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Background and objectiveBlood-based biomarkers represent a promising approach to help identify early Alzheimer’s disease (AD). Previous research has applied traditional machine learning (ML) to analyze plasma omics data and search for potential biomarkers, but the most modern ML methods based on deep learning has however been scarcely explored. In the current study, we aim to harness the power of state-of-the-art deep learning neural networks (NNs) to identify plasma proteins that predict amyloid, tau, and neurodegeneration (AT[N]) pathologies in AD.MethodsWe measured 3,635 proteins using SOMAscan in 881 participants from the European Medical Information Framework for AD Multimodal Biomarker Discovery study (EMIF-AD MBD). Participants underwent measurements of brain amyloid β (Aβ) burden, phosphorylated tau (p-tau) burden, and total tau (t-tau) burden to determine their AT(N) statuses. We ranked proteins by their association with Aβ, p-tau, t-tau, and AT(N), and fed the top 100 proteins along with age and apolipoprotein E (APOE) status into NN classifiers as input features to predict these four outcomes relevant to AD. We compared NN performance of using proteins, age, and APOE genotype with performance of using age and APOE status alone to identify protein panels that optimally improved the prediction over these main risk factors. Proteins that improved the prediction for each outcome were aggregated and nominated for pathway enrichment and protein–protein interaction enrichment analysis.ResultsAge and APOE alone predicted Aβ, p-tau, t-tau, and AT(N) burden with area under the curve (AUC) scores of 0.748, 0.662, 0.710, and 0.795. The addition of proteins significantly improved AUCs to 0.782, 0.674, 0.734, and 0.831, respectively. The identified proteins were enriched in five clusters of AD-associated pathways including human immunodeficiency virus 1 infection, p53 signaling pathway, and phosphoinositide-3-kinase–protein kinase B/Akt signaling pathway.ConclusionCombined with age and APOE genotype, the proteins identified have the potential to serve as blood-based biomarkers for AD and await validation in future studies. While the NNs did not achieve better scores than the support vector machine model used in our previous study, their performances were likely limited by small sample size.

Objectives To evaluate the utility of a transseptal needle for balloon-assisted sharp recanalization of chronically occluded central venous structures. Background Chronically occluded central veins are not an uncommon problem, which may arise due to a plethora of reasons. Traditionally, wire and catheter techniques are often used first in an attempt to reestablish flow. When these methods fail, more aggressive techniques are employed, such as sharp recanalization using the back end of wires, Teflon-coated wires, or Rosch–Uchida or Colapinto needles. However, utilization of transseptal needles, traditionally reserved for cardiac procedures, has rarely been described. Methods Transseptal needle was utilized for balloon-assisted sharp recanalization after traditional wire and catheter techniques failed in revascularization of chronically occluded iliac veins. Results Transseptal needle was utilized successfully in two cases in revascularization of chronically occluded central veins. Conclusion Transseptal needle is a viable tool to add to the interventional radiologists’ armamentarium in reestablishing flow in chronically occluded central veins.

Abstract Rapid mapping of event landslides is crucial to identify the areas affected by damages as well as for effective disaster response. Traditionally, such maps are generated with visual interpretation of remote sensing imagery (manned/unmanned airborne systems or spaceborne sensors) and/or using pixel-based and object-based methods exploiting data-intensive machine learning algorithms. Recent works have explored the use of convolutional neural networks (CNN), a deep learning algorithm, for mapping landslides from remote sensing data. These methods follow a standard supervised learning workflow that involves training a model using a landslide inventory covering a relatively small area. The trained model is then used to predict landslides in the surrounding regions. Here, we propose a new strategy, i.e., a progressive CNN training relying on combined inventories to build a generalized model that can be applied directly to a new, unexplored area. We first prove the effectiveness of CNNs by training and validating on event landslides inventories in four regions after earthquakes and/or extreme meteorological events. Next, we use the trained CNNs to map landslides triggered by new events spread across different geographic regions. We found that CNNs trained on a combination of inventories have a better generalization performance, with a bias towards high precision and low recall scores. In our tests, the combined training model achieved the highest (Matthews correlation coefficient) MCC score of 0.69 when mapping landslides in new unseen regions. The mapping was done on images from different optical sensors, resampled to a spatial resolution of 6 m, 10 m, and 30 m. Despite a slightly reduced performance, the main advantage of combined training is to overcome the requirement of a local inventory for training a new deep learning model. This implementation can facilitate automated pipelines providing fast response for the generation of landslide maps in the post-disaster phase. In this study, the study areas were selected from seismically active zones with a high hydrological hazard distribution and vegetation coverage. Hence, future works should also include regions from less vegetated geographic locations.

Abstract Background Major depression (MD) is the most prevalent psychiatric disease in the population and is considered a prodromal stage of the Alzheimer’s disease (AD). Despite both diseases having a robust genetic component, the common transcriptomic signature remains unknown. Methods We investigated the cognitive and emotional behavioural responses in 3- and 6-month-old APP/PSEN1-Tg mice, before β-amyloid plaques were detected. We studied the genetic and pathway deregulation in the prefrontal cortex, striatum, hippocampus and amygdala of mice at both ages, using transcriptomic and functional data analysis. Results We found that depressive-like and anxiety-like behaviours, as well as memory impairments, are already present at 3-month-old APP/PSEN1-Tg mutant mice together with the deregulation of several genes, such as Ciart, Grin3b, Nr1d1 and Mc4r, and other genes including components of the circadian rhythms, electron transport chain and neurotransmission in all brain areas. Extending these results to human data performing GSEA analysis using DisGeNET database, it provides translational support for common deregulated gene sets related to MD and AD. Conclusions The present study sheds light on the shared genetic bases between MD and AD, based on a comprehensive characterization from the behavioural to transcriptomic level. These findings suggest that late MD could be an early manifestation of AD.

