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See Ware et al. (doi:10.1093/brain/awaa166) for a scientific commentary on this article. Using a modified MRI-based method that can detect subtle, region-specific blood-brain barrier dysfunction, Veksler et al. reveal slow blood-to-brain transport in a subset of amateur American football players. MRI may allow early detection of microvascular pathology in patients at risk for chronic traumatic encephalopathy., Repetitive mild traumatic brain injury in American football players has garnered increasing public attention following reports of chronic traumatic encephalopathy, a progressive tauopathy. While the mechanisms underlying repetitive mild traumatic brain injury-induced neurodegeneration are unknown and antemortem diagnostic tests are not available, neuropathology studies suggest a pathogenic role for microvascular injury, specifically blood–brain barrier dysfunction. Thus, our main objective was to demonstrate the effectiveness of a modified dynamic contrast-enhanced MRI approach we have developed to detect impairments in brain microvascular function. To this end, we scanned 42 adult male amateur American football players and a control group comprising 27 athletes practicing a non-contact sport and 26 non-athletes. MRI scans were also performed in 51 patients with brain pathologies involving the blood–brain barrier, namely malignant brain tumours, ischaemic stroke and haemorrhagic traumatic contusion. Based on data from prolonged scans, we generated maps that visualized the permeability value for each brain voxel. Our permeability maps revealed an increase in slow blood-to-brain transport in a subset of amateur American football players, but not in sex- and age-matched controls. The increase in permeability was region specific (white matter, midbrain peduncles, red nucleus, temporal cortex) and correlated with changes in white matter, which were confirmed by diffusion tensor imaging. Additionally, increased permeability persisted for months, as seen in players who were scanned both on- and off-season. Examination of patients with brain pathologies revealed that slow tracer accumulation characterizes areas surrounding the core of injury, which frequently shows fast blood-to-brain transport. Next, we verified our method in two rodent models: rats and mice subjected to repeated mild closed-head impact injury, and rats with vascular injury inflicted by photothrombosis. In both models, slow blood-to-brain transport was observed, which correlated with neuropathological changes. Lastly, computational simulations and direct imaging of the transport of Evans blue-albumin complex in brains of rats subjected to recurrent seizures or focal cerebrovascular injury suggest that increased cellular transport underlies the observed slow blood-to-brain transport. Taken together, our findings suggest dynamic contrast-enhanced-MRI can be used to diagnose specific microvascular pathology after traumatic brain injury and other brain pathologies.

National audience; Integrating local knowledge with scientific knowledge can offer significant benefits to improving environmental decision-making. However, this is especially challenging in environmental conflict situations where advice is lacking, and no single approach can foster conflict transformation. To understand stakeholder knowledge and its integration in a conflict transformation process in Scotland, we brought together diverse stakeholder organisations and encouraged power sharing in the project’s management. Our mixed-methods approach was based on theories of community science, knowledge co-production, knowledge integration and implementation and conflict transformation. We gathered stakeholder perceptions to see where local and scientific knowledge converged and diverged. Stakeholders holding opposing views mutually prioritised knowledge gaps and identified future collaborative actions. Building upon lessons learned, we present a practical framework and associated considerations to realise knowledge integration goals in conservation conflict situations. This framework is widely applicable, especially in situations where disputes over the evidence-base prevent positive outcomes for people and nature.

Friction in sheet metal forming is largely governed by the flattening of surface asperities. Liquid lubricants can reduce flattening and friction by filling the surface valleys and carry a substantial amount of the pressure. Experimental and numerical work is presented for investigating the influence of the compressibility of the lubricant and the material strength on the deformation of a simple cylinder with an enlarged surface pocket filled with oil. Results show that deformation is not sensitive to differences in compressibility of typical lubrication oils under hydrostatic pressure conditions. This was observed independent of the material strength.

