fig. B 67, 68.Interaction of glycan ligands with the CRDs in many of these signaling receptors has been described in molecular detail and the downstream intracellular signaling pathways that are activated following ligand binding are well documented. However, a key outstanding question is how information about ligand binding reaches the cytoplasmic side of the membrane. There are several reasons that conformational changes in the glycan‐binding domains are unlikely to be transmitted across the membrane. The CRDs are generally rigid and the binding sites do not change upon ligand binding One way that dimerization could initiate signaling has been suggested for dectin‐1, because engagement with β glucan brings together two receptor polypeptides to create a fully functional ITAM from the hemi‐ITAMs present in the cytoplasmic domain of each polypeptide Fig. C 73. IntGalectins interacting with glycosylated membrane receptors provide an alternative model for how glycan‐binding proteins can modulate signaling Fig. D. GalectDefining specific molecular mechanisms by which different glycan‐binding receptors link to activation of signaling pathways remains an important outstanding task, one which will potentially provide insights into how activating and inhibitory ligands can be created for therapeutic purposes.Lectins from several different structural groups are involved in pathogen binding by cells of the innate immune system, but there has been a particular proliferation of receptors that contain C‐type CRDs. These receptors distinguish self glycans from those on the surfaces of pathogens by combining several levels of recognition. The primary monosaccharide‐binding sites in many pathogen‐specific glycan‐binding receptors interact with mannose residues and often can also bind to GlcNAc Mycobacterium tuberculosisBeyond simple monosaccharide recognition, the CRDs in pathogen‐binding receptors often have extended binding sites which bind common disaccharide motifs such as Manα1‐2Man, which is a common terminal structure on mannans of yeast and other fungi In spite of this broad understanding of the principles that underlie selective recognition of pathogens, there are some important outstanding questions. Only a limited amount is known about the actual mechanism of binding to specific pathogen glycans, in part because identification of potential ligands for sugar‐binding receptors often involves glycan arrays that are populated with mammalian glycans in vitro. A further issue is the large amount of evolutionary variation between species that often makes it difficult to use mouse models to understand human biology in this context. There are examples of receptors found in humans but not in mice and others present in mice but not in humans From what is known about the binding specificity of various receptors of the innate immune system, it is clear that there is substantial redundancy, with many receptors binding to overlapping sets of target glycans. This redundancy is one complicating factor in demonstrating in whole organisms the importance of interactions observed An alternative to the use of mouse models is correlation of polymorphisms in the human receptor genes with disease or susceptibility to infection. A few well‐established examples illustrate the power of this approach. Mutations that affect the stability of the collagen‐like portion of human mannose‐binding protein, which is the initiating protein in the lectin pathway of complement fixation, result in decreased serum concentrations of the protein. This reduction in serum concentrations is in turn correlated with increased susceptibility to infections in young infants and in patients with compromised adaptive immune systems x and Lewisa trisaccharides Finally, it is important to note that there can be overlap between binding of sugars on pathogens and binding to endogenous mammalian glycans. As well as to mediating clearance of endogenous glycoproteins, the mannose receptor can serve as a pathogen receptor by virtue of its ability to bind to exposed mannose and GlcNAc residues Genomic analysis of the repertoire of potential glycan‐binding receptors and increasingly high‐throughput identification of ligands using array‐based technology, accompanied by structural and biochemical analysis, are providing comprehensive knowledge of binding specificity and mechanisms for all of the known classes of glycan‐binding receptors. These results, coupled with analysis of the receptors in cells and in whole organisms, help to define the physiological functions of these receptors, although further work is needed. The fact that the selectins and the mannose receptor have been successfully targeted for development of anti‐inflammatory therapy and for treatment of lysosomal storage diseases provides paradigms for how knowledge of the mechanisms and physiological functions of additional sugar‐binding receptors will provide a basis for additional therapeutic applications.The authors declare no conflict of interest.All authors reviewed the literature, wrote the manuscript and created the figures.