Abstract The lack of translation from basic research into new medicines is a major challenge in CNS drug development. The need to use novel approaches relying on (i) patient clustering based on neurobiology irrespective to symptomatology and (ii) quantitative biomarkers focusing on evolutionarily preserved neurobiological systems allowing back-translation from clinical to nonclinical research has been highlighted. Here we sought to evaluate the mismatch negativity (MMN) response in schizophrenic (SZ) patients, Alzheimer’s disease (AD) patients, and age-matched healthy controls. To evaluate back-translation of the MMN response, we developed EEG-based procedures allowing the measurement of MMN-like responses in a rat model of schizophrenia and a mouse model of AD. Our results indicate a significant MMN attenuation in SZ but not in AD patients. Consistently with the clinical findings, we observed a significant attenuation of deviance detection (~104.7%) in rats subchronically exposed to phencyclidine, while no change was observed in APP/PS1 transgenic mice when compared to wild type. This study provides new insight into the cross-disease evaluation of the MMN response. Our findings suggest further investigations to support the identification of neurobehavioral subtypes that may help patients clustering for precision medicine intervention. Furthermore, we provide evidence that MMN could be used as a quantitative/objective efficacy biomarker during both preclinical and clinical stages of SZ drug development.

Abstract Background People with neurodegenerative disorders show diverse clinical syndromes, genetic heterogeneity, and distinct brain pathological changes, but studies report overlap between these features. DNA methylation (DNAm) provides a way to explore this overlap and heterogeneity as it is determined by the combined effects of genetic variation and the environment. In this study, we aim to identify shared blood DNAm differences between controls and people with Alzheimer’s disease, amyotrophic lateral sclerosis, and Parkinson’s disease. Results We use a mixed-linear model method (MOMENT) that accounts for the effect of (un)known confounders, to test for the association of each DNAm site with each disorder. While only three probes are found to be genome-wide significant in each MOMENT association analysis of amyotrophic lateral sclerosis and Parkinson’s disease (and none with Alzheimer’s disease), a fixed-effects meta-analysis of the three disorders results in 12 genome-wide significant differentially methylated positions. Predicted immune cell-type proportions are disrupted across all neurodegenerative disorders. Protein inflammatory markers are correlated with profile sum-scores derived from disease-associated immune cell-type proportions in a healthy aging cohort. In contrast, they are not correlated with MOMENT DNAm-derived profile sum-scores, calculated using effect sizes of the 12 differentially methylated positions as weights. Conclusions We identify shared differentially methylated positions in whole blood between neurodegenerative disorders that point to shared pathogenic mechanisms. These shared differentially methylated positions may reflect causes or consequences of disease, but they are unlikely to reflect cell-type proportion differences.

BackgroundPrevious studies have shown that national cultural traits, such as collectivism–individualism and tightness–looseness, are associated with COVID-19 infection and mortality rates. However, although East Asian countries have outperformed other countries in containing COVID-19 infections and lowering mortality in the first pandemic waves, no studies to date have examined flexibility-monumentalism, a cultural trait that uniquely distinguishes East Asia from the rest of the world. Moreover, none of the previous studies have explored mechanisms underpinning the association between national culture and COVID-19 mortality.AimsOur study fills in these gaps by examining the association between flexibility-monumentalism and COVID-19 mortality, adjusting for important covariates and by analyzing mask wearing and fear of COVID-19 during the first weeks of the pandemic as plausible mechanisms underpinning this association.MethodsWe constructed and analyzed a dataset including 37 countries that have valid information on flexibility-monumentalism, COVID-19 deaths as of 31 October 2020 (before the start of vaccination campaigns), and relevant covariates including two other national cultural traits (individualism–collectivism and tightness–looseness) and other national characteristics (economic, political, demographic and health). Multiple linear regression with heteroscedasticity-consistent standard errors was used to assess the independent effect of flexibility-monumentalism on COVID-19 mortality. Mediation was assessed by examining the indirect effects of flexibility through mask wearing and fear of COVID-19 and determining the statistical significance through bootstrapping. Graphical and delete-one analysis was used to assess the robustness of the results.ResultsWe found that flexibility was associated with a significant reduction in COVID-19 mortality as of 31 October 2020, independent of level of democracy, per capita GDP, urbanization, population density, supply of hospital beds, and median age of the population. This association with mortality is stronger and more robust than for two other prominent national cultural traits (individualism–collectivism and tightness–looseness). We also found tentative evidence that the effect of flexibility on COVID-19 mortality may be partially mediated through mask wearing in the first weeks of the pandemic.