The changes in the automotive market and their effects on industry are nowadays hot topics in metal forming seminars and conferences around the world. The rise in the number of electric vehicles will inevitably lead to a decrease in the demand of components for combustion engines and power drive trains. Typical forming components such as pistons, connecting rods, valves, camshafts, crankshafts, multi-speed gear boxes and others that exist in diesel or petrol vehicles, will no longer be required. However, the lightweight construction requirements for the body-in-white of electric vehicles, the production of components for asynchronous motors and the fabrication of battery components, namely busbars, are bringing new challenges and opportunities for the metal forming industry. This chapter is focused on busbars, which are metallic strips or sheets that are utilized to distribute electric power to multiple equipment such as the electric motor, the electric power steering unit, and the AC/DC converters. In particular, the chapter addresses the challenge of replacing copper busbars by hybrid busbars made from copper and aluminium, due to the expected savings in weight and cost. For this purpose, the authors discuss the challenge of connecting copper to aluminium in hybrid busbars by means of existing joining technologies and introduce a new joining by forming process aimed at connecting hybrid busbars at room temperature without giving rise to material protrusions above and below the sheet surfaces. The effectiveness of the new process is compared against fastening by measuring the electric resistivities in both types of hybrid busbar joints. Finite element analysis gives support to the presentation and proves to be suitable for the electro-thermo-mechanical analysis of busbar connections.

This paper revisits double-sided self-pierce riveting to discuss the applicability limits related to the thickness of the sheets to be used in hidden lap joint connections. Special emphasis is given to thin sheets with thicknesses that are significantly smaller than those earlier reported in the literature. The overall methodology draws from experimental and numerical simulation to aspects related to the working principle of double-sided self-pierce riveting and geometric scalability of the chamfered tubular rivets. Results show that double-sided self-pierce riveting can be successfully applied in thin sheets and must be seen as an alternative to well-established joining processes such as conventional self-pierce riveting and resistance spot welding. In case of the latter, comparisons are made against double-sided self-pierce riveting regarding the force and energy requirements to assemble and destroy the resulting lap joints. Destruction of the joints is performed by means of shear tests and provides the maximum force and energy that both types of lap joints are capable to withstand without failure.

Tracking the migration of superparamagnetic iron oxide (SPIO)-labeled immune cells in vivo is valuable for understanding the immunogenic response to cancer and therapies. Quantitative cell tracking using TurboSPI-based R2* mapping is a promising development to improve accuracy in longitudinal studies on immune recruitment. However, off-resonance fat signal isochromats lead to modulations in the signal time-course that can be erroneously fit as R2* signal decay, overestimating the density of labeled cells, while excluding voxels with fat-typical modulations results in underestimation of cell density in voxels with mixed content. Approaches capable of accurate R2* estimation in the presence of fat are needed. We propose a dual-decay (separate R2f* and R2w* for fat and water) Dixon-based signal model that accounts for the presence of fat in a voxel to provide better estimates of SPIO-induced dephasing. This model was tested in silico, in phantoms with varying quantities of fat and SPIO-labeled cells, and in 5 mice injected with SPIO-labeled CD8+ T cells. In silico single voxel simulations illustrate how the proposed dual-decay model provides stable R2w* estimates that are invariant to fat content. The proposed model outperforms previous methods when applied to in vitro samples of SPIO-labeled cells and oil prepared with oil content ≥ 15%. Preliminary in vivo results show that, compared to previous methods, the dual-decay model improves the balance of R2* mapping in fat-dense areas, which will yield more reliable analysis in future cell tracking studies. The proposed model is a promising tool for quantitative TurboSPI R2* cell tracking, with further refinements offering the possibility of better specificity and sensitivity.

