Vaughan Gilmore (portocelot14)

In the field of transmission electron microscopy, data interpretation often lags behind acquisition methods, as image processing methods often have to be manually tailored to individual datasets. Machine learning offers a promising approach for fast, accurate analysis of electron microscopy data. Here, we demonstrate a flexible two-step pipeline for the analysis of high-resolution transmission electron microscopy data, which uses a U-Net for segmentation followed by a random forest for the detection of stacking faults. Our trained U-Net is able to segment nanoparticle regions from the amorphous background with a Dice coefficient of 0.8 and significantly outperforms traditional image segmentation methods. Using these segmented regions, we are then able to classify whether nanoparticles contain a visible stacking fault with 86% accuracy. We provide this adaptable pipeline as an open-source tool for the community. The combined output of the segmentation network and classifier offer a way to determine statistical distributions of features of interest, such as size, shape, and defect presence, enabling the detection of correlations between these features. Despite the vast majority of evidence indicating the efficacy of traditional and recent cognitive behaviour therapy (CBT) therapies in treating social anxiety disorder (SAD), some individuals with SAD do not improve by these interventions, particularly when co-morbidity is present. It is not clear how emotion regulation therapy (ERT) can improve SAD co-morbid with symptoms of generalized anxiety disorder (GAD) and depression. This study investigated this gap. Treatment efficacy was assessed using a single case series methodology. Four clients with SAD co-occurring with GAD and depression symptoms received a 16-session version of ERT in weekly individual sessions. During the treatment, self-report measures and clinician ratings were used to assess the symptom intensity, model-related variables, and quality of life, work and social adjustment of participants every other week throughout the treatment. Follow-up was also conducted at 1, 2 and 3 months after treatment. Data were analysed using visual analysis, effect size (Cohen's d) and percentage of improvement. SAD clients with depression and GAD symptoms demonstrated statistically and clinically significant improvements in symptom severity, quality of life, work, social adjustment and model-related measures (i.e. negative emotionality/safety motivation, emotion regulation strategies). The improvements were largely maintained during the follow-up period and increased for some variables. These findings showed preliminary evidence for the role of emotion dysregulation and motivational factors in the aetiology and maintenance of SAD and the efficacy of ERT in the treatment of co-morbid SAD. These findings showed preliminary evidence for the role of emotion dysregulation and motivational factors in the aetiology and maintenance of SAD and the efficacy of ERT in the treatment of co-morbid SAD. Memory symptoms and objective impairment are common in HIV disease and are associated with disability. A paradoxical issue is that objective episodic memory failures can interfere with accurate recall of memory symptoms. The present study assessed whether responses on a self-report scale of memory symptoms demonstrate measurement invariance in persons with and without objective HIV-associated memory impairment. In total, 505 persons with HIV completed the Prospective and Retrospective Memory Questionnaire (PRMQ). Objective memory impairment (n = 141) was determined using a 1-SD cutoff on clinical tests of episodic memory. PRMQ measurement invariance was assessed by confirmatory factor analyses examining a one-factor model with increasing cross-group equality constraints imposed on factor loadings and item thresholds (i