ObjectiveTo estimate the pooled odds ratio of endometriosis and irritable bowel syndrome, and to estimate the pooled prevalence of irritable bowel syndrome in patients with endometriosis.Data sourcesUsing Cochrane Library, MEDLINE, Science Direct, ClinicalTrials.gov, Web of Science, and CINAHL, we conducted a systematic literature search through October 2021, using the key terms “endometriosis” and “irritable bowel syndrome.” Articles had to be published in English or Spanish. No restriction on geographical location was applied.Methods of study selectionThe following eligibility criteria were applied: full-text original articles; human studies; studies that investigated the association between endometriosis and irritable bowel syndrome. Two investigators screened and reviewed the studies. A total of 1,776 studies were identified in 6 separate databases. After screening and applying the eligibility criteria, a total of 17 studies were included for analyses. The meta-analysis of association between endometriosis and irritable bowel syndrome included 11 studies, and the meta-analysis on the prevalence of irritable bowel syndrome in endometriosis included 6 studies.Tabulation, integration, and resultsOverall 96,119 subjects were included in the main meta-analysis (11 studies) for endometriosis and irritable bowel syndrome, with 18,887 endometriosis patients and 77,171 controls. The odds of irritable bowel syndrome were approximately 3 times higher among patients with endometriosis compared with healthy controls (odds ratio 2.97; 95% confidence interval, 2.17 – 4.06). Similar results were obtained after subgroup analyses by endometriosis diagnosis, irritable bowel syndrome diagnostic criteria, and Newcastle-Ottawa Scale scores. Six studies reported prevalence rates of irritable bowel syndrome in women with endometriosis, ranging from 10.6 to 52%. The pooled prevalence of irritable bowel syndrome in women with endometriosis was 23.4% (95% confidence interval, 9.7 – 37.2).ConclusionPatients with endometriosis have an approximately threefold increased risk of developing irritable bowel syndrome. Development and recent update of Rome criteria has evolved the diagnosis of IBS, potential bias should still be considered as there are no specific tests available for diagnosis.Systematic Review Registration[https://www.crd.york.ac.uk/prospero/displa y_record.php?ID=CRD42018080611], identifier [CRD42018080611].

I artiklen diskuteres samspillet mellem uddannelse, studerende og aftagermarked i et projektorienteret praktikforløb på universitetet. Denne undersøgelse tager afsæt i en dokumentanalyse og et fokusgruppeinterview foretaget på kandidatuddannelsen i Idrætsvidenskab ved Aarhus Universitet. Analysen viser god overensstemmelse mellem den viden, de færdigheder og kompetencer (VFK) som de studerende anvender og som praktikpladserne efterspørger. Dog bør der sondres mellem generel VFK, der er fundamental i løsningen af virkelighedsnære arbejdsopgaver, og idrætsspecifik VFK, der værdsættes af praktikvejledere, men ikke opfattes som lige nødvendig på alle arbejdspladser. For at opnå gennemskuelige og afstemte forløb opfordres til øget fokus på dialog og involvering af praktikvejledere, og forslag gives til hvordan strukturelle forhold kan understøtte dette. Når teoretisk kernefaglighed på campus konverteres til nøje struktureret opgaveløsning i praksis, påpeges imidlertid en risiko for umyndiggørelse af de studerende og en desavouering af akademia. Denne må holdes for øje i fremadrettet af- og udvikling af projektorienterede praktikforløb på universiteterne.

Abstract Background Both serotonergic signalling disruption and systemic inflammation have been associated with the pathogenesis of Alzheimer’s disease (AD). The common denominator linking the two is the catabolism of the essential amino acid, tryptophan. Metabolism via tryptophan hydroxylase results in serotonin synthesis, whilst metabolism via indoleamine 2,3-dioxygenase (IDO) results in kynurenine and its downstream derivatives. IDO is reported to be activated in times of host systemic inflammation and therefore is thought to influence both pathways. To investigate metabolic alterations in AD, a large-scale metabolic phenotyping study was conducted on both urine and serum samples collected from a multi-centre clinical cohort, consisting of individuals clinically diagnosed with AD, mild cognitive impairment (MCI) and age-matched controls. Methods Metabolic phenotyping was applied to both urine (n = 560) and serum (n = 354) from the European-wide AddNeuroMed/Dementia Case Register (DCR) biobank repositories. Metabolite data were subsequently interrogated for inter-group differences; influence of gender and age; comparisons between two subgroups of MCI - versus those who remained cognitively stable at follow-up visits (sMCI); and those who underwent further cognitive decline (cMCI); and the impact of selective serotonin reuptake inhibitor (SSRI) medication on metabolite concentrations. Results Results revealed significantly lower metabolite concentrations of tryptophan pathway metabolites in the AD group: serotonin (urine, serum), 5-hydroxyindoleacetic acid (urine), kynurenine (serum), kynurenic acid (urine), tryptophan (urine, serum), xanthurenic acid (urine, serum), and kynurenine/tryptophan ratio (urine). For each listed metabolite, a decreasing trend in concentrations was observed in-line with clinical diagnosis: control > MCI > AD. There were no significant differences in the two MCI subgroups whilst SSRI medication status influenced observations in serum, but not urine. Conclusions Urine and serum serotonin concentrations were found to be significantly lower in AD compared with controls, suggesting the bioavailability of the neurotransmitter may be altered in the disease. A significant increase in the kynurenine/tryptophan ratio suggests that this may be a result of a shift to the kynurenine metabolic route due to increased IDO activity, potentially as a result of systemic inflammation. Modulation of the pathways could help improve serotonin bioavailability and signalling in AD patients.