The COST Action ‘European Raptor Biomonitoring Facility’ (ERBFacility) aims to develop pan-European raptor biomonitoring in support of better chemicals management in Europe, using raptors as sentinel species. This presents a significant challenge involving a range of constraints that must be identified and addressed. The aims of this study were to: (1) carry out a comprehensive review of the constraints that may limit the gathering in the field of raptor samples and contextual data, and assess their relative importance across Europe; and (2) identify and discuss possible solutions to the key constraints that were identified. We applied a participatory approach to identify constraints and to discuss feasible solutions. Thirty-one constraints were identified, which were divided into four categories: legal, methodological, spatial coverage, and skills constraints. To assess the importance of the constraints and their possible solutions, we collected information through scientific workshops and by distributing a questionnaire to stakeholders in all the countries involved in ERBFacility. We obtained 74 answers to the questionnaire, from 24 of the 39 COST participating countries. The most important constraints identified were related to the collection of complex contextual data about sources of contamination, and the low number of existing raptor population national/regional monitoring schemes and ecological studies that could provide raptor samples. Legal constraints, such as permits to allow the collection of invasive samples, and skills constraints, such as the lack of expertise to practice necropsies, were also highlighted. Here, we present solutions for all the constraints identified, thus suggesting the feasibility of establishing a long-term European Raptor Sampling Programme as a key element of the planned European Raptor Biomonitoring Facility., This paper is based on work from COST Action European Raptor Biomonitoring Facility (COST Action CA16224) supported by COST (European Cooperation in Science and Technology), including a grant for a short-term scientific mission awarded to the lead author. COST is funded by the Horizon 2020 Framework Programme of the European Union. Silvia Espín was financially supported by Ministerio de Ciencia, Innovación y Universidades (Juan de la Cierva-Incorporación postdoctoral contract, IJCI-2017-34653).

MalariaGEN is a data-sharing network that enables groups around the world to work together on the genomic epidemiology of malaria. Here we describe a new release of curated genome variation data on 7,000 Plasmodium falciparum samples from MalariaGEN partner studies in 28 malaria-endemic countries. High-quality genotype calls on 3 million single nucleotide polymorphisms (SNPs) and short indels were produced using a standardised analysis pipeline. Copy number variants associated with drug resistance and structural variants that cause failure of rapid diagnostic tests were also analysed. Almost all samples showed genetic evidence of resistance to at least one antimalarial drug, and some samples from Southeast Asia carried markers of resistance to six commonly-used drugs. Genes expressed during the mosquito stage of the parasite life-cycle are prominent among loci that show strong geographic differentiation. By continuing to enlarge this open data resource we aim to facilitate research into the evolutionary processes affecting malaria control and to accelerate development of the surveillance toolkit required for malaria elimination.

Most psychiatric disorders emerge in the second decade of life. In the present study, we examined whether environmental adversity, developmental antecedents, major depressive disorder, and functional impairment correlate with deviation from normative brain development in adolescence.We trained a brain age prediction model using 189 structural magnetic resonance imaging brain features in 1299 typically developing adolescents (age range 9-19 years, mean = 13.5, SD = 3.04), validated the model in a holdout set of 322 adolescents (mean = 13.5, SD = 3.07), and used it to predict age in an independent risk-enriched cohort of 150 adolescents (mean = 13.6, SD = 2.82). We tested associations between the brain age gap and adversity, early antecedents, depression, and functional impairment.We accurately predicted chronological age in typically developing adolescents (mean absolute error = 1.53 years). The model generalized to the validation set (mean absolute error = 1.55 years, 1.98 bias adjusted) and to the independent at-risk sample (mean absolute error = 1.49 years, 1.86 bias adjusted). The brain age estimate was reliable in repeated scans (intraclass correlation = 0.94). Experience of environmental adversity (β = 0.18; 95% CI, 0.04 to 0.31; p = .02), diagnosis of major depressive disorder (β = 0.61; 95% CI, 0.23 to 0.99; p = .01), and functional impairment (β = 0.16; 95% CI, 0.05 to 0.27; p = .01) were associated with a positive brain age gap.Risk factors, diagnosis, and impact of mental illness are associated with an older-appearing brain during development.

