Fretting wear behaviour of MoS2 dry film lubricant

Dry film lubricants (DFL) are used as palliative coatings to prevent fretting wear. In this work fretting tests are carried out on coated Ti6Al4V cylinders on coated flat samples under dry sliding conditions, using an amplitude of 300 µm, 2.5 Hz frequency and 575 N normal loads. During the tests the coefficient of friction (CoF) was monitored, with tests being terminated when the coefficient of friction reached 0.7. Wear scars were analysed by profilometry and SEM to elucidate wear mechanisms. Results show that CoF initially increases rapidly to 0.4, this is then followed by a plateau region that finishes in a sudden step decrease in CoF following which CoF rises steadily. This behaviour is shown to be characteristic and interrupted tests are presented to allow elucidation of the wear scar at different stages in the lifetime and thus aid an understanding of the mechanisms of degradation which control the tribological behaviour

Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation

Training neural networks using limited annotations is an important problem in the medical domain. Deep Neural Networks (DNNs) typically require large, annotated datasets to achieve acceptable performance which, in the medical domain, are especially difficult to obtain as they require significant time from expert radiologists. Semi-supervised learning aims to overcome this problem by learning segmentations with very little annotated data, whilst exploiting large amounts of unlabelled data. However, the best-known technique, which utilises inferred pseudo-labels, is vulnerable to inaccurate pseudo-labels degrading the performance. We propose a framework based on superpixels - meaningful clusters of adjacent pixels - to improve the accuracy of the pseudo labels and address this issue. Our framework combines superpixels with semi-supervised learning, refining the pseudo-labels during training using the features and edges of the superpixel maps. This method is evaluated on a multimodal magnetic resonance imaging (MRI) dataset for the task of brain tumour region segmentation. Our method demonstrates improved performance over the standard semi-supervised pseudo-labelling baseline when there is a reduced annotator burden and only 5 annotated patients are available. We report DSC=0.824 and DSC=0.707 for the test set whole tumour and tumour core regions respectively

Pseudo-label refinement using superpixels for semi-supervised brain tumour segmentation

Training neural networks using limited annotations is an important problem in the medical domain. Deep Neural Networks (DNNs) typically require large, annotated datasets to achieve acceptable performance which, in the medical domain, are especially difficult to obtain as they require significant time from expert radiologists. Semi-supervised learning aims to overcome this problem by learning segmentations with very little annotated data, whilst exploiting large amounts of unlabelled data. However, the best-known technique, which utilises inferred pseudo-labels, is vulnerable to inaccurate pseudo-labels degrading the performance. We propose a framework based on superpixels - meaningful clusters of adjacent pixels - to improve the accuracy of the pseudo labels and address this issue. Our framework combines superpixels with semi-supervised learning, refining the pseudo-labels during training using the features and edges of the superpixel maps. This method is evaluated on a multimodal magnetic resonance imaging (MRI) dataset for the task of brain tumour region segmentation. Our method demonstrates improved performance over the standard semi-supervised pseudo-labelling baseline when there is a reduced annotator burden and only 5 annotated patients are available. We report DSC=0.824 and DSC=0.707 for the test set whole tumour and tumour core regions respectively

DRD2/ANKK1 Taq1A (rs 1800497 C>T) genotypes are associated with susceptibility to second generation antipsychotic-induced akathisia

Although the advent of atypical, second-generation antipsychotics (SGAs) has resulted in reduced likelihood of akathisia, this adverse effect remains a problem. It is known that extrapyramidal adverse effects are associated with increased drug occupancy of the dopamine 2 receptors (DRD2). The A1 allele of the DRD2/ANKK1, rs1800497, is associated with decreased striatal DRD2 density. The aim of this study was to identify whether the A1(T) allele of DRD2/ANKK1 was associated with akathisia (as measured by Barnes Akathisia Rating Scale) in a clinical sample of 234 patients who were treated with antipsychotic drugs. Definite akathisia (a score ≥ 2 in the global clinical assessment of akathisia) was significantly less common in subjects who were prescribed SGAs (16.8%) than those prescribed FGAs (47.6%), p < 0.0001. Overall, 24.1% of A1+ patients (A1A2/A1A1) who were treated with SGAs had akathisia, compared to 10.8% of A1- (thus, A2A2) patients. A1+ patients who were administered SGAs also had higher global clinical assessment of akathisia scores than the A1- subjects (p = 0.01). SGAs maintained their advantage over FGAs regarding akathisia, even in A1+ patients who were treated with SGAs. These results strongly suggested that A1+ variants of the DRD2/ANKK1 Taq1A allele do confer an associated risk for akathisia in patients who were treated with SGAs, and these variants may explain inconsistencies found across prior studies, when comparing FGAs and SGAs