This paper describes the design, characterization, and testing of a compact hydrophone capable of measuring acoustic signals from cardiomyocytes. Our hydrophone consists of a nanofabricated photonic-crystal diaphragm externally-mounted to the facet of an optical fiber to form a pressure-sensitive Fabry-Pérot cavity. Our hydrophone can operate in small liquid volumes less than 5 mm deep and incorporates a microchannel to vent air during immersion. The venting channel is designed to optimize bandwidth and sensitivity. Modeling and experimental results in water show a bandwidth from 50 Hz to 18 kHz and a minimum detectable pressure of $3~\mu Pa/ \sqrt {Hz}$ . We demonstrate the sensitivity to simulated bio-acoustic sources with nanometer-scale displacements.

Abstract Finding early disease markers using non-invasive and widely available methods is essential to develop a successful therapy for Alzheimer’s Disease. Few studies to date have examined urine, the most readily available biofluid. Here we report the largest study to date using comprehensive metabolic phenotyping platforms (NMR spectroscopy and UHPLC-MS) to probe the urinary metabolome in-depth in people with Alzheimer’s Disease and Mild Cognitive Impairment. Feature reduction was performed using metabolomic Quantitative Trait Loci, resulting in the list of metabolites associated with the genetic variants. This approach helps accuracy in identification of disease states and provides a route to a plausible mechanistic link to pathological processes. Using these mQTLs we built a Random Forests model, which not only correctly discriminates between people with Alzheimer’s Disease and age-matched controls, but also between individuals with Mild Cognitive Impairment who were later diagnosed with Alzheimer’s Disease and those who were not. Further annotation of top-ranking metabolic features nominated by the trained model revealed the involvement of cholesterol-derived metabolites and small-molecules that were linked to Alzheimer’s pathology in previous studies.

In this study, the authors present a global map of rockfalls on the lunar surface and determine impact events as short- and long-term driver for rockfall events.

Chimeric antigen receptor T cells (CAR-T) targeting CD19 or B cell maturation antigen (BCMA) are highly effective against B cell malignancies. However, application of CAR-T to less differentially expressed targets remains a challenge due to lack of tumor-specific antigens and CAR-T controllability. CD123, a highly promising leukemia target, is expressed not only by leukemic and leukemia-initiating cells, but also by myeloid, hematopoietic progenitor, and certain endothelial cells. Thus, CAR-T lacking fine-tuned control mechanisms pose a high toxicity risk. To extend the CAR-T target landscape and widen the therapeutic window, we adapted our rapidly switchable universal CAR-T platform (UniCAR) to target CD123. UniCAR-T efficiently eradicated CD123+ leukemia in vitro and in vivo. Activation, cytolytic response, and cytokine release were strictly dependent on the presence of the CD123-specific targeting module (TM123) with comparable efficacy to CD123-specific CAR-T in vitro. We further demonstrated a pre-clinical proof of concept for the safety-switch mechanism using a hematotoxicity mouse model wherein TM123-redirected UniCAR-T showed reversible toxicity toward hematopoietic cells compared to CD123 CAR-T. In conclusion, UniCAR-T maintain full anti-leukemic efficacy, while ensuring rapid controllability to improve safety and versatility of CD123-directed immunotherapy. The safety and efficacy of UniCAR-T in combination with TM123 will now be assessed in a phase I clinical trial (ClinicalTrials.gov: NCT04230265).

Abstract We use multitemporal analyses based on Synthetic Aperture Radar differential interferometry (DInSAR) to study the slope adjacent to the large Punatsangchhu-I hydropower plant, a concrete gravity dam under construction in Bhutan since 2009. Several slope failures affected the site since 2013, probably as a consequence of toe undercutting of a previously unrecognised active landslide. Our results indicate that downslope displacement, likely related to the natural instability, was already visible in 2007 on various sectors of the entire valley flank. Moreover, the area with active displacements impinging on the dam site has continuously increased in size since 2007 and into 2018, even though stabilization measures have been implemented since 2013. Stabilisation measures currently only focus on a small portion of the slope, however, the unstable area is larger than previously evaluated. Highly damaged rock is present across many areas of the entire valley flank, indicating that the volumes involved may be orders of magnitude higher than the area on which stabilisation efforts have been concentrated after the 2013 failure. The results highlight that satellite-based DInSAR could be systematically used to support decision making processes in the different phases of a complex hydropower project, from the feasibility study, to the dam site selection and construction phase.

Abstract Background Aggregation of amyloid β into plaques in the brain is one of the earliest pathological events in Alzheimer’s disease (AD). The exact pathophysiology leading to dementia is still uncertain, but the apolipoprotein E (APOE) ε4 genotype plays a major role. We aimed to identify the molecular pathways associated with amyloid β aggregation using cerebrospinal fluid (CSF) proteomics and to study the potential modifying effects of APOE ε4 genotype. Methods We tested 243 proteins and protein fragments in CSF comparing 193 subjects with AD across the cognitive spectrum (65% APOE ε4 carriers, average age 75 ± 7 years) against 60 controls with normal CSF amyloid β, normal cognition, and no APOE ε4 allele (average age 75 ± 6 years). Results One hundred twenty-nine proteins (53%) were associated with aggregated amyloid β. APOE ε4 carriers with AD showed altered concentrations of proteins involved in the complement pathway and glycolysis when cognition was normal and lower concentrations of proteins involved in synapse structure and function when cognitive impairment was moderately severe. APOE ε4 non-carriers with AD showed lower expression of proteins involved in synapse structure and function when cognition was normal and lower concentrations of proteins that were associated with complement and other inflammatory processes when cognitive impairment was mild. Repeating analyses for 114 proteins that were available in an independent EMIF-AD MBD dataset (n = 275) showed that 80% of the proteins showed group differences in a similar direction, but overall, 28% effects reached statistical significance (ranging between 6 and 87% depending on the disease stage and genotype), suggesting variable reproducibility. Conclusions These results imply that AD pathophysiology depends on APOE genotype and that treatment for AD may need to be tailored according to APOE genotype and severity of the cognitive impairment.