A multidisciplinary design optimization (MDO) strategy for the preliminary design of a sailplane has been developed. The proposed approach applies MDO techniques and multi-fidelity analysis methods which have seen successful use in many aerospace design applications. A customized genetic algorithm (GA) was developed to control the sailplane optimization that included aerodynamics/stability, structures/weights and balance and, performance/airworthiness disciplinary analysis modules. An adaptive meshing routine was developed to allow for accurate modeling of the aero structural couplinginvolved in wing design, which included a finite element method (FEM) structural solver along with a vortex lattice aerodynamics solver. Empirical equations were used to evaluate basic sailplane performance and airworthiness requirements. This research yielded an optimum design that correlated well with an existing high performance sailplane. The results of this thesis suggest that preliminary sailplane design is a well suited application for modern optimization techniques when coupled with, multi-fidelity analysis methods.

Patterns of energy and available moisture can vary over small (

Resumen del póster presentado al SETAC Europe 31st Annual Meeting, celebrado de forma virtual del 3 al 6 de mayo de 2021., As a key part of the European Green Deal, the EU Chemicals Strategy aims for a non-toxic environment. A key challenge in this respect is to apply biomonitoring data in toxicological and ecotoxicological risk assessment to inform better management of chemicals in Europe. Raptors (birds of prey, owls and falcons) are widely used as sentinel species in monitoring programs and are particularly well-suited to contaminant monitoring. Delivering relevant contaminant data at pan-European scale requires the development of a pan-European network that brings together fieldworkers, sample collections and analytical laboratories, and of frameworks for the gathering, storage and analysis of raptor samples, together with related capacity-building and sharing of best practice. This challenge is being addressed by the COST Action European Raptor Biomonitoring Facility (https://erbfacility.eu/) building on the precursor Networking Programme Research and Monitoring for and with Raptors in Europe (EURAPMON; http://www.eurapmon.net/). This poster introduces a schematic sampling protocol for contaminant monitoring in raptors. The protocol provides best-practice guidance in a format accessible to both professionals and volunteers. The protocol aims to enhance sampling capabilities across Europe, ensure appropriate quality of samples and facilitate harmonization of procedures to maximize the reliability, comparability and interoperability of resulting contaminant data. The protocol starts with each matrix type, including sample types that are collected both during active (samples from captured birds and monitored nests) and passive (samples from dead birds and deserted nests) sampling. For each sample type (whole blood, plasma, serum, deserted or addled eggs, feathers, preen oil, regurgitated pellets, prey remains, gastric content and internal tissues), a specific protocol offering additional information is provided (e.g., collecting procedure, volume/mass of sample needed for contaminant monitoring, container types to conserve the sample, transport and storage conditions). The full protocol can be found online (https://doi.org/10.1007/s13280-020-01341-9)., European Raptor Biomonitoring Facility COST Action (CA16224) is supported by COST (European Cooperation in Science and Technology). Silvia Espín is supported by Ministerio de Ciencia, Innovación y Universidades (IJCI-2017-34653).

We aim to extend the use of image quality metrics (IQMs) from static magnetic resonance imaging (MRI) applications to dynamic MRI studies. We assessed the use of 2 IQMs, the root mean square error and structural similarity index, in evaluating the reconstruction of quantitative dynamic contrast-enhanced (DCE) MRI data acquired using golden-angle sampling and compressed sensing (CS). To address the difficulty of obtaining ground-truth knowledge of parameters describing dynamics in real patient data, we developed a Matlab simulation framework to assess quantitative CS-DCE-MRI. We began by validating the response of each IQM to the CS-MRI reconstruction process using static data and the performance of our simulation framework with simple dynamic data. We then extended the simulations to the more realistic extended Tofts model. When assessing the Tofts model, we tested 4 different methods of selecting a reference image for the IQMs. Results from the retrospective static CS-MRI reconstructions showed that each IQM is responsive to the CS-MRI reconstruction process. Simulations of a simple contrast evolution model validated the performance of our framework. Despite the complexity of the Tofts model, both IQM scores correlated well with the recovery accuracy of a central model parameter for all reference cases studied. This finding may form the basis of algorithms for automated selection of image reconstruction aspects, such as temporal resolution, in golden-angle-sampled CS-DCE-MRI. These further suggest that objective measures of image quality may find use in general dynamic MRI applications.