The role of noise in denoising models for anomaly detection in medical images

Pathological brain lesions exhibit diverse appearance in brain images, in terms of intensity, texture, shape, size, and location. Comprehensive sets of data and annotations are difficult to acquire. Therefore, unsupervised anomaly detection approaches have been proposed using only normal data for training, with the aim of detecting outlier anomalous voxels at test time. Denoising methods, for instance classical denoising autoencoders (DAEs) and more recently emerging diffusion models, are a promising approach, however naive application of pixelwise noise leads to poor anomaly detection performance. We show that optimization of the spatial resolution and magnitude of the noise improves the performance of different model training regimes, with similar noise parameter adjustments giving good performance for both DAEs and diffusion models. Visual inspection of the reconstructions suggests that the training noise influences the trade-off between the extent of the detail that is reconstructed and the extent of erasure of anomalies, both of which contribute to better anomaly detection performance. We validate our findings on two real-world datasets (tumor detection in brain MRI and hemorrhage/ischemia/tumor detection in brain CT), showing good detection on diverse anomaly appearances. Overall, we find that a DAE trained with coarse noise is a fast and simple method that gives state-of-the-art accuracy. Diffusion models applied to anomaly detection are as yet in their infancy and provide a promising avenue for further research.Comment: Submitted to Medical Image Analysis special issue for MIDL 202

Proinflammatory genotype is associated with the frailty phenotype in the English Longitudinal Study of Ageing

Background: Frailty is a state of increased vulnerability to poor resolution of homeostasis after a stressor event, which increases the risk of adverse outcomes including falls, disability and death. The underlying pathophysiological pathways of frailty are not known but the hypothalamic–pituitary–adrenal axis and heightened chronic systemic inflammation appear to be major contributors. Methods: We used the English Longitudinal Study of Ageing dataset of 3160 individuals over the age of 50 and assessed their frailty status according to the Fried-criteria. We selected single nucleotide polymorphisms in genes involved in the steroid hormone or inflammatory pathways and performed linear association analysis using age and sex as covariates. To support the biological plausibility of any genetic associations, we selected biomarker levels for further analyses to act as potential endophenotypes of our chosen genetic loci. Results: The strongest association with frailty was observed in the Tumor Necrosis Factor (TNF) (rs1800629, P = 0.001198, β = 0.0894) and the Protein Tyrosine Phosphatase, Receptor type, J (PTPRJ) (rs1566729, P = 0.001372, β = 0.09397) genes. Rs1800629 was significantly associated with decreased levels of high-density lipoprotein (HDL) (P = 0.00949) and cholesterol levels (P = 0.00315), whereas rs1566729 was associated with increased levels of HDL (P = 0.01943). After correcting for multiple testing none of the associations remained significant. Conclusions: We provide potential evidence for the involvement of a multifunctional proinflammatory cytokine gene (TNF) in the frailty phenotype. The implication of this gene is further supported by association with the endophenotype biomarker results

Seed Transmission of Epichloë Endophytes in Lolium perenne Is Heavily Influenced by Host Genetics

Vertical transmission of symbiotic Epichloë endophytes from host grasses into progeny seed is the primary mechanism by which the next generation of plants is colonized. This process is often imperfect, resulting in endophyte-free seedlings which may have poor ecological fitness if the endophyte confers protective benefits to its host. In this study, we investigated the influence of host genetics and environment on the vertical transmission of Epichloë festucae var. lolii strain AR37 in the temperate forage grass Lolium perenne. The efficiency of AR37 transmission into the seed of over 500 plant genotypes from five genetically diverse breeding populations was determined. In Populations I–III, which had undergone previous selection for high seed infection by AR37, mean transmission was 88, 93, and 92%, respectively. However, in Populations IV and V, which had not undergone previous selection, mean transmission was 69 and 70%, respectively. The transmission values, together with single-nucleotide polymorphism data obtained using genotyping-by-sequencing for each host, was used to develop a genomic prediction model for AR37 seed transmission. The predictive ability of the model was estimated at r = 0.54. While host genotype contributed greatly to differences in AR37 seed transmission, undefined environmental variables also contributed significantly to seed transmission across different years and geographic locations. There was evidence for a small host genotype-by-environment effect; however this was less pronounced than genotype or environment alone. Analysis of endophyte infection levels in parent plants within Populations I and IV revealed a loss of endophyte infection over time in Population IV only. This population also had lower average tiller infection frequencies than Population I, suggesting that AR37 failed to colonize all the daughter tillers and therefore seeds. However, we also observed that infection of seed by AR37 may fail during or after initiation of floral development from plants where all tillers remained endophyte-infected over time. While the effects of environment and host genotype on fungal endophyte transmission have been evaluated previously, this is the first study that quantifies the relative impacts of host genetics and environment on endophyte vertical transmission

The Impact of Entrepreneurship Education in Higher Education: A Systematic Review and Research Agenda

Using a teaching model framework, we systematically review empirical evidence on the impact of entrepreneurship education (EE) in higher education on a range of entrepreneurial outcomes, analyzing 159 published articles from 2004 to 2016. The teaching model framework allows us for the first time to start rigorously examining relationships between pedagogical methods and specific outcomes. Reconfirming past reviews and meta-analyses, we find that EE impact research still predominantly focuses on short-term and subjective outcome measures and tends to severely underdescribe the actual pedagogies being tested. Moreover, we use our review to provide an up-to-date and empirically rooted call for less obvious, yet greatly promising, new or underemphasized directions for future research on the impact of university-based entrepreneurship education. This includes, for example, the use of novel impact indicators related to emotion and mind-set, focus on the impact indicators related to the intention-to-behavior transition, and exploring the reasons for some contradictory findings in impact studies including person-, context-, and pedagogical model-specific moderator