Abstract Background Inflammatory processes have been shown to play a role in dementia. To understand this role, we selected two anti-inflammatory drugs (methotrexate and sulfasalazine) to study their association with dementia risk. Methods A retrospective matched case-control study of patients over 50 with rheumatoid arthritis (486 dementia cases and 641 controls) who were identified from electronic health records in the UK, Spain, Denmark and the Netherlands. Conditional logistic regression models were fitted to estimate the risk of dementia. Results Prior methotrexate use was associated with a lower risk of dementia (OR 0.71, 95% CI 0.52–0.98). Furthermore, methotrexate use with therapy longer than 4 years had the lowest risk of dementia (odds ratio 0.37, 95% CI 0.17–0.79). Sulfasalazine use was not associated with dementia (odds ratio 0.88, 95% CI 0.57–1.37). Conclusions Further studies are still required to clarify the relationship between prior methotrexate use and duration as well as biological treatments with dementia risk.

Alzheimer’s disease (AD) is the most frequent neurodegenerative disease with an increasing prevalence in industrialized, aging populations. AD susceptibility has an established genetic basis which has been the focus of a large number of genome-wide association studies (GWAS) published over the last decade. Most of these GWAS used dichotomized clinical diagnostic status, i.e., case vs. control classification, as outcome phenotypes, without the use of biomarkers. An alternative and potentially more powerful study design is afforded by using quantitative AD-related phenotypes as GWAS outcome traits, an analysis paradigm that we followed in this work. Specifically, we utilized genotype and phenotype data from n = 931 individuals collected under the auspices of the European Medical Information Framework for Alzheimer’s Disease Multimodal Biomarker Discovery (EMIF-AD MBD) study to perform a total of 19 separate GWAS analyses. As outcomes we used five magnetic resonance imaging (MRI) traits and seven cognitive performance traits. For the latter, longitudinal data from at least two timepoints were available in addition to cross-sectional assessments at baseline. Our GWAS analyses revealed several genome-wide significant associations for the neuropsychological performance measures, in particular those assayed longitudinally. Among the most noteworthy signals were associations in or near EHBP1 (EH domain binding protein 1; on chromosome 2p15) and CEP112 (centrosomal protein 112; 17q24.1) with delayed recall as well as SMOC2 (SPARC related modular calcium binding 2; 6p27) with immediate recall in a memory performance test. On the X chromosome, which is often excluded in other GWAS, we identified a genome-wide significant signal near IL1RAPL1 (interleukin 1 receptor accessory protein like 1; Xp21.3). While polygenic score (PGS) analyses showed the expected strong associations with SNPs highlighted in relevant previous GWAS on hippocampal volume and cognitive function, they did not show noteworthy associations with recent AD risk GWAS findings. In summary, our study highlights the power of using quantitative endophenotypes as outcome traits in AD-related GWAS analyses and nominates several new loci not previously implicated in cognitive decline.

Introduction Breast cancer is the most common cancer and the leading cause of cancer-related death in women worldwide. Risk prediction models may be useful to guide risk-reducing interventions (such as pharmacological agents) in women at increased risk or inform screening strategies for early detection methods such as screening.Methods and analysis The study will use data for women aged 20–90 years between 2000 and 2020 from QResearch linked at the individual level to hospital episodes, cancer registry and death registry data. It will evaluate a set of modelling approaches to predict the risk of developing breast cancer within the next 10 years, the ‘combined’ risk of developing a breast cancer and then dying from it within 10 years, and the risk of breast cancer mortality within 10 years of diagnosis. Cox proportional hazards, competing risks, random survival forest, deep learning and XGBoost models will be explored. Models will be developed on the entire dataset, with ‘apparent’ performance reported, and internal-external cross-validation used to assess performance and geographical and temporal transportability (two 10-year time periods). Random effects meta-analysis will pool discrimination and calibration metric estimates from individual geographical units obtained from internal-external cross-validation. We will then externally validate the models in an independent dataset. Evaluation of performance heterogeneity will be conducted throughout, such as exploring performance across ethnic groups.Ethics and dissemination Ethics approval was granted by the QResearch scientific committee (reference number REC 18/EM/0400: OX129). The results will be written up for submission to peer-reviewed journals.

The 4.9 Mw earthquake of 11 November 2019 at Le Teil (France) occurred at a very shallow depth (about 1 km), inducing the surface rupture of La Rouvière fault. The question was raised shortly after about the potential impact of a nearby surface quarry. Thanks to satellite differential interferometry, here, we revealed the existence of a secondary surface rupture of the quasi-parallel Bayne Rocherenard fault. A newly processed seismic cross-section allowed us to shape the three-dimensional geometry of the local three-fault system. Assuming that the earthquake was triggered by the impact of meteoric water recharge, our numerical simulations show that the hydraulic pressure gradient at depth was at a maximum during the period of 2010–2019, just before the seismic event. The estimated overpressure at the intersection of the two faults, which is the most probable place of the hypocenter, was close to 1 MPa. This hydraulic effect is about two and a half times larger than the cumulative effect of mechanical stress release due to the mass removal from the surface quarry over the two past centuries. This work suggests a rapid hydraulic triggering mechanism on a network of faults at a shallow depth after a heavy rainfall episode.