Characterisation of model asperity flattening under longitudinal subsurface elongation is presented with a variety of normal pressure, flank angle and strain hardening behaviour. A systematic investigation outlines an optimal specimen design to ensure a homogenous deformation field in the workpiece under stress-strain conditions, which are typical in sheet metal forming. Asperity flattening increases with normal pressure, and the longitudinal subsurface strain reduces the necessary yield pressure and promotes asperity flattening. When the flank angle is increased, the longitudinal stress component due to elongation gets smaller in the asperity, reducing the effect of longitudinal straining. Strain hardening decreases the asperity flattening rate as strain hardening extends the deformation field further into the underlying material, which results in less asperity flattening.

Introduction A new generation of large‐scale studies is using neuroimaging to investigate adolescent brain development across health and disease. However, imaging artifacts such as head motion remain a challenge and may be exacerbated in pediatric clinical samples. In this study, we assessed the scan–rescan reliability of multimodal MRI in a sample of youth enriched for risk of mental illness. Methods We obtained repeated MRI scans, an average of 2.7 ± 1.4 weeks apart, from 50 youth (mean age 14.7 years, SD = 4.4). Half of the sample (52%) had a diagnosis of an anxiety disorder; 22% had attention‐deficit/hyperactivity disorder (ADHD). We quantified reliability with the test–retest intraclass correlation coefficient (ICC). Results Gray matter measurements were highly reliable with mean ICCs as follows: cortical volume (ICC = 0.90), cortical surface area (ICC = 0.89), cortical thickness (ICC = 0.82), and local gyrification index (ICC = 0.85). White matter volume reliability was excellent (ICC = 0.98). Diffusion tensor imaging (DTI) components were also highly reliable. Fractional anisotropy was most consistently measured (ICC = 0.88), followed by radial diffusivity (ICC = 0.84), mean diffusivity (ICC = 0.81), and axial diffusivity (ICC = 0.78). We also observed regional variability in reconstruction, with some brain structures less reliably reconstructed than others. Conclusions Overall, we showed that developmental MRI measures are highly reliable, even in youth at risk for mental illness and those already affected by anxiety and neurodevelopmental disorders. Yet, caution is warranted if patterns of results cluster within regions of lower reliability., Developmental MRI measures are highly reliable, even in youth at risk for mental illness and those already affected by anxiety and neurodevelopmental disorders. Yet, caution is warranted if patterns of results cluster within regions of lower reliability.

ObjectivesTo examine the association between blood-brain barrier (BBB) integrity, brain volume and cognitive dysfunction in adult patients with systemic lupus erythematosus (SLE).MethodsA total of 65 ambulatory patients with SLE and 9 healthy controls underwent dynamic contrast-enhanced MRI scanning, for quantitative assessment of BBB permeability. Volumetric data were extracted using the VolBrain pipeline. Global cognitive function was evaluated using a screening battery consisting of tasks falling into five broad cognitive domains, and was compared between patients with normal versus extensive BBB leakage.ResultsPatients with SLE had significantly higher levels of BBB leakage compared with controls (p=0.04). Extensive BBB leakage (affecting over >9% of brain volume) was identified only in patients with SLE (16/65; 24.6%), who also had smaller right and left cerebral grey matter volumes compared with controls (p=0.04). Extensive BBB leakage was associated with lower global cognitive scores (p=0.02), and with the presence of impairment on one or more cognitive tasks (p=0.01).ConclusionOur findings provide evidence for a link between extensive BBB leakage and changes in both brain structure and cognitive function in patients with SLE. Future studies should investigate the mechanisms underlying BBB-mediated cognitive impairment, validate the diagnostic utility of BBB imaging, and determine the potential of targeting the BBB as a therapeutic strategy in patients with SLE.

Multiparametric MRI (mp-MRI) is a widely used tool for diagnosing and staging prostate cancer. The purpose of this study was to evaluate whether transfer learning, unsupervised pre-training and test-time augmentation significantly improved the performance of a convolutional neural network (CNN) for pixel-by-pixel prediction of cancer vs. non-cancer using mp-MRI datasets.154 subjects undergoing mp-MRI were prospectively recruited, 16 of whom subsequently underwent radical prostatectomy. Logistic regression, random forest and CNN models were trained on mp-MRI data using histopathology as the gold standard. Transfer learning, unsupervised pre-training and test-time augmentation were used to boost CNN performance. Models were evaluated using Dice score and area under the receiver operating curve (AUROC) with leave-one-subject-out cross validation. Permutation feature importance testing was performed to evaluate the relative value of each MR contrast to CNN model performance. Statistical significance (p0.05) was determined using the paired Wilcoxon signed rank test with Benjamini-Hochberg correction for multiple comparisons.Baseline CNN outperformed logistic regression and random forest models. Transfer learning and unsupervised pre-training did not significantly improve CNN performance over baseline; however, test-time augmentation resulted in significantly higher Dice scores over both baseline CNN and CNN plus either of transfer learning or unsupervised pre-training. The best performing model was CNN with transfer learning and test-time augmentation (Dice score of 0.59 and AUROC of 0.93). The most important contrast was apparent diffusion coefficient (ADC), followed by Ktrans and T2, although each contributed significantly to classifier performance.The addition of transfer learning and test-time augmentation resulted in significant improvement in CNN segmentation performance in a small set of prostate cancer mp-MRI data. Results suggest that these techniques may be more broadly useful for the optimization of deep learning algorithms applied to the problem of semantic segmentation in biomedical image datasets. However, further work is needed to improve the generalizability of the specific model presented herein.

Highlights • A sub-group of bipolar patients have extensive blood-brain barrier leakage • These patients have a more chronic form of illness, greater depression and anxiety • All patients with extensive blood-brain barrier leakage are also insulin resistant, Bipolar disorder affects approximately 2% of the population and is typically characterized by recurrent episodes of mania and depression. While some patients achieve remission using mood-stabilizing treatments, a significant proportion of patients show progressive changes in symptomatology over time. Bipolar progression is diverse in nature and may include a treatment-resistant increase in the frequency and severity of episodes, worse psychiatric and functional outcomes, and a greater risk of suicide. The mechanisms underlying bipolar disorder progression remain poorly understood and there are currently no biomarkers for identifying patients at risk. The objective of this study was to explore the potential of blood-brain barrier (BBB) imaging as such a biomarker, by acquiring the first imaging data of BBB leakage in bipolar patients, and evaluating the potential association between BBB dysfunction and bipolar symptoms. To this end, a cohort of 36 bipolar patients was recruited through the Mood Disorders Clinic (Nova Scotia Health Authority, Canada). All patients, along with 14 control subjects (matched for sex, age and metabolic status), underwent contrast-enhanced dynamic MRI scanning for quantitative assessment of BBB leakage as well as clinical and psychiatric evaluations. Outlier analysis has identified a group of 10 subjects with significantly higher percentages of brain volume with BBB leakage (labeled the “extensive BBB leakage” group). This group consisted exclusively of bipolar patients, while the “normal BBB leakage” group included the entire control cohort and the remaining 26 bipolar subjects. Among the bipolar cohort, patients with extensive BBB leakage were found to have more severe depression and anxiety, and a more chronic course of illness. Furthermore, all bipolar patients within this group were also found to have co-morbid insulin resistance, suggesting that insulin resistance may increase the risk of BBB dysfunction in bipolar patients. Our findings demonstrate a clear link between BBB leakage and greater psychiatric morbidity in bipolar patients and highlight the potential of BBB imaging as a mechanism-based biomarker for bipolar disorder progression.