Sulfoximines, the next generation systemic insecticides developed to replace neonicotinoids, have been shown to negatively impact pollinator development and reproduction. However, field-realistic studies on sulfoximines are few and consequences on pollination services unexplored. Moreover, the impacts of other agrochemicals such as fungicides, and their combined effects with insecticides remain poorly investigated. Here, we show in a full factorial semi-field experiment that spray applications of both the product Closer containing the insecticide sulfoxaflor and the product Amistar containing the fungicide azoxystrobin, negatively affected the individual foraging performance of bumblebees (Bombus terrestris). Insecticide exposure further reduced colony growth and size whereas fungicide exposure decreased pollen deposition. We found indications for resource limitation that might have exacerbated pesticide effects on bumblebee colonies. Our work demonstrates that field-realistic exposure to sulfoxaflor can adversely impact bumblebees and that applications before bloom may be insufficient as a mitigation measure to prevent its negative impacts on pollinators. Moreover, fungicide use during bloom could reduce bumblebee foraging performance and pollination services.

Eccentric and concentric actions produce distinct mechanical stimuli and result in different adaptations in skeletal muscle architecture. Cycling predominantly involves concentric activity of the gastrocnemius muscles, while playing basketball requires both concentric and eccentric actions to support running, jumping, and landing. The aim of this study was to examine differences in the architecture of gastrocnemius medialis (GM) and gastrocnemius lateralis (GL) between elite basketballers and cyclists. A trained sonographer obtained three B-mode ultrasound images from GM and GL muscles in 44 athletes (25 basketballers and 19 cyclists; 24 ± 5 years of age). The images were digitized and average fascicle length (FL), pennation angle (θ), and muscle thickness were calculated from three images per muscle. The ratio of FL to tibial length (FL/TL) and muscle thickness to tibial length (MT/TL) was also calculated to account for the potential scaling effect of stature. In males, no significant differences were identified between the athletic groups in all parameters in the GM, but a significant difference existed in muscle thickness in the GL. In basketballers, GL was 2.5 mm thicker (95% CI: 0.7–4.3 mm, p = 0.011) on the left side and 2.6 mm thicker (95% CI: 0.6–5.7 mm, p = 0.012) on the right side; however, these differences were not significant when stature was accounted for (MT/TL). In females, significant differences existed in the GM for all parameters including FL/TL and MT/TL. Female cyclists had longer FL in both limbs (MD: 11.2 and 11.3 mm), narrower θ (MD: 2.1 and 1.8°), and thicker muscles (MD: 2.1 and 2.5 mm). For the GL, female cyclists had significantly longer FL (MD: 5.2 and 5.8 mm) and narrower θ (MD: 1.7 and 2.3°) in both limbs; no differences were observed in absolute muscle thickness or MT/TL ratio. Differences in gastrocnemius muscle architecture were observed between female cyclists and basketballers, but not between males. These findings suggest that participation in sport-specific training might influence gastrocnemius muscle architecture in elite female athletes; however, it remains unclear as to whether gastrocnemius architecture is systematically influenced by the different modes of muscle activation between these respective sports.

Subterranean openings, including mines, present a unique and challenging environment for robots and autonomous exploration systems. Autonomous robots that are created today will be deployed in harsh and unexplored landscapes that humanity is increasingly encountering in its scientific and technological endeavors. Terrestrial and extraterrestrial environments pose significant challenges for both humans and robots: they are inhospitable and inaccessible to humans due to a lack of space or oxygen, poor or no illumination, unpredictable terrain, a GPS-denied environment, and a lack of satellite imagery or mapping information of any type. Underground mines provide a good physical simulation for these types of environments, and thus, can be useful for testing and developing highly sought-after autonomous navigation frameworks for autonomous agents. This review presents a collective study of robotic systems, both of individual and hybrid types, intended for deployment in such environments. The prevalent configurations, practices for their construction and the hardware equipment of existing multi-agent hybrid robotic systems will be discussed. It aims to provide a supplementary tool for defining the state of the art of coupled Unmanned Ground Vehicle (UGV)–Unmanned Aerial Vehicle (UAV) systems implemented for underground exploration and navigation purposes, as well as to provide some suggestions for multi-agent robotic system solutions, and ultimately, to support the development of a semi-autonomous hybrid UGV–UAV system to assist with mine emergency responses.

The analysis of rockfall distribution and magnitude is a useful tool to study the past and current endogenic and exogenic activity of Mars. At the same time, tracks left by rockfalls provide insights into the mechanical properties of the Martian surface. While a wealth of high-resolution spaceborne image data are available, manual mapping of displaced boulders with tracks is inefficient and slow, resulting in: 1) a small total number of mapped features; 2) inadequate statistics; and 3) a suboptimal utilization of the available big data. This study implements a deep learning-driven approach to automatically detect and map Martian boulders with tracks in high resolution imaging science experiment (HiRISE) imagery. Six off-the-shelf neural networks have been trained either on Martian or lunar rockfall data, or a combination of both, and are able to achieve a maximum overall recall of up to 0.78 and a maximum overall precision of up to 1.0, with a mean average precision of 0.71. The fusion of training data from different planets and sensors results in an increased detection precision, highlighting the value of domain generalization and multidomain learning. Average processing time per HiRISE image is ~45 s using an NVIDIA Titan Xp, which is more than one order of magnitude faster than a human operator. The developed deep learning-driven infrastructure can be deployed to map Martian rockfalls on a global scale and within a realistic timeframe.

Abstract Background Down syndrome (DS), caused by chromosome 21 trisomy, is associated with an ultra-high risk of dementia due to Alzheimer’s disease (AD), driven by amyloid precursor protein (APP) gene triplication. Understanding relevant molecular differences between those with DS, those with sporadic AD (sAD) without DS, and controls will aid in understanding AD development in DS. We explored group differences in plasma concentrations of amyloid-β peptides and tau (as their accumulation is a characteristic feature of AD) and cytokines (as the inflammatory response has been implicated in AD development, and immune dysfunction is common in DS). Methods We used ultrasensitive assays to compare plasma concentrations of the amyloid-β peptides Aβ40 and Aβ42, total tau (t-tau), and the cytokines IL1β, IL10, IL6, and TNFα between adults with DS (n = 31), adults with sAD (n = 27), and controls age-matched to the group with DS (n = 27), and explored relationships between molecular concentrations and with age within each group. In the group with DS, we also explored relationships with neurofilament light (NfL) concentration, due to its potential use as a biomarker for AD in DS. Results Aβ40, Aβ42, and IL1β concentrations were higher in DS, with a higher Aβ42/Aβ40 ratio in controls. The group with DS showed moderate positive associations between concentrations of t-tau and both Aβ42 and IL1β. Only NfL concentration in the group with DS showed a significant positive association with age. Conclusions Concentrations of Aβ40 and Aβ42 were much higher in adults with DS than in other groups, reflecting APP gene triplication, while no difference in the Aβ42/Aβ40 ratio between those with DS and sAD may indicate similar processing and deposition of Aβ40 and Aβ42 in these groups. Higher concentrations of IL1β in DS may reflect an increased vulnerability to infections and/or an increased prevalence of autoimmune disorders, while the positive association between IL1β and t-tau in DS may indicate IL1β is associated with neurodegeneration. Finally, NfL concentration may be the most suitable biomarker for dementia progression in DS. The identification of such a biomarker is important to improve the detection of dementia and monitor its progression, and for designing clinical intervention studies.

This work presents an aerodynamic and structural optimization for a Droop Nose Leading Edge Morphing airfoil as a high lift device for the UAS-S45. The results were obtained using three optimization algorithms: coupled Particle Swarm Optimization-Pattern Search, Genetic Algorithm, and Black Widow Optimization algorithm. The lift-to-drag ratio was used as the fitness function, and the impact of the choice of optimization algorithm selection on the fitness function was evaluated. The optimization was carried out at various Mach numbers of 0.08, 0.1, and 0.15, respectively, and at the cruise and take-off flight conditions. All these optimization algorithms obtained effectively comparable lift-to-drag ratio results with differences of less than 0.03% and similar airfoil geometries and pressure distributions. In addition, an unsteady analysis of a Variable Morphing Leading Edge airfoil with a dynamic meshing scheme was carried out to study its flow behaviour at different angles of attack and the feasibility of leading-edge downward deflection as a stall control mechanism. The numerical results showed that the variable morphing leading edge reduces the flow separation areas over an airfoil and increases the stall angle of attack. Furthermore, a preliminary investigation was conducted into the design and sensitivity analysis of a morphing leading-edge structure of the UAS-S45 wing integrated with an internal actuation mechanism. The correlation and determination matrices were computed for the composite wing geometry for sensitivity analysis to obtain the parameters with the highest correlation coefficients. The parameters include the composite material qualities, thickness, ply angles, and the ply stacking sequence. These findings can be utilized to design the flexible skin optimization framework, obtain the target droop nose deflections for the morphing leading edge, and design an improved model.

Background and Objective: Plasma biomarkers for the diagnosis and stratification of Alzheimer’s disease (AD) are intensively sought. However, no plasma markers are well established so far for AD diagnosis. Our group has identified and validated various blood-based proteomic biomarkers relating to AD pathology in multiple cohorts. The study aims to conduct a meta-analysis based on our own studies to systematically assess the diagnostic performance of our previously identified blood biomarkers.Methods: To do this, we included seven studies that our group has conducted during the last decade. These studies used either Luminex xMAP or ELISA to measure proteomic biomarkers. As proteins measured in these studies differed, we selected protein based on the criteria that it must be measured in at least four studies. We then examined biomarker performance using random-effect meta-analyses based on the mean difference between biomarker concentrations in AD and controls (CTL), AD and mild cognitive impairment (MCI), MCI, and CTL as well as MCI converted to dementia (MCIc) and non-converted (MCInc) individuals.Results: An overall of 2,879 subjects were retrieved for meta-analysis including 1,053 CTL, 895 MCI, 882 AD, and 49 frontotemporal dementia (FTD) patients. Six proteins were measured in at least four studies and were chosen for meta-analyses for AD diagnosis. Of them, three proteins had significant difference between AD and controls, among which alpha-2-macroglobulin (A2M) and ficolin-2 (FCN2) increased in AD while fibrinogen gamma chain (FGG) decreased in AD compared to CTL. Furthermore, FGG significantly increased in FTD compared to AD. None of the proteins passed the significance between AD and MCI, or MCI and CTL, or MCIc and MCInc, although complement component 4 (CC4) tended to increase in MCIc individuals compared to MCInc.Conclusions: The results suggest that A2M, FCN2, and FGG are promising biomarkers to discriminate AD patients from controls, which are worthy of further validation.

The innate immune and host-protective responses to viruses, such as the airway pathogen human metapneumovirus (HMPV), depend on interferons (IFNs) that is induced through TANK-binding kinase 1 (TBK1) and IFN regulatory factors (IRFs). The transcription factor IRF1 is important for host resistance against several viruses and has a key role in induction of IFN-λ at mucosal surfaces. In most cell types IRF1 is expressed at very low levels, but its mRNA is rapidly induced when the demand for IRF1 activity arises. Despite general recognition of the importance of IRF1 to antiviral responses, the molecular mechanisms by which IRF1 is regulated during viral infections are not well understood. Here we identify the serine/threonine kinase TBK1 and IFN-β as critical regulators of IRF1 mRNA and protein levels in human monocyte-derived macrophages. We find that inhibition of TBK1 activity either by the semi-selective TBK1/IKKε inhibitor BX795 or by siRNA-mediated knockdown abrogates HMPV-induced expression of IRF1. Moreover, we show that canonical NF-κB signaling is involved in IRF1 induction and that the TBK1/IKKε inhibitor BX795, but not siTBK1 treatment, impairs HMPV-induced phosphorylation of the NF-κB subunit p65. At later time-points of the infection, IRF1 expression depended heavily on IFN-β-mediated signaling via the IFNAR-STAT1 pathway. Hence, our results suggest that TBK1 activation and TBK1/IKKε-mediated phosphorylation of the NF-κB subunit p65 control transcription of IRF1. Our study identifies a novel mechanism for IRF1 induction in response to viral infection of human macrophages that could be relevant not only to defense against HMPV, but also to other viral, bacterial and fungal pathogens.

Chimeric antigen receptor T cells (CAR-T) targeting CD19 have achieved significant success in patients with B cell malignancies. To date, implementation of CAR-T in other indications remains challenging due to the lack of truly tumor-specific antigens as well as control of CAR-T activity in patients. CD123 is highly expressed in acute myeloid leukemia (AML) blasts including leukemia-initiating cells making it an attractive immunotherapeutic target. However, CD123 expression in normal hematopoietic progenitor cells and endothelia bears the risk of severe toxicities and may limit CAR-T applications lacking fine-tuned control mechanisms. Therefore, we recently developed a rapidly switchable universal CAR-T platform (UniCAR), in which CAR-T activity depends on the presence of a soluble adapter called targeting module (TM), and confirmed clinical proof-of-concept for targeting CD123 in AML with improved safety. As costimulation via 4–1BB ligand (4–1BBL) can enhance CAR-T expansion, persistence, and effector functions, a novel CD123-specific TM variant (TM123-4-1BBL) comprising trimeric single-chain 4–1BBL was developed for transient costimulation of UniCAR-T cells (UniCAR-T) at the leukemic site in trans. TM123-4-1BBL-directed UniCAR-T efficiently eradicated CD123-positive AML cells in vitro and in a CDX in vivo model. Moreover, additional costimulation via TM123-4-1BBL enabled enhanced expansion and persistence with a modulated UniCAR-T phenotype. In addition, the increased hydrodynamic volume of TM123-4-1BBL prolonged terminal plasma half-life and ensured a high total drug exposure in vivo. In conclusion, expanding the soluble adapter optionality for CD123-directed UniCAR-T maintains the platforms high anti-leukemic efficacy and immediate control mechanism for a flexible, safe, and individualized CAR-T therapy of AML patients.

Ultrasonography is widely used to measure gastrocnemius muscle architecture; however, it is unclear if values obtained from digitised images are sensitive enough to track architectural responses to clinical interventions. The purpose of this study was to explore the reliability and determine the minimal detectable change (MDC) of gastrocnemius medialis (GM) and gastrocnemius lateralis (GL) muscle architecture using ultrasound in a clinical setting. A trained sonographer obtained three B-mode images from each of the GM and GL muscles in 87 volunteers (44 males, 43 females; 22±9 years of age) on two separate occasions. Three independent investigators received training, then digitised the images to determine intra-rater, inter-rater, and test-retest reliability for fascicle length (FL), pennation angle (θ) and muscle thickness. Median FL, θ, and muscle thickness for GM and GL were 53.6-55.7 mm and 65.8-69.3 mm, 18.7-19.5° and 11.9-12.5°, and 12.8-13.2 mm and 15.9-16.9 mm, respectively. Intra- and inter-rater reliability of manual digitisation was excellent for all parameters. Test-retest reliability was moderate to excellent with intraclass correlation coefficient (ICC) values ≥0.80 for FL, ≥0.61 for θ, and ≥0.81 for muscle thickness, in both GM and GL. The respective MDC for GM and GL FL, θ, and muscle thickness was ≤12.1 mm and ≤18.00 mm, ≤6.4° and ≤4.2°, and ≤3.2 mm and ≤3.1 mm. Although reliable, the relatively large MDC suggest that clinically derived ultrasound measurements of muscle architecture in GM and GL are more likely to be useful to detect differences between populations than to detect changes in muscle architecture following